Modern Difference-in-Differences: New Problems, New Solutions - Linear Regression and Panel Data in STATA
PREREQUISITES Working knowledge of linear regression and basic familiarity with panel data concepts (fixed effects, clustering) Prior experience with Stata is expected No prior exposure to the new DiD literature is assumed — the workshop builds from the ground up PROGRAM SESSION I: SIMPLE DID DESIGNS The 2×2 design 2×T event studies Parallel trends Interpreting ATT parameters as estimands Clustering and standard errors SESSION II: VIOLATIONS OF PARALLEL TRENDS Covariates Triple differences Compositional changes over time Addressing weak overlap SESSION III: DIFFERENTIAL TIMING Two-way fixed effects problems Goodman-Bacon decomposition, Callaway and Sant’Anna, Sun and Abraham, Borusyak et al., Wooldridge Imputation vs. weighting estimators SESSION IV: SENSITIVITY ANALYSIS Bounding parallel trends violations Sensitivity of results across packages and languages SESSION V: CONTINUOUS DID AND DOSAGE DESIGNS Extending difference-in-differences to continuous treatments and dosage frameworks SESSION VI: A MODEST SUGGESTION OF CHECKS Walk-throughs using several empirical examples, all in Stata Practical guidance on reporting and diagnostics DATE AND LOCATION This Workshop is being offered on the second day of the Italian Stata Users Conference that will take place in Milan, on the 17th-18th September 2026. COURSE LEADERS Scott Cunningham – Ben H. Williams Professor of Economics, Baylor University REGISTRATION FEE AND REGISTRATION FORM Conference Conference and Workshop Students and PhD Students* full-time € 60.00 € 195.00 Other € 110.00 € 420.00 All participation fees are subject to VAT (applied at the current Italian rate of 22%); *To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Individuals interested in participating should return their duly completed registration forms to the conference organizers by the 10.09.2026 at the very latest. To request further information or the registration form, please fill in the following form or send an email to training@tstat.eu
This Workshop is being offered on the second day of the Italian Stata Users Conference that will take place in Milan, on the 17th-18th September 2026.
Econometrics, Statistics and Quantitative Methods (JEL C)
Large Language Models, Graphs and Applications (LLGA) Master's Specialization
The LLGA Master’s specialization is a two-year, fully English-taught degree with a strong international outlook. Designed for ambitious students, the specialization equips graduates with both a solid theoretical foundation and hands-on industry expertise in Generative AI. With a special emphasis on Large Language Models (LLMs) and graph-structured data, students gain the skills to harness cutting-edge technologies and apply them across diverse fields—from healthcare and biology to finance, social networks, and beyond. In recent years, Generative AI has grown explosively, with Large Language Models (LLMs) transforming how society and the economy interact with the digital world. From translation and code generation to creative content and advanced reasoning, these technologies achieve tasks once unimaginable. The LLGA Master’s specialization prepares students to master this field, combining theory with real-world applications. Graph-based models, such as Graph Neural Networks (GNNs), add another layer by capturing complex structures in networks, molecules, or logistics. Together, LLMs and graph methods enable multimodal generative AI, opening opportunities in healthcare, finance, recommendation systems, and beyond. The LLGA specialization provides a strong foundation in machine and deep learning, plus hands-on experience with cutting-edge generative techniques. Through projects, case studies, and industry applications, graduates gain rare expertise, equipping them to lead the next generation of AI innovation. Program Details Master's Degree Industry-Oriented Duration: 2 Years Taught in: English Location: France Credits: 140 ECTS Cost: Up to €15,400 Per Year Academic Prerequisites Students are required to have a suitable background in mathematics and computer science. In practice, this means knowledge of linear algebra, statistics, as well as Python programming, algorithms and data structures. Some exposure to natural language processing and graph theory is desirable, but not required. Candidates with Bachelor degrees that are not in Mathematics or Computer Science are also admissible if the required knowledge domains are covered in their degree programs. Career Prospects Students will have highly suitable profiles for data science or AI roles in industry. While the LLM aspect of the specialization allows students to apply to companies in most digital companies, the graphs and applications aspect of the specialization prepares the students particularly well for roles in the pharma, logistics and communication industries. Graduates of the LLGA Master’s specialization are prepared for careers as AI/ML Engineers, Data Scientists, NLP Specialists, Graph AI Experts, and AI Research Scientists, as well as strategic roles such as AI Product Managers, Solution Architect. Opportunities span industries including tech, healthcare, finance, consulting, social media, e-commerce, and public institutions. Also - since the specialization teaches cutting-edge methodology in a relevant field of research, pursuing a PhD after this program is perfectly possible. Contact Us ECOLE POLYTECHNIQUE Route de Saclay 91128 PALAISEAU Cedex France
The LLGA Master’s specialization is a two-year, fully English-taught degree focusing on Generative AI and Large Language Models.
Time Series Modelling and Forecasting using Stata
COURSE DESCRIPTION Time Series data is today available for a wide range of several phenomena in Business, Finance, Economics, Public Health, the Political and Social Sciences. The aim of TStat Training’s Times Series Modelling and Forecasting Course is therefore, to provide researchers and professionals with the standard tool kit required for the analysis of time series data in Stata. As such the program has been developed to offer an overview of the most commonly used methods for analysing, modelling and forecasting the dynamic behaviour of time series data, offering practical examples of empirical modelling using real-world data. The course begins with an introduction to Stata’s basic time series commands, before moving onto the analysis of time series features and to univariate time series models. Sessions 3 and 4 instead focus on the estimation of both multivariate time series models with stationary and nonstationary data and univariate models of volatility. In common with TStat’s training philosophy, throughout the course theory and methods are illustrated in an intuitive way and are complemented by practical exercises undertaken in Stata, during which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. Particular attention is also given to both the interpretation and presentation of empirical results. In this manner, the course leader is able to bridge the “often difficult” gap between theory and practice of time series modelling and forecasting. Upon completion, it is expected that participants are able to autonomously implement the statistical methods discussed during the course to their own data, customizing when necessary, the Stata do-file routines specifically developed for the course. TARGET AUDIENCE Researchers and professionals working in financial institutions, policy institutions, research departments of utilities, governments, corporations, Ph.D and Master students in biostatistics, economics, finance, engineering, psychology, social and political sciences needing to implement time series data analysis methods. PREREQUISITES Participants are required to have a good working knowledge of: • Linear regression model definition and assumptions • Ordinary Least Squares (OLS) estimation. Properties ofOLS • Inference in the linear regression model: confidenceintervals, t-test, F-test • Violation of the linear regression model assumptions:heteroscedasticity, serial correlation, functional formmisspecification, non-Normality. Consequences ofviolations and remedies • Diagnostic analysis of regression: tests forheteroscedasticity, test for serial correlation, Normalitytest, Ramsey’s RESET • Regression with time series data. Concepts of laggedvariable and differenced variable • Dynamic models Those needing to refresh these concepts are referred to: • Hill, R.C., Griffiths, W.E., and G.C. Lim (2018). Principlesof Econometrics, 5th Edition. Wiley • Stock, J.H., and Watson, M.W. (2019). Introduction toEconometrics, 4th edition, Pearson • Wooldridge, J.M. (2020). Introductory Econometrics: AModern Approach, 7th Edition, Cengage Learning PROGRAM SESSION I: WORKING WITH TIME SERIES IN STATA 1. A quick introduction to Stata for time series data: • Creating and formatting date variables using date and time functions and time-series declaration • Using Stata time-series operators to create lags, leads, differences, seasonal differences, percentage changes 2. Graphical analysis of time series: • Creating and examining line plots to understand the behaviour of series over time • Creating and examining correlograms to assess dependence in time series • Creating and examining histograms to evaluate Normality 3. Descriptive statistics analysis of time series: • Unit root test: Augmented Dickey Fuller Test and DFGLS test • White Noise test • Normality test SESSION II: UNIVARIATE TIME SERIES MODELS 1. Univariate Time Series Models: theoretical elements • Autoregressive model (AR) • Moving Average model (MA) • Autoregressive Moving Average Model (ARMA) • Autoregressive Integrated Moving Average Model (ARIMA) • Seasonal ARIMA models (SARIMA) 2. ARIMA modelling in practice: using the Box-Jenkins approach to model real-world economic time series • Examining the sample autocorrelation function and the sample partial autocorrelation function • Estimation of ARIMA models • Model diagnostic analysis and model selection using information criteria 3. Forecasting with ARIMA models 4. An introduction to Markov-Switching Models • Markov-switching dynamic regression • Markov-switching autoregression SESSION III: MULTIVARIATE TIME SERIES MODELS 1. Multivariate Time Series Models – Vector Autoregression (VAR): theoretical elements 2. VAR modelling in practice: • Lag order selection • Estimation • Diagnostic checking: autocorrelation, stationarity and normality 3. Granger causality and impulse response function analysis 4. Forecasting with VAR models 5. Nonstationary time series: spurious regressions versus cointegration • Concepts of long-run equilibrium relationships and short-run relationships • Vector Error Correction Model (VECM) • Testing for cointegration in regressions: a residuals-based test 6. Engle and Granger two-step method 7. Testing for cointegration and estimating cointegrating relationships with the Johansen’s approach SESSION IV: MODELLING AND FORECASTING VOLATILITY IN TIME SERIES MODELS 1. Time-varying volatility: • Visual inspection of time-varying volatility (i.e. volatility clustering) and leptokurtosis with line plots and histograms • ARCH test 2. Modelling volatility of financial time series: • ARCH model: estimation and diagnostic checking • Extension to the ARCH model: GARCH model, estimation and diagnostic checking • ARCH-in-Mean model • Modelling the asymmetric impact of news on volatility: Asymmetric GARCH models (EGARCH, GJR-GARCH, TGARCH, APARCH) • Model selection 3. Forecasting volatility with GARCH models SUGGESTED READING (PRE – AND POST-COURSE) Becketti, S. (2020). Introduction to Time Series Using Stata. Stata Press Publication. Boffelli, S. and Urga G. (2016). Financial Econometrics Using Stata. Stata Press Publication. DATE AND LOCATION The 2026 edition of this training course will be offered ONLINE on a part-time basis on the 1st-2nd, 8th-9th of October. To this end, programme includes a series of sessions based on 4 modules from 10:00 am to 1:30 pm Central European Summer Time (CEST). COURSE LEADERS Dr Elisabetta PELLINI, Centre for Econometric Analysis, Bayes Business School (formerly Cass), London (UK). REGISTRATION FEE AND REGISTRATION FORM Students*: € 470.00 Ph.D Students: € 610.00 University: € 1110.00 Commercial: € 1480.00 *To be eligible for full-time student prices, participants must provide proof of their full-time student status for the current academic year. Our standard policy is to provide all full-time students, be they Undergraduates or Masters, access to our student registration rates. Part-time master and doctoral students on the other hand, who are also currently employed will however, be assigned the standard academic registration fee. Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number. The number of participants is limited to 8. Places will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 5 individuals are enrolled. Course fees cover: teaching materials (handouts, Stata do-files, program templates and datasets to use during the course), a temporary course licence of StataNow™ valid for 30 days from the beginning of the course. Individuals interested in attending the training course, must return their completed registration forms to TStat by the 20th of September 2026. To request further information or the registration form, please fill in the following form or send an email to training@tstat.eu
The course has been developed to offer an overview of the most commonly used methods for analysing, modelling and forecasting the dynamic behaviour of time series data, offering practical examples of empirical modelling using real-world data. TStat Training’s live online training courses are offered interactively via Zoom with a qualified trainer in real-time. All materials (slides, datasets and Stata routines specifically developed for the course) are made available for download before the start of the course. The 2026 edition of this training course will be offered ONLINE on a part-time basis on the 1st-2nd of October and 8th-9th of October.
CIMS Online Summer Schools: Foundations and Advanced of DSGE Macro Modelling
This is a Virtual Summer school (online) this year. Registration is now open for the CIMS summer school 2026. Register Here This year's Summer school will feature: Foundations of DSGE Macro Modelling The five-day course in Foundations of DSGE Modelling will run from 07-11 September 2026. The five days will consist of foundations courses aimed at early researchers with some knowledge of Real Business Cycle (RBC) or Dynamic Stochastic General Equilibrium (DSGE) macroeconomic models, but little or no experience of Dynare. This course assumes a basic knowledge of Matlab and will begin with the basics of Dynare and proceed to the construction, in stages, of a closed economy New Keynesian (NK) DSGE model. It will then progress to the estimation by Bayesian methods, the study of optimal monetary policy and finish with an extension to the open economy NK DSGE model. The course is aimed at MSc, MRes and PhD students, early researchers, and research staff from central banks, ministries and the private sector, all with some prior experience of macroeconomic modelling and a basic knowledge of Matlab. No prior knowledge of Dynare is necessary. Please note that all participants need to have their own copy of Matlab (https://www.mathworks.com/products/matlab.html) for the course on Foundations of DSGE Macro Modelling. There is a short-term license available for students which is worth checking: https://www.mathworks.com/campaigns/products/trials.html Advanced Macro-Modelling with Applications to Numerical Methods and Heterogeneous Agents The five-day course in Advanced Modelling will run from 14-18 September 2026. The advanced course is aimed at people who are already fluent in Dynare and Matlab, but are finding that their ambition currently exceeds what they are able to do with these tools. We will cover advanced numerical and computational methods to enable people to tackle non-standard models. This course will be useful to anyone who is engaged in practical macroeconomic modelling work, especially if they are interested in working with models that are either computationally expensive to simulate, nonlinear, or infinite dimensional thanks to heterogeneous agents, monetary policy, or optimal taxation and inequality. The course is aimed at MSc, MRes and PhD students, early researchers, and research staff from central banks, ministries and the private sector, all with some prior experience of macroeconomic modelling and a basic knowledge of Matlab. Please note that all participants need to have their own copy of Matlab (https://www.mathworks.com/products/matlab.html) for the course on Advanced Macro Modelling. There is a short-term license available for students which is worth checking: https://www.mathworks.com/campaigns/products/trials.html Online delivery method: The course will be conducted using the Microsoft Teams platform. The university and the lecturers are used to do online teaching and have state-of-the-art systems to do so and are able to deliver highly interactive experiences. Though we will do our best to record all sessions, we are expecting participants to attend them live to enable interactions between participants and lectures. Any and all questions are welcome. There will be opportunities to discuss the projects and research ideas of the participants. This will occur both informally as well as formally during the lecture or break time. After the summer school, lecturers will be available to answer questions and discuss the projects of the participants. Participants will be sent computer codes, lecture notes, and slides ahead of time. Participants will also receive direct assistance before the summer school starts in order to set up all the systems ahead of the online events. The summer school covers the theoretical aspects of the techniques. In addition, the summer school is very hands-on and participants will have carefully implemented exercises where they need to modify parts of the codes. This will be done in the lectures, both in groups and with the help of the instructors. The participants will receive solutions to all exercises and will be provided with computer codes that may then use in their own research..
Following highly successful Summer Schools in previous years, the Centre for International Macroeconomic Studies (CIMS) in the School of Economics, University of Surrey will hold two online summer schools on the Macroeconomics/DSGE School (Foundation and Advanced), from September 7 to 18, 2026. Foundations of DSGE Macro Modelling will consist of a five-day course running from 7 September to 11 September 2026. Advanced Macro-Modelling with Topics including Numerical Methods and Heterogeneous Agents will consist of a five-day course running from 14 September to 18 September 2026.
Macroeconomics and Monetary Economics (JEL E)
Visualizing Geospatial Data In Stata: Spatial Maps In Stata - Live Online Course
The creation of spatial maps has become a valuable tool for researchers interested in both the spatial data visualization and the identification of geographical patterns in the data. This interactive course therefore, offers an introduction to the mapping techniques currently available in Stata for visualization of geospatial data. The workshop opens with an overview of the peculiar characteristics of geospatial data, the implications of such for the analysis of such data and an overview of the special terminology used in this field, before moving on to discuss the concept of spatial proximity and its relevance to the visualization of spatial data analysis. The remainder of the workshop is dedicated to the principle mapping techniques, using a combination of both official Stata commands and a series of user written commands, currently adopted to visualize geospatial data in Stata. Participants will learn how to import geospatial data shapefiles in Stata and merge them with their existing Stata databases in order to create, compare and customize different mapping techniques. At the end of the course, participants will be able to autonomously implement (with the help of the Stata routine templates specifically developed for the workshop) the appropriate mapping methods, given both the nature of their spatial data and the analysis in hand, within their own research. TARGET AUDIENCE This online course is of particular interest for criminologists, social psychologists, sociologists, economists, epidemiologists and political scientists seeking to acquire the requisite tools required for the exploration and visualisation of spatial data in Stata. PREREQUISITES A knowledge of basic statistics (distributions of variables, position indices, dispersion indices) and the statistical software Stata is recommended. PROGRAM SESSION I: SPATIAL DATA 1. General characteristics of spatial data 2. Types of spatial objects 3. Spatial coordinate systems 4. Maps and shapefiles 5. The transformation of spatial databases SESSION II: SPATIAL PROXIMITY 1. Spatial distance 2. Spatial proximity matrices 3. Spatial lags 4. Spatial autocorrelation SESSION III: VISUAL ANALYSIS OF SPATIAL DATA 1. Visual analytics and data science 2. Thematic maps 3. Dot maps 4. Graduated symbol maps 5. Diagram maps 6. Choropleth Maps 7. Isarithmic Maps 8. Multivariate Maps SUGGESTED READING Anthamatten, P. (2021). How to Make Maps: An Introduction to Theory and Practice of Cartography. Abingdon: Routledge. Lambert, N. & Zanin, C. (2020). Practical Handbook of Thematic Cartography: Principles, Methods, and Applications. Boca Raton, FL: CRC Press. DATE AND LOCATION The next edition of this training course will be offered online on a part-time basis on the 30th of September from 9:00 am to 1:30 pm CET. COURSE LEADERS Professor Maurizio Pisati, University of Milano-Bicocca, Department of Sociology and Social Research, Milan (IT). REGISTRATION FEE AND REGISTRATION FORM Full-time Students*: € 155 Ph.D. Students: € 205 Academic: € 370 Commercial: € 495 *To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Our standard policy is to provide all full-time students, be they Undergraduates or Masters students, access to student participation rates. Part-time master and doctoral students who are also currently employed will however, be allocated academic status. Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number. Register Here The number of participants is limited to 8. Places will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 5 individuals are enrolled. Course fees cover: I) teaching materials – copies of lecture slides, databases and Stata programs specifically developed for the course; ii) a temporary licence of StataNow™ valid for 30 days from the day before the course commences. Individuals interested in attending this course must return their completed registration forms by e-mail to TStat by the 20th of September 2026.
This interactive online course offers an introduction to the mapping techniques currently available in Stata for visualization of geospatial data. The next edition of this training course will be offered online on a part-time basis on the 30th of September.
Urban, Rural and Transportation Economics (JEL R)
Ulrich Bindseil Joins Frankfurt School as Professor of Practice in Financial Infrastructure
Frankfurt School of Finance & Management has appointed Ulrich Bindseil as a Professor of Practice in Financial Infrastructure, effective 1 September 2026. Bindseil, a distinguished figure in central banking and financial market infrastructure, is transitioning from the Technische Universität Berlin to strengthen the Economics & Law Department at Frankfurt School. His expertise encompasses payment infrastructures, cross-border transactions, and innovations in currency forms—including stablecoins and central bank digital currencies. This appointment is set to enhance both research and teaching initiatives in the Centre for Digital Economics (C4DE) and the Centre for Central Banking. Markus Reisinger, Head of the Economics & Law Department, expressed enthusiasm about Bindseil’s arrival, underscoring his extensive background in the digital economy and financial technologies. "He is a perfect fit for our department and the Frankfurt School at large," Reisinger stated. Co-Pierre Georg, Director of the Centre for Digital Economics, remarked on the timely nature of Bindseil's expertise, noting it aligns with the ongoing transformation of financial market infrastructures driven by new technologies. Recognized for his international leadership in monetary policy and regulatory frameworks, Bindseil previously served in key positions at the European Central Bank (ECB) and is also a respected author in the field. "I am delighted to join Frankfurt School, where I can help bridge central banking with digital innovations," said Bindseil, highlighting the school’s unique positioning during this pivotal growth phase in financial technologies. Read the full press-release: frankfurt-school.de
Frankfurt School of Finance & Management welcomes Professor Ulrich Bindseil from Technische Universität Berlin to bolster its Centres for Digital Economics and Central Banking from September 1, 2026. Bindseil, an expert in central banking and financial infrastructures, will enhance research and education, focusing on payments and monetary innovations like stablecoins and digital currencies.
Frankfurt School of Finance and Management
2026 Top-Up Fanno & Foscolo Fellowships Target: Students and researchers Category: Research support Date: 20 July 2026 - 30 October 2026 This initiative aims to attract and retain outstanding PhD graduates in Economics or Finance by enabling academic institutions to offer internationally competitive compensation packages, consistently with UniCredit Foundation mission to invest in talent and promote academic excellence in Europe. Fellowship Details UniCredit Foundation will award 7 Top-Up fellowships (2 Marco Fanno and 5 Ugo Foscolo), intended for university departments or research institutes in Economics or Finance located in countries where the UniCredit Group operates. The fellowships consist in salary top-ups aimed at supporting and recognizing outstanding researchers. Total value: €150,000 for each fellowship Duration: 3 years Eligibility Criteria To be eligible, applicants must meet the following criteria: Be departments or research institutes in Economics, Finance, or related fields Be located in one of the Countries where UniCredit Group operates Recruit outstanding job market candidates through internationally recognized selection processes, including participation in global PhD job markets such as the US job market or equivalent European placement processes Apply rigorous and transparent evaluation methods aimed at appointing exceptional early-career scholars to post-doctoral or tenure-track positions. Key Dates Application deadline: 30 October 2026 Announcement of winners: by 11 December 2026 This initiative reflects UniCredit Foundation’s commitment to investing in young talent, supporting academic excellence, and fostering the next generation of economists and researchers. Download the announcement Apply Eligibility Who can apply? Applications can be submitted by departments or research institutes active in Economics, Finance, or related fields. Can individual candidates apply for a fellowship? No. The fellowship is awarded to the institution which subsequently appoints the selected recipient through its own recruitment process. Can research institute (rather than a university department) apply? Yes, both university departments and research institutes in eligible countries may apply, provided they operate in Economics, Finance, or related fields and satisfy the eligibility requirements. Which countries are eligible for Fellowships? Eligible institutions must be in the countries where UniCredit operates: Austria; Bosnia and Herzegovina; Bulgaria; Croatia; Czech Republic; Germany; Hungary; Italy; Poland; Romania; Serbia; Slovakia; Slovenia. Is the United Kingdom eligible? No. The United Kingdom is not included among the eligible countries. Can institutions that received a fellowship in previous years apply again? The applicants have not been awarded either the Fanno Top-up or the Foscolo Top-up fellowship in the previous year (i.e. 2025). Contact Information For further information email: info@unicreditfoundation.org
UniCredit Foundation announces the opening of the Top-Up Fanno & Foscolo fellowships competition for students and researchers in Economics and Finance.
Up and Running in Stata - Live Online Training
TStat live online training courses are offered interactively via Zoom with a qualified trainer in real-time. All materials (slides, datasets and Stata routines specifically developed for the course) are made available for download before the start of the course. COURSE DESCRIPTION Up and Running in Stata provides participants with the necessary introductory toolset to enable them to carry out efficient data analysis and data management in Stata in a reproducible manner. The interactive course covers everything from the very basics, in order to get one up and running in Stata, to an overview of the available Stata commands for preliminary data analysis, data management, importing and exporting data formats, merging of databases and the creation of graphs in Stata. In common with TStat’s training philosophy, Up and Running in Stata is by its very nature to be considered very much a hands on approach to learning Stata course. Each individual session is composed of both a theoretical component and an applied segment, during which participants have the opportunity to implement the techniques illustrated under the watchful eye of the course tutor. COURSE ID: D-IN10-OL LANGUAGE: English TARGET AUDIENCE This interactive online course is invaluable to both professionals and researchers (working in Epidemiology, Business Studies, Marketing and Management, Biostatistics, Economics, Epidemiology, Medicine, Public Health, Social and Political Sciences and Statistics), wishing to use Stata for applied statistical analysis, data management or data visualization. PREREQUISITES Familiarity with PCs and a working knowledge of English. PROGRAM SESSION I: GETTING STARTED SESSION II: STATA FUNDAMENTALS SESSION III: CREATING SIMPLE STATA ROUTINES: DO FILES SESSION IV: IMPORTING DATA FROM SPREADSHEETS SESSION V: MERGING DATA BASES SESSION VI: GRAPHICS – AN INTRODUCTION SUGGESTED READINGS - Acock, A. (2023). A Gentle Introduction to Stata. Revised Sixth Edition. Stata Press Publication. - Kohler, U. & Kreuter, F. (2012). Data Analysis Using Stata. Third Edition. Stata Press Publication. - Mitchell, M. N. (2020). Data Management Using Stata: A Practical Handbook. Second Edition. Stata Press Publication. DATE AND LOCATION The next 2026 edition of this training course will be offered online on a part-time basis on the 19th-20th, 22nd-23rd of October from 9:30 am to 1:30 pm CET. REGISTRATION FEE AND REGISTRATION FORM Full-Time Students*: € 545.00 Ph.D. Students: € 710.00 Academic: € 1295.00 Commercial: € 1505.00 *To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Our standard policy is to provide all full-time students, be they Undergraduates or Masters students, access to student participation rates. Part-time master and doctoral students who are also currently employed will however, be allocated academic status. Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number. The number of participants is limited to 8. Places will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 5 individuals are enrolled. Course fees cover: I) teaching materials – copies of lecture slides, databases and Stata programs specifically developed for the course; ii) a temporary licence of StataNow™ valid for 30 days from the day before the course commences. Individuals interested in attending this course must return their completed registration forms by e-mail to TStat by the 9th of October 2026. To request further information or the registration form, please fill in the form on our website or send an email to training@tstat.eu
Up and Running in Stata provides participants with the necessary introductory toolset to enable them to carry out efficient data analysis and data management in Stata in a reproducible manner. The next 2026 edition of this training course will be offered online on a part-time basis on the 19th-20th, 22nd-23rd of October from 9:30 am to 1:30 pm CEST.
GTAPinR Course "Running the GTAP Model using R packages HARr and tabloToR" General Information Dates: Open enrollment Learners can enroll in this course at any time. Once payment has been received and access to the learning management system has been granted, learners will have 56 days to complete the course before access ends. Since the course is self-paced, instructor assistance is not available during or after the course. No feedback or grading beyond automated assessments will be provided. Location GTAPinR is delivered fully online. There is no onsite component to this course. Offered by Center for Global Trade Analysis, Purdue University Enrollment Fees $625 Professional (Developed Country) $470 Professional (Developing Country) $315 Student (Developed Country) $160 Student (Developing Country) Why You Should Enroll Enhance your GTAP skills with open-source R tools designed to streamline simulation and analysis! Execute the GTAP Model directly in R - Learn how to run GTAP simulations end-to-end within the R environment using the open-source HARr and tabloToR packages Streamline data handling and model workflows - Gain practical skills in reading and writing GTAP data, solution files, and header arrays directly in R, enabling more efficient experiment setup and result processing Build practical skills for GTAP workflows - Build hands-on skills in GTAP workflows – setting closures, applying shocks, solving the model, and extracting results all in R environment Strengthen policy analysis with advanced visualization - Turn outputs into insight with R visualization workflows, improving interpretation, communication, and presentation of simulation results Flexible, self-paced learning for busy schedules - With open enrollment and a 56-day completion window, the course is designed to fit into busy schedules while providing structured modules, practical exercises and assessments Course Content This course aims to empower GTAP users with practical tools in R statistical software to enhance simulation analysis for policy making through improved processing of economic model outputs. The specific objective of this course is to introduce the R packages "HARr" and "tabloToR" which are open-source programs specifically designed to interface with the GTAP Model, as well as its outputs and inputs. The "HARr" package serves as a bridge between the GTAP Data Bases and the R environment by enabling the reading and writing of the header array (.har) file format adopted in the GEMPACK suite. The "tabloToR" package facilitates GTAP simulations by interpreting the model in GEMPACK-style TABLO (.tab) files and solves it within the R environment after the model inputs are defined. This course covers topics such as basic installation of the R packages, utilizing its features, as well as data visualization emphasizing practical, hands-on experience. This course is geared towards current users of the GTAP Model who are interested in running the model and processing its inputs and outputs within the R environment. Course Structure The course is delivered fully online and includes reading materials, codes, audio-visual lectures, hands-on exercises, and weekly assessments, all designed to enhance learning. As a self-paced online course, participants will work through the weekly plan of study outlined in the learning management system. Since the course is self-paced, instructor assistance is not available during or after the course. No feedback or grading beyond automated assessments will be provided. Learners will have 56 days from the date access to the learning management system was granted, to complete the course before access ends. Content Overview Module 1 - Getting Started with GTAPinR Module 2 - Reading and Writing Databases with HARr Module 3 - Solving the Model with tabloToR Module 4 - Wrap-Up and Evaluation Time Commitment Participants should expect to spend approximately 2-6 hours per module on this course. Prerequisites Participants are expected to know how to run the GTAP Model and be familiar with the inputs used in running the model. Participants are also expected to know how to use R statistical software. Enrollment Individuals interested in enrolling in this course must register on the GTAP website, after which they will be provided details for making payment by bank transfer or credit card. Once payment has been received and access to the learning management system has been granted, learners will have 56 days to complete the course before access ends.
Enroll in the GTAPinR Course to enhance your GTAP skills with practical tools in R statistical software for policy making.
Public Economics & Economic Policy (JEL H)
Economic Development, Innovation and Growth (JEL O)
Agricultural and Environmental Economics (JEL Q)
2027 Short Course in Dynamic Global Trade Analysis
2027 Short Course in Dynamic Global Trade Analysis "Applied General Equilibrium Analysis using a Dynamic Multi-Region Model" General Information Course Dates (Course phases are not offered independently.) Phase 1 (Online): January 18 - February 28, 2027 Phase 2 (Onsite): March 8-12, 2027 Application Deadline August 30, 2026 Location Purdue University, 403 Mitch Daniels Blvd., West Lafayette, Indiana 47907 USA Offered by Center for Global Trade Analysis, Purdue University Enrollment Fees $5,000 USD Professional (Developed Country) $3,750 USD Professional (Developing Country) $2,500 USD Student (Developed Country) $1,250 USD Student (Developing Country) Why You Should Apply Move Beyond Static Analysis: Model the Global Economy Dynamically! Master dynamic CGE analysis with the GTAP-RD framework - Analyze how the impact of trade and climate policy shocks unfold over time. Develop the skills to design, run, and interpret recursive dynamic simulations using GTAP-RD Models. Address High-Impact Global Policy Challenges - Sharpen your skills to produce policy-relevant analysis. Apply your modeling toolkit to mentored capstone projects focused on real-world impacts of major trade blocks and climate frameworks. Implement real-world baseline scenarios - Learn how to construct business-as-usual baselines using Shared Socioeconomic Pathways (SSPs), projections, and dynamic closures for credible long-run policy analysis. Build proficiency in the dynamic software - Gain extensive hands-on experience with RunDynam through guided labs that translate conceptual model features into operational workflows for scenario setup, policy execution, and result analysis. Learn directly from GTAP experts - Combine flexible online preparation with an immersive onsite residency at Purdue University, collaborating with peers and GTAP experts to deepen understanding and refine your own applications. Course Content The objective of this course is to introduce participants to dynamic economic analysis in an applied general equilibrium setting. This course is designed to provide participants with ample hands-on training with software that has been tailored for undertaking analysis using a dynamic recursive model. Participants will interact with economists working on global trade and environmental issues using a dynamic framework. The end goal of the course is for participants to leave with the capability of conducting and analyzing their own dynamic simulations. Course Structure The GTAP Dynamic Short Course is comprised of an online instruction phase followed by an intensive onsite course delivered by a team of GTAP specialists in data, model construction, and policy analysis. Note that the online and onsite phases of the course are not offered independently. Prerequisites It is expected that applicants have undertaken courses on multi-region general equilibrium analysis (such as the standard GTAP Short Course) or have completed a significant piece of analysis using a dynamic multi-region GE model or another, comparable general equilibrium model. Evidence of the latter (e.g., citations or links to papers, reports, and/or presentations) should be included with application. Application Individuals interested in taking this course must apply on the GTAP website. All applications will be reviewed following the deadline. Those accepted into the course will receive an email including an invitation letter and details for registration and payment. Note that application does not guarantee acceptance.
Join the GTAP Dynamic Short Course at Purdue University for a comprehensive exploration of dynamic economic analysis.
Target Students and researchers Category Research support Call Open 20 July 2026 - 16 October 2026 The initiative aims to support and recognise outstanding early-career researchers in the fields of economics and finance, enabling them to pursue innovative research projects and contribute to the advancement of academic research, in line with UniCredit Foundation’s mission to invest in talent and promote scientific excellence. Research Grant Details UniCredit Foundation will award 4 research grants to researchers of any nationality employed at European universities located in Countries where UniCredit Group operates. At least one of the four grants will be awarded to a female candidate. Grant amount: €25,000 gross per year, renewable for a second year, up to a maximum total amount of €50,000 gross per recipient Duration: up to two years. Key Dates Application deadline: 16 October 2026 Announcement of winners: by 1 February 2027 This initiative reflects UniCredit Foundation’s commitment to investing in young talent, supporting academic excellence and fostering the next generation of economists and researchers. - Download the announcement - Apply Eligibility Who can apply for the grant? The competition is open to young researchers of any nationality employed at European universities located in the countries where UniCredit Group operates (see following question). Which countries are considered part of UniCredit’s footprint? The eligible countries are: Austria, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Germany, Hungary, Italy, Poland, Romania, Serbia, Slovakia, and Slovenia. Is there an age limit? Yes, applicants must be born on or after 1 January 1991. Exceptions apply: female candidates: +1 year per child; male candidates: extension equivalent to documented parental leave. What academic qualifications are required? Applicants must hold a PhD in economics, finance, or a related quantitative field (e.g., statistics, mathematics, data science) with a demonstrated focus on economic or financial applications. What professional position is required? Applicants must hold positions such as: Assistant Professor, Research Assistant or equivalent position at a European university within eligible countries (see previous questions). Is a publication record required? Yes, candidates must have at least two articles published or forthcoming in international journals. What kind of research project is required? Applicants must submit an original research project in economics or finance that: is not funded by other sources, is suitable for academic publication. Can I apply with co-authors? Yes, but all co-authors must meet the same eligibility requirements. When must the research project start? The project must start no later than March 2027. Are funded projects eligible? No, projects already funded by other sources are not eligible. Are PhD students eligible to apply? No. Only candidates who already hold a PhD in economics, finance, or a related quantitative field are eligible to apply. Current PhD students do not meet eligibility requirements. Are Associate Professors eligible? No. Associate Professors are not eligible, either as principal applicants or as co-authors. The Modigliani Research Grant is intended for early-career researchers holding positions such as Assistant Professor, Research Assistant, or equivalent roles. Do previous academic positions need to be held in an eligible country? No. The eligible-country requirement (see previous Questions) applies only to the applicant’s current academic position. Previous academic appointments may have been held in any country. Can a project receive additional funding from other sources during the grant period? No. Projects that are already funded or expected to receive funding from other institutions during the grant period are not eligible. Applicants must ensure that the proposed research project is carried out exclusively under the support of the Modigliani Research Grant. Is a project eligible if it has received funding only for a pilot phase? Funding received exclusively for a preliminary pilot phase may be evaluated on a case-by-case basis. Applicants should clearly describe the nature, scope, and purpose of any previous funding in their application. The Scientific Committee and UniCredit Foundation reserve the right to assess whether such funding affects the project's eligibility. Contact Information For further information email: info@unicreditfoundation.org
UniCredit Foundation is pleased to announce the launch of the 17th edition of the Modigliani Research Grant, established in memory of Nobel Laureate Franco Modigliani.
Research & Teaching Assistant (Finance) m/f/d | Part-Time (70%)
What you can look forward to: - A “we, not me” culture – a highly collaborative, team-oriented work atmosphere with open and honest communication - A “can do” spirit – we are looking for people who want to make a difference - Exciting research projects for doctoral studies and intensive exchange with the supervising professors - Close integration of theory and practice Your responsibilities - Conduct quantitative data analysis, support empirical research projects, and manage, clean, and analyze survey and field data using statistical software - Apply artificial intelligence (AI), blockchain, and digital technologies to support innovative research projects, particularly in agricultural and related fields - Collaborate with researchers, prepare research materials, and ensure accurate documentation of research findings - Support the preparation, supervision, and delivery of courses, tutorials, and guidance for bachelor’s and master’s theses - Contribute to scientific and practice-oriented publications, academic collaborations, and the communication of research results Your profile - A strong Master’s degree (good to very good) in Economics, Mathematics, Computer Science, or a closely related quantitative discipline, demonstrating solid analytical, statistical, and methodological expertise - Strong quantitative and analytical skills with experience in empirical research and statistical analysis - Proficiency in statistical software such as MATLAB, R, Stata, or similar programming tools - Experience with randomized controlled trials, field survey data, and research methods in development economics - Highly motivated, responsible, and independent researcher with the ability to work effectively in an academic environment We seek the best candidates. In doing so, we value personality, talent, determination, and character. We expressly emphasize that all individuals—regardless of gender, nationality, ethnic or social background, religion/ideology, disability, age, or sexual orientation—are equally welcome. Applicants with disabilities will be given preferential consideration if equally qualified. For inquiries, please contact Prof. Raša Karapandža, Ph.D. | Rasa.Karapandza@ebs.edu | Telefon: +49 (0) 6723 9168 - 244 Become part of our team and apply online, stating your earliest possible start date and reference number 16584. EBS University of Business & Law | Human Resources | hr@ebs.edu Visit us at: www.ebs.edu/de/stellenangebote
Enrich EBS. We are a private university dedicated to educating tomorrow’s leaders who strive to make a difference in the world -economically, socially, and ecologically. We aim to help shape global transformation through outstanding knowledge transfer and the promotion of strong interpersonal skills. At EBS Business School, EBS Law School, and EBS Executive School, a total of 2,200 students and professional development participants engage in learning and research. With our education philosophy of Challenge-Based Learning, personalized coaching, extracurricular engagement, and a powerful alumni network, EBS holds a unique position in the German higher education landscape. We are part of SRH—a leading provider of education and healthcare services with 17,000 employees. SRH operates private universities, educational centers, schools, and hospitals. We are looking for a research assistant (m/f/d) for the Chair of Finance (Prof. Raša Karapandža Ph.D.) at EBS Business School, starting September 1st, 2026.
Junior Professorship (W1) with Tenure Track to W2 ”Management of Bio-based Value Chains“
Martin Luther University Halle-Wittenberg's aim in offering the junior professorship with tenure track is to enable young academics with excellent prospects to become independent at an early stage of their career and to offer them long-term career prospects. The junior professorship with tenure track is aimed at academics in the early career phase. At the Faculty of Natural Sciences III, Institute of Agricultural and Nutritional Sciences (IAEW) at Martin Luther University Halle-Wittenberg, the following position is to be filled: Junior Professorship (W1) with Tenure Track to W2 ”Management of Bio-based Value Chains“ We are seeking a candidate with in-depth knowledge and an outstanding doctorate in agricultural economics or related disciplines. The candidate should be involved in research and teaching in the field of demand-oriented management of bio-based value chains and after completing the tenure track, will represent the entire broad spectrum of the subject area in research and teaching. Bio-based value chains form the basis of modern sustainable agriculture. Appropriate management of bio-based value chains should reduce negative social externalities and promote positive economic, ecological and social development, at the same time being subject to dynamic change. On the one hand, there are technological innovations, e.g. in the substitution of fossil raw materials in the bioeconomy, the cascade use of plant and animal products, and the efficient use of resources through circular economy. On the other hand, change arises from organisational innovations within value chains, for example through digitised and automated business processes, as well as digital and platform-based transactions between stakeholders. The junior professor therefore will focus on examining the impact of innovations on businesses, end consumers and the agri-environmental system across the value chain – always from a business management and strategic planning perspective. Relevant areas of research include but are not limited to: new customer-centric business models, risk management and corporate resilience in the face of increasingly volatile environmental factors, relationship marketing, cross-functional quality management, corporate social responsibility, consumer and societal acceptance of business models and the promotion of positive externalities. Methodologically, one can employ both quantitative and qualitative methods of business analysis and planning, agricultural economic modelling as well as life-cycle assessment and sustainability metrics for estimating environmental impacts. The research focus of the professor offers a wide range of opportunities for cooperation with other professorships at the IAEW and the Faculty. Examples are joint research on new concepts for coupled plant and animal production to replace fossil raw materials and energy sources or to assess environmental impacts. Links are also available to existing and future interdisciplinary joint projects (e.g. DiP – https://www.dip-sachsen-anhalt.de and JTC – https://jtc.uni-halle.de) focusing on innovation and the design of socioeconomic agricultural systems. Research should be compatible with the university's profile areas, in particular ‘transformation’ and ‘sustainability’. Networking is highly desired with the natural and nutritional sciences within the university and with socio-economic institutions, such as the Leibniz Institute for Agricultural Development in Transition Economies (IAMO). A willingness to cooperate in related fields is expected with the aim of contributing to the research output and third-party funding of the IAEW. Experience in independent scientific publishing must be demonstrated through relevant international publications. Experience in acquiring third-party funding is an advantage. The focus of the teaching offered by the professor should be on management in the bachelor's and master's programmes at the IAEW, e.g. on the management of value-chains, marketing, and corporate social responsibility. Experience in teaching and supervising academic theses is expected. The willingness and ability to teach in German and English is a prerequisite. The faculty places great emphasis on committed and innovative teaching. Applicants are expected to demonstrate a commitment to delivering attractive teaching through innovative ideas. Experience in the development and design of degree programs is desirable. In addition, a cooperative, team-oriented approach to work and strong communication skills are desirable. Participation is also required in academic self-administration and a willingness to take on leadership responsibilities at Martin Luther University Halle-Wittenberg and to improve personal development. Further duties are specified in Section 34 of the Saxony-Anhalt Higher Education Act. Applicants must fulfil the requirements of being appointed according to Section 40 of the Saxony-Anhalt State Law of Universities. In particular, the prerequisites for employment are a completed university degree, pedagogical ability and a particular aptitude for scientific work, which is usually demonstrated by the outstanding quality of a doctorate. As a rule, the time period between the final doctoral degree examination and the application for this junior professorship should not exceed six years (family-related reductions in working hours are taken into account appropriately when assessing the academic age). Junior professorships are filled on the basis of a temporary civil service contract, initially for a period of three years. The position shall be renewed for a further three years conditional upon a positive mid-term evaluation. Upon a positive tenure evaluation, and without a new vacancy announcement, there is a transition to a permanent W2 professorship. If the general legal requirements are met, this transition includes appointment through a lifelong civil servant relationship, or alternatively, transfer to an indefinite private-law employment relationship. The Martin Luther University Halle-Wittenberg is committed to diversity and aims to create an inclusive environment where our staff and students feel valued and inspired. In particular, we aim to increase the proportion of women appointed as permanent staff; therefore, appropriately qualified female candidates are strongly encouraged to apply for this position. The Martin Luther University Halle-Wittenberg strives to provide an environment compatible with work-life balance and has been certified as a family-friendly university since 2009. Applications from candidates with disabilities are particularly welcome, and will be given preference in the event of equivalent qualification. Applications are accepted only electronically via the Appointment Portal of Martin Luther University Halle-Wittenberg at: https://berufungsportal.uni-halle.de/ before 18.09.2026. With the application the following information must be submitted: a cover letter, a detailed curriculum vitae should be uploaded including the candidate’s scientific career and qualifications, academic certificates, a list of publications and successful third-party funding as well as a teaching record including evaluations. Please include a statement outlining future research plans, with a brief description of your research project for the next six years of the junior professorship in relation to MLU as a research institution (maximum 2 pages), as well as a teaching plan (1–2 pages). For further subject-related information, please contact Prof. Dr. Jan-Henning Feil (Tel.: +49 345 55 22361, e-mail: jan-henning.feil@landw.uni-halle.de). Please note: Applications to his email address will not be considered. If there are any questions about the appointment portal, please contact the Dean of the Faculty of Natural Sciences III and Chair of the Appointments Committee, Prof. Dr. Matthias Müller-Hannemann, (e-mail: dekanat@natfak3.uni-halle.de). The call for applications is subject to possible budgetary restrictions. Please note that the University is unfortunately unable to reimburse costs associated with the application.
We are seeking a candidate with in-depth knowledge and an outstanding doctorate in agricultural economics or related disciplines.
Martin-Luther-Universität Halle-Wittenberg
2026 Economic Job Market Best Paper Award
Target: Students and researchers Category: Research support Date: 20 July 2026 - 12 October 2026 The Award aims to promote excellence in economic research by recognising outstanding Job Market papers and enhancing the visibility of the most talented young economists as they enter the international academic job market, in line with UniCredit Foundation’s mission to invest in talent and support the next generation of researchers. Award Details UniCredit Foundation will award 5 prizes of €1,000 gross each. The Award is granted directly to individual candidates, irrespective of their nationality or the university at which they are enrolled. Preference is given to single-authored papers demonstrating high levels of originality and publication potential. Key Dates Application submission deadline: 12 October 2026 Announcement of winners: by 20 November 2026 Download the announcement For further information email: info@unicreditfoundation.org Apply Here Eligibility Who is eligible to apply? Applicants must be PhD candidates or PhD graduates that will participate to the European Job Market for Economists 2026. Is registration for the European Job Market for Economists required? Yes. Registration for the European Job Market for Economists 2026 is a mandatory eligibility requirement. Applicants must provide evidence of participation, such as a confirmation email or a screenshot of the relevant registration or participation webpage. Can recent PhD graduates apply? Yes. Both PhD candidates and recent PhD graduates who are participating in the EJM 2026 may apply. Can applicants from non-European universities participate? Yes. Eligibility is not restricted by geographic location or university nationality, provided the applicant is participating in the European Job Market for Economists 2026.
UniCredit Foundation announces the launch of the 13th edition of the Economic Job Market Best Paper Award, promoted in collaboration with the European Economic Association.
Two Tenure-Track Assistant Professorships (2 x 100%) - Specialisation: Empirical Management Research
The two professorships are in empirical management research, with a particular interest in the opportunities and challenges associated with digital transformation, data-driven decision-making, and the increasing use of artificial intelligence in organisations and the wider economy. Relevant research areas include decision research, human resource management, managerial accounting, innovation and digitisation, industrial organisation, information systems, data science for business, and related areas of empirical management research. Methodologically, the research profiles should be grounded in quantitative empirical approaches, including field or laboratory experiments and/or the analysis of organisational, personnel, or other data sources using modern quantitative methods. Successful candidates fit well with the faculty’s existing research cluster in applied microeconomics and econometrics and contribute to collaboration and exchange across research groups. Ideal candidates therefore also build on perspectives from microeconomics and address topics of societal relevance related to the existing research cluster. Your responsibilities Conducting methodologically rigorous and societally relevant empirical management research, guided by open science practices. Publishing regularly in high-quality scientific journals in the field. Actively participating in relevant national and international workshops and conferences. Supervising Bachelor’s, Master’s, and doctoral theses. Acquiring competitive third-party funding from institutions such as the Swiss National Science Foundation and other Swiss or international funding bodies. Establishing research and teaching cooperations with other universities and with other faculties at UniDistance Suisse. Teaching in the faculty’s Bachelor programmes (in German, French, and/or English) and/or in the Master’s and Doctoral programmes (in English). Developing and implementing innovative teaching methods, with a particular focus on e-learning and digital education. Actively supporting the further development of the faculty, the faculty’s study programmes, and the range of courses offered by the faculty. Contributing to and further developing continuing education courses and the faculty’s services targeted at a broader public. Your profile Doctoral degree and research experience in a relevant field. Expertise in the application of quantitative empirical research approaches, including field or laboratory experiments and/or the analysis of organisational, personnel, or other data sources using modern quantitative methods. A promising research agenda in empirical management research, with a specialisation that complements the faculty’s existing research cluster and demonstrates integration into an international research network. A promising research and publication pipeline, ideally including first publications in high-quality journals in the field. Experience in acquiring, or strong potential to acquire, competitive third-party research funding. Willingness and ability to develop effective online teaching modules in areas related to the candidate’s specialisation; teaching experience, particularly in blended learning, is an advantage. Proficiency in English and active knowledge of either German or French is required, together with passive knowledge of the other language, as well as the ability and willingness to teach in German or French within one year. We offer Tenure-track assistant professor positions, with the possibility of promotion to a permanent position, subject to a positive tenure evaluation. Support and dedicated funding to establish and develop a research and teaching team. A dynamic academic environment within a research-intensive institution, characterised by strong scientific expertise, interdisciplinary collaboration, and a growing international profile. A high degree of autonomy and responsibility, offering substantial scope for initiative, ownership, and professional development within a dynamic and evolving institutional context. The opportunity to contribute to and shape innovative approaches to online teaching and distance education, supported by comprehensive training and technical pedagogical support. An inspiring working environment at our campus in Brig, located close to the historic town centre and the Swiss Federal Railways (SBB) station, with excellent rail connections across Switzerland and Europe as well as into the surrounding mountain region, offering a high quality of life. Attractive employment conditions, including modern working arrangements, competitive social benefits, and access to a wide range of continuing education and professional development opportunities. A strong institutional commitment to equity, diversity, inclusion, and accessibility. We particularly encourage applications from individuals who are underrepresented in academia and from candidates with diverse backgrounds, experiences, and career paths. How to apply We look forward to receiving your full application in one PDF document no later than 13 September 2026 at the following email address: jobs@unidistance.ch. Your application should include the following elements: Letter of motivation, CV (including a list of publications, working papers, and/or current research projects), teaching experience, teaching evaluations (if available), list of third-party funds (if applicable), research statement (maximum 2 pages), teaching statement (maximum 2 pages), and the names and contacts of two to four references who are ready to supply a reference letter upon request. For administrative questions regarding the procedure, please contact Mr Patrick Constantin, Head of Human Resources (patrick.constantin@unidistance.ch). For academic questions, please contact Prof. Manuel Grieder, Dean of the Faculty (manuel.grieder@unidistance.ch).
UniDistance Suisse offers fully online university degree programmes and is accredited under the Swiss Higher Education Act (HEdA). It combines flexible, location-independent, part-time distance learning with an active, interdisciplinary research culture on its campus in Brig. Across five faculties – Law, Psychology, Business & Economics, History, and Mathematics & Computer Science – we have been shaping modern teaching, learning, and research for more than 30 years. Today, UniDistance Suisse has over 2,700 students and around 550 staff members. To further support the growth and development of UniDistance Suisse, the Faculty of Business & Economics invites applications for two Tenure-Track Assistant Professorships (2 x 100%), Specialisation: Empirical Management Research Place of work: Brig | Start of work: 1 February 2027 or upon agreement
Understanding bias Bias means that an estimator we’re using to learn about a real-world phenomenon is not accurate. It’s not hard to see that this is a bad thing! On the flip side, if an estimator is unbiased, it’s (usually) accurate and thus we can trust it without doing any further work. The linguistic definition of bias varies and can be misleading, but the mathematical definition is precise. We’ll use it to explore the concept of bias further. It states: \begin{equation*} \mathbb{E}(\hat{\beta}) - \beta = 0 \end{equation*} Or, equivalently, \begin{equation*} \mathbb{E}(\hat{\beta}) = \beta \end{equation*} This equation says “the expectation of our estimator of beta minus the true value of beta is zero”, or equivalently, “the expectation of our estimator of beta equals the true value of beta”. Beta is simply a Greek letter that’s most often used to denote a regression coefficient; here, it symbolizes the effect we’re interested in studying. The expectation symbol (the fancy “E”) is a very important part of this definition: it gives the expected value, or mean, of the quantity inside it. (If you need touching up on mathematical symbols, we’ve got you covered.) Let’s dig deeper into how this works. The expectation of a constant is trivial, since constants don’t vary and the mean of a constant is itself. So for example, the expectation of 4 is just 4. Not very exciting. But when it comes to random variables that can take on many different values — like “the number of heads in 5 coin flips” — the expectation is suddenly very useful. It tells us what our “best bet” is: the average (or most likely) outcome. Of course, in 5 coin flips, the expected number of heads is 2.5, and it’s impossible to get 2.5 heads out of 5 flips. But, this is still an intuitive way to think about it. Therefore, the expectation of our estimator \hat{\beta} tells us its expected value, or in other words, what we’d guess it would be if we ran an experiment to measure it. When we’re using this estimator \(\hat{\beta}\) to study, say, the effect of car sales on carbon dioxide emissions, we want its expected value to be the true effect that selling a car has on emissions. If our estimator is wrong on average — say, it oversells the true effect and makes selling cars look worse than it actually is (or vice versa) — then it is biased. In other words, if the estimator’s expectation is not equal to the true, real-world effect (represented by \(\beta\) ), it is biased! Meanwhile, estimators that satisfy this criteria are unbiased, which is great news! Unbiased estimators are usually preferred over biased ones, all else equal. Examples of bias in economics Sadly, in economics and many sciences, naturally-occurring unbiased estimators are few and far between. This is often the case because of how complicated and interconnected economic factors are. When one factor affects another in economics, we usually need to work hard to disentangle the effects of everything else in the economy on those two things. If we can do so, we will be able to study them in “isolation”. This will lead us to getting an unbiased estimator, but it can be devilishly tricky. For example, we can’t simply throw investment and GDP into a regression and expect to get an unbiased estimate for “how much investment increases GDP”. This is a classic endogeneity problem, because investment increases GDP, but a higher GDP means an economy will have more funds to invest! And even besides this issue, there are many factors that increase both investment and GDP at the same time: a fall in interest rates, for example, can spur investment and increase GDP. Therefore we must account for interest rates when considering this relationship. In fact, once you learn how to think about bias in economics, it becomes difficult to find an estimator that isn’t biased. The effects of education on income are biased because of people’s innate ability — those with more ability tend to get more schooling, and those with more schooling tend to earn higher wages. An estimate of how city parks affect people’s life satisfaction would be biased, too — wealthier neighborhoods can afford more parks, but also likely have higher life satisfaction than poorer neighborhoods because they’re wealthier (not because of the parks). The effects of policing on crime can be biased because high-crime areas attract more police attention. Analyzing these biased scenarios improperly can lead to erroneous results — with this last example, a simple regression might suggest that more police cause more crime! There are nearly infinite examples of how an estimator could be biased in economics. It seems important, then, that we understand what the effects of bias on our analysis are. Bias causes inference to be inaccurate As we discussed above, the mathematical definition of bias tells us that if an estimator is biased, it’s inaccurate. Let’s expand on this idea further. The regression coefficient \(\hat{\beta}\) is supposed to give an idea of the effect size of x on y. This number is usually calculated with a computer program and reported with supplementary statistics that tell the economist how “significant” the result is. But if the estimator is biased, a significant result might lead us to conclude that the true real-world effect size is close to the biased estimator’s value. By the definition of bias, this is wrong! (Think of our policing example above.) Even worse, our estimate of the sample mean is often used to construct so-called confidence intervals. If we have a biased estimator, the sample mean will also be biased. Confidence intervals are ranges that tell the researcher “with 95% likelihood, the true effect size lies within this range” (or a similar percentage). For example, if we find that 1 additional year of schooling increases future income by $5000, a confidence interval might range from $4300 to $5700. Then, the researcher is led to believe that the true population value lies within that interval with a high probability — it’s at least $4300 or not more than $5700. However, if the estimator was biased this can be completely wrong. We can’t observe innate ability (we can use imperfect measures like IQ scores, but let’s ignore that for now), which increases both the likelihood of getting more education and the likelihood of making a higher income. This means that our simple estimator is biased upwards, since the regression falsely attributes all of “innate ability”’s power for increasing income to “education” instead. Therefore, it’s possible the true effect would be only $3900, which is completely outside of the biased confidence interval that the researcher is now very confident in! Correcting bias If we end up with a biased estimator, not all is lost. There are methods to correct bias. Most of these involve accurately diagnosing the form of the bias and ensuring that appropriate steps are taken to resolve the issue. Let’s return to some of our earlier examples. Suppose we wanted to measure the effect of parks on people’s life satisfaction, and we run a regression to capture this effect. If we don’t have data on wealth included, clearly we have an omitted variable problem and the estimator will be biased. But there’s an easy solution in this case: collect data on wealth of the areas used in the study, and this will ensure the effect of wealth is not captured by our “parks” variable. This can remove the bias. Sometimes, it’s not possible to collect data that we might need to remove the bias. Consider “attenuation bias”, which is caused by measurement error that systematically under- or over-states the true values — if our measurements themselves are biased, clearly we can’t simply fix that by collecting more data. One great example of this is self-reported income: people tend to overstate their income in surveys, which leads to a bias we cannot remove. In cases like these, we can turn to advanced regression and statistical inference methods to help us analyze the data and attempt to recover the truth. Still, it’s better to simply avoid these scenarios if at all possible. Identifying and correcting bias in an economic or data science model is complex and can require years of studying to master. Still, this section should have given a good intuition for how it can be done. We’ve spent quite a while now discussing bias and its effects, and clearly, ceteris paribus, it’s better to have an estimator that is unbiased. And yet… A little bias is often acceptable Bias is sometimes not only okay, but even welcome in an analysis. Of course, this begs a question. Why on earth would we ever want an estimator to be biased?! Has this whole article been a lie? The unfortunate truth is that bias and its cousin, variance, have an interconnected relationship. This forces economists, statisticians, and data scientists to make a choice between how much of either one they’re willing to tolerate. In short, scientists can increase bias to reduce variance, so in cases where variance is too high (creating an “unstable” estimator) we might introduce bias to settle down the variance. In this case, we can reduce the total error of the regression equation, which improves our analysis. Say that increasing bias by 10% reduces variance by 50%; this will lower the overall error of our analysis, and is a worthwhile trade. Still, in general, bias isn’t something to go looking for. Now you should be well equipped to recognize it! Good to Know Small sample sizes are often an issue in regression analysis. Because of this, every economist or data scientist prefers to have more data rather than less in their dataset. To see this, consider a coin flip. When we flip a coin, we expect it to be heads half the time and tails the other half of the time. But, it’s entirely possible that we flip a coin 10 times and get heads 8 times instead of 5. If we were running a regression and used this experiment as our estimate, we’d get an 80% chance of heads. This seems like a problem — if we didn’t already know beforehand that a fair coin has a 50% chance of landing on heads, how could we tell that this estimate is wrong? And, is this a case of bias? This example — and indeed, all of the examples in this article — highlight that theory is very important. When studying bias and analyzing data, it’s imperative that the data scientist understands what the data actually means. This will help them make the right decisions in their analysis, and help them to make correct takeaways. So, if this coin experiment was run and the data scientist had a solid theory about it — like that coins should land on heads 50% of the time — they could make an informed guess that either they’ve observed a slightly unexpected outcome, or the coin could be weighted. Then, they can figure out how to test these hypotheses and learn more. Further Reading Advanced readers might be interested to know that, shockingly, there exist estimators that are biased but provably better than unbiased options — even if variances are comparable! The James-Stein estimator is one such intentionally biased estimator that improves upon the usual “default” results. The classic method for “solving” a regression equation, Ordinary Least Squares (or OLS), minimizes the squared error terms in the regression. This results in coefficient values that can be unbiased (if the researcher used OLS correctly) and that are as close to the true values as we can get, given the variations in the random data we collected. The James-Stein estimator is built on a concept that shocked statisticians when it was initially introduced in 1961. Put simply: we can bias our own estimates downward in order to reduce the sum of squared error even more than OLS can. Then, since we have less error on average, we are closer to discovering the true population value overall, even though we purposefully biased the estimators downwards to understate the effect size! Estimators that are strictly worse in a total-error sense than other estimators in this way are called “inadmissible”. Thus, statisticians proved OLS is inadmissible. And later, they also proved that the James-Stein estimator itself is also inadmissible! This begs many questions, not least of which is why we’d ever use OLS (or other unbiased estimators) if we can purposefully bias our estimators to get “better” results. Alas, this topic goes much beyond the scope of this article, but the answer lies in the bias-variance tradeoff, the specific research question at hand, and the (size of the) dataset used among other factors. Consider taking courses in statistics, data science, or econometrics to learn more!
Nobody wants to be biased! But rather than suggest someone isn’t a fair judge, “bias” in economics has a very specific — and perhaps equally distasteful — meaning. This econometric concept is borrowed straight from the field of statistics, just like its cousin, variance. Both bias and variance have very important roles to play in our analysis of real-world data via regression analysis, and they’re linked together through the bias-variance tradeoff. Regression analysis is an important tool in the modern economists’ toolkit (as well as any other scientist or data analyst!). With it, we have methods to test economic theories and understand how certain processes or variables affect others. In our article on regression analysis, we touched on several factors that can complicate an analysis and lead to incorrect conclusions. Bias is one such factor.
als wissenschaftliche/n Referentin/Referenten des Vorstands. Neben der Erledigung unterstützender Aufgaben für den Vorstand des RWI in allen Bereichen des Wissenschaftsmanagements besteht die vielseitige Tätigkeit in der wissenschaftlichen Forschung in einem der Forschungsschwerpunkte des Instituts. Von Bewerbern/Bewerberinnen wird erwartet, dass sie neben einem Prädikatsexamen und ersten Erfahrungen in der wissenschaftlichen Forschung über fundierte Kenntnisse in ökonomischer Theorie und angewandter Ökonometrie sowie über die Fähigkeit zum eigenständigen Arbeiten innerhalb eines Teams verfügen. Darüber hinaus setzen wir Belastbarkeit, Flexibilität, Organisationstalent, ausgezeichnete Ausdrucksformen in Wort und Schrift und sehr gute Fremdsprachenkenntnisse (v.a. Englisch) voraus. Ebenso erwarten wir Kenntnisse im Umgang mit gängiger Software (insbesondere statistisch-ökonometrischen Programmen). Erste Ein-blicke in die Organisationsstrukturen einer Forschungseinrichtung und das Wissenschaftsmanagement sind wünschenswert. Die Stelle ist mit der Möglichkeit zur Promotion verbunden, die im Rahmen eines strukturierten Doktorandenprogramms am RWI aktiv unterstützt wird. Die Stelle ist in Vollzeit zu besetzen und ist zunächst voraussichtlich für die Dauer von zwei Jahren befristet, die Befristungsdauer orientiert sich an der angestrebten Qualifizierung. Eine Verlängerung ist möglich und erwünscht. Die Vergütung erfolgt nach dem Tarifvertrag für den öffentlichen Dienst der Länder (TV-L) mit den Sonderregeln für Beschäftigte an Hochschulen und Forschungseinrichtungen entspre- chend der mit der Stelle einhergehenden Verantwortung in der Entgeltgruppe 13. Die Beachtung der Schwerbehindertenrichtlinien und der Vorschriften des Gesetzes über Teilzeitarbeit ist gewährleistet. Wir fördern die Gleichstellung aller Geschlechter. Bitte richten Sie die üblichen Bewerbungsunterlagen, gerne auch per E-Mail, unter Angabe der Kennziffer 70001-06/26 bis zum 31. August 2026 an RWI – Leibniz-Institut für Wirtschaftsforschung Herr Tilo Schneider Postfach 10 30 54, 45030 Essen, personal@rwi-essen.de Nähere Informationen zum Institut unter www.rwi-essen.de Bitte beachten Sie bei der Übersendung Ihrer Bewerbung per E-Mail, dass bei diesem Übermittlungsweg Ihre Daten unverschlüsselt sind und unter Umständen von Unbefugten zur Kenntnis genommen oder auch verfälscht werden könnten. Gerne können Sie uns Ihre Unterlagen per Post zukommen lassen. Essen, 21. Juli 2026
Das RWI – Leibniz-Institut für Wirtschaftsforschung ist ein führendes Zentrum für wirtschaftswissenschaftliche Forschung und evidenz- basierte Politikberatung in Deutschland und wird überwiegend aus Mitteln des Bundes und der Länder finanziert. Das Institut informiert mit seinen Arbeiten über ökonomische Entwicklungen und deren Ursachen, erleichtert Politik und Unternehmen sachgerechte Entschei-dungen und fördert in der Öffentlichkeit das Verständnis für wirtschaftliche Zusammenhänge. Das Institut sucht zum nächstmöglichen Zeitpunkt eine/n
RWI – Leibniz-Institut für Wirtschaftsforschung
Description: Role Purpose: Support economic and financial analysis of utility networks by evaluating CAPEX, OPEX, lifecycle costs, and investment decisions to improve cost efficiency, resilience, and service performance. Key Responsibilities: Data Assessment: Identify data gaps, assess data quality, and recommend improvements for cost analysis. Benchmarking: Analyze CAPEX, OPEX, and maintenance costs; benchmark against international utilities; identify efficiency gaps. Techno-Economic Analysis: Evaluate Security of Supply Standards (SoSS), resilience, and service levels; assess cost vs reliability trade-offs. Value Engineering: Conduct lifecycle cost analysis; identify cost-saving design and material alternatives. Standards & Specifications: Perform cost-benefit analyses for new technologies and standards; support investment decisions. Digital Tools Support: Develop cost models and support investment prioritization tools. Qualifications: Bachelor’s degree in Finance, Accounting, Economics, or related field. Professional certifications preferred (e.g., CFA, CPA, or FMVA). Advanced degree (MBA / MSc Finance) desirable. Minimum 10 years of work experience. Experience in water, energy or infrastructure sectors preferred. Preferred Specifications: CAPEX/OPEX budgeting, forecasting, and variance analysis. Financial modelling and business case evaluation (ROI, NPV). Strong accounting knowledge (cost allocation, capitalization). Advanced Excel and ERP systems (e.g., SAP) proficiency. Data analysis and reporting (insight generation for decision-making). Stakeholder coordination with finance, engineering, and operations teams. Cost-benefit and investment analysis (NPV, IRR). About Stantec: We’re active members of the communities we serve. That’s why at Stantec we always design with community in mind. We believe growing a great design company happens from the inside out. We look for people who are drawn to use every talent they possess, plus creativity, determination and a drive to do the extraordinary. The Stantec community unites approximately 22,000 employees working in over 400 locations across six continents. We collaborate across disciplines and industries to bring projects to life. Our work as architects, engineers, and consultants from initial project concept and planning through design, construction and commissioning is built on a solid history of success. So, when we take on a project, we see the opportunity to make a lasting connection with the people and places where we live and work. Redefine your personal best. Join us! Work Location(s): United Arab Emirates - Abu Dhabi Employment Type: Full-Time Job Type: Regular Job Category: Consulting APPLY NOW Stantec provides equal employment opportunities to all qualified employees and applicants for future and current employment and prohibit discrimination on the grounds of race, color, religion, sex, national origin, age, marital status, genetic information, disability, protected veteran status, sexual orientation, gender identity or gender expression. We prohibit discrimination in decisions concerning recruitment, hiring, referral, promotion, compensation, fringe benefits, job training, terminations or any other condition of employment. Stantec is in compliance with local, state and federal laws and regulations and ensures equitable opportunities in all aspects of employment.
Join Stantec as an Economic Analyst in Abu Dhabi to support economic and financial analysis of utility networks.
Researcher | Pre-/Post-Doc - Applied Microeconomics
The position is available from October 2026 for a period of four years, with the option of extension. A part-time arrangement is possible. Our research and teaching focus on the economic behaviour of firms and individuals in the context of globalisation, migration, and structural change. A central element of our research is the empirical analysis of economic phenomena based on large individual-level datasets. Your responsibilities will include: • teaching duties amounting to 4 hours per week during term time (Semesterwochenstunden) • your own research, comprising at least 50 percent of working time, in pursuit of further academic qualification (e.g. PhD or habilitation), publications • supporting the Chair's research activities as well as contributing to academic self-administration Your profile: • Master degree in economics or a related field • interest in and knowledge of applied microeconomics and international economics • interest in, and very good knowledge of, econometrics and the application of empirical methods • very good programming skills in R and/or Stata • very good command of spoken and written English • initiative, creativity, and strong analytical skills • a keen interest in research Desirable selection criteria • experience and expertise in working with individual-level and/or foreign trade data • experience in developing and implementing independent research ideas We offer: • attractive remuneration with financial stability, regular pay adjustments, and an annual special payment • occupational benefits (e.g. retirement provision through VBL, capital-forming benefits, job ticket) • collectively agreed annual leave plus non-working days on 24 and 31 December • flexible working arrangements and family-friendly working conditions • and much more… Please submit your application, quoting the above reference number, as a single PDF file by 20.09.2026 to: bewerbung@europa-uni.de (application data will be deleted without delay once the selection procedure has been completed). Questions regarding the position may be directed by e-mail to: geishecker@europa-uni.de. Interviews are expected to take place on Frankfurt (Oder) in early October 2026. Applications should be submitted digitally in English and include the following documents: a letter of motivation, a detailed curriculum vitae, academic transcripts and references, a list of publications where applicable, a sample publication (PDF), a research proposal outlining the development of a research project (max. 4 pages), and a proposed outline for one MA and one BA seminar related to the subject area of this position (title and a brief description of approx. 10 lines each). The Europa-Universität Viadrina Foundation is actively committed to diversity and equal opportunity among all its staff and pursues the goal of being a non-discriminatory institution that welcomes and protects people in their diversity within the university community. Further information can be found at: www.europa-uni.de/stellenangebote
Europa-Universität Viadrina is an internationally and interdisciplinarily oriented university located in the twin city of Frankfurt (Oder)-Słubice, on the German and Polish banks of the River Oder, where it educates around 4,000 students from all over the world. The university lives the European idea in its everyday practice and promotes European integration through teaching and research. With around 300 academic and 300 non-academic staff, it forms a community for which successful communication and cooperation with Eastern Europe are a particular concern. The Chair of Economics, in particular Applied Microeconomics at the Faculty of Business Administration and Economics invites applications for the position of Researcher | Pre-/Post-Doc Reference number 1201-26-01 (up to pay grade 13 TV-L, 40 hours/week)
The interdisciplinary B.Sc. Hons programme in Economics and Finance will help students to understand the principles of economics and its applications to financial systems and organisations. The department recommends that students complete mathematics in Classes 11 and 12 to be eligible for any economics-related major or minor. For students who have not studied mathematics in Classes 11 and 12 to be eligible for any economics-related major or minor, they need to: a) Take MAT 1000: Calculus (a 1000-level course) offered by the Mathematics department in their first semester and b) Secure at least an A- to be eligible Note, there may be an entry test requirement for MAT 1000 which will be set by the Maths department. Please check the mathematics department website for more details. In addition to quantitative techniques in mathematics and statistics, the curriculum will also include advanced financial techniques such as derivatives pricing, risk management and portfolio management. Finance Courses A sample of courses constituting the Finance electives is the following. Introduction to Finance Financial Markets and Asset Pricing Finance and the Economy Advanced Financial Management Financial Markets and Institutions International Finance Asset Pricing Derivatives and Risk Management Empirical Monetary Policy and Finance The list of economics and finance electives from recent semesters can be found here. Major Requirements Students admitted to Ashoka in 2022 and thereafter fall within the NEP Programs. Credit requirements for interdisciplinary majors are listed below. Four-year Economics and Finance UG degree (BSc Hons. degree) requires 20 major courses, each of 4 credits. The structure of the 20 courses is the following: 9 compulsory courses in Economics: these include — Introduction to Economics, Mathematics for Economics, Microeconomic Theory I, Macroeconomic Theory I, Statistics for Economics, Microeconomic Theory II, Macroeconomic Theory II, Econometrics, and Development Economics. Any 11 economics or finance electives, of which exactly 4 finance electives. In addition to this, one can do a BSc Honours with Research with an additional Dissertation of 12 credits. The exact breakdown of the year-long sequence will be announced after the university discussions by the end of the academic year 2024-25. Three-year Economics and Finance UG degree (BSc degree) comprises of 15 major courses, each of 4 credits. The structure of the 15 courses will be the following. The 9 compulsory Economics major courses include — Introduction to Economics, Mathematics for Economics, Microeconomic Theory I, Macroeconomic Theory I, Statistics for Economics, Microeconomic Theory II, Macroeconomic Theory II, Econometrics, and Development Economics. Any 6 economics or finance electives, of which at least 3 finance electives. Sample Curriculum Structure (For students admitted in 2022 and later) 4-year Economics and Finance major Semester I Semester II Semester III Semester IV Semester V Semester VI Semester VII Semester VIII Introduction to Critical Thinking Foundation Course 4 Foundation Course 6 Foundation Course 7 Foundation Course 8 Economics Elective 2 Economics Elective 4 Economics Elective 7 Foundation Course 1 Foundation Course 5 Statstics for Economics Econometrics Development Economics Economics Elective 3 Economics Elective 5 Finance Elective 4 Foundation Course 2 Introduction To Economics Microeconomics Theory I Microeconomics Theory II Economics Elective 1 Finance Elective 2 Economics Elective 6 Foundation Course 3 Mathematics for Economists Macroeconomics Theory I Macroeconomics Theory II Finance Elective 1 Finance Elective 3 The above table is only a sample curriculum structure. Blank boxes are for non-major courses. There could be other possibilities, e.g. taking the 7th Foundation Course in 6th semester and taking three Economics electives in the 5th semester. Please refer to the UG Handbook for overall degree requirements. 3-year Economics and Finance major Semester I Semester II Semester III Semester IV Semester V Semester VI Introduction to Critical Thinking Foundation Course 4 Foundation Course 6 Foundation Course 7 Foundation Course 8 Economics Elective 2 Foundation Course 1 Foundation Course 5 Statstics for Economics Econometrics Development Economics Economics Elective 3 Foundation Course 2 Introduction To Economics Microeconomics Theory I Microeconomics Theory II Economics Elective 1 Finance Elective 2 Foundation Course 3 Mathematics for Economists Macroeconomics Theory I Macroeconomics Theory II Finance Elective 1 Finance Elective 3
The interdisciplinary B.Sc. Hons programme in Economics and Finance will help students to understand the principles of economics and its applications to financial systems and organisations.