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.
Econometrics, Statistics and Quantitative Methods (JEL C)
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.
Automating Your Research in Stata: A Little Bit of Programming Goes An Awfully Long Way! - Live Online Training
COURSE DESCRIPTION This course aims to provide participants with the fundamental Stata programming toolkit in order to facilitate, automate, replicate and personalize both data analysis, management and presentation. As such, session 1 reviews some general Stata commands, illustrating how they can be combined with some powerful Stata programming constructs for looping and branching. The course then moves on to focus on how the programming concepts of Macros, Loops, and Branching, can be implemented to effectively write, modify and develop do files (user written Stata programs). In common with TStat’s course philosophy, each session is composed of both a theoretical component (in which the programming techniques are fully explained via a series of course specific developed examples), and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques under the watchful eye of the course tutor. At the end of the course, it is expected that participants are able to personalize existing Stata commands and develop their own do files in order to organize their workload in a more automated, efficient, flexible and reproducible manner. TARGET AUDIENCE Researchers or professionals with previous programming skills in other software wishing to work effectively in Stata. Existing Stata users wishing to acquire the “standard” Stata programming toolkit in order to implement basic programming techniques to effectively automate a substantial part of their empirical data analysis. PREREQUISITES It is expected that individuals wishing to follow this course have a sound working knowledge of Stata. Participants are not however, required to have any programming experience in Stata or in other statistical packages. PROGRAM SESSION I: ORGANISING, MANIPULATING AND VISUALIZING YOUR DATASETS WITHIN A DO-FILE – A REVIEW SESSION II: STATA CONSTRUCTS FOR DO-FILES PROGRAMMING SESSION III: AUTOMATION DO-FILE PROGRAMMING IN PRACTICE – MAKING LIFE EASIER! SESSION IV: AUTOMATING THE TRANSFER OF STATA RESULTS TO EXTERNAL SOFTWARES – STATA’S MATRIX CAPABILITIES COURSE REFERENCES Baum, C.F., (2016). An Introduction to Stata Programming Second Edition, Stata Press Publication. DATE AND LOCATION The 2026 edition of this training course will be offered online on a part-time basis on the 26th of October from 2:30 pm to 6:00 pm and the 27th, 28th of October from 10:00 am to 2:30 pm, Central European Time (CET). REGISTRATION FEE AND REGISTRATION FORM Full-time Student*: € 485.00 Full-time PhD Student: € 635.00 Academic: € 1155.00 Commercial: € 1350.00 *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 16th of October 2026. To request further information or the registration form, please fill in the following form or send an email to training@tstat.eu
This online course aims to provide participants with the fundamental Stata programming toolkit in order to facilitate, automate, replicate and personalize both data analysis, management and presentation. 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 26th of October from 2:30 pm to 6:00 pm and the 27th, 28th of October from 10:00 am to 2:30 pm, Central European Time (CET).
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.
EcoMod ONLINE - Introduction to General Equilibrium Modeling using GAMS
Date: January 1, 2026 - December 31, 2026 EcoMod ONLINE expands the EcoMod School’s offerings to a flexible, accessible learning environment designed for professionals worldwide. Our online programs cover the major economic policy challenges facing governments, central banks, international organizations, and research institutions. Key topics include sustainability, energy, climate change, monetary and fiscal policy, taxation, labor markets, development, pensions, health, social protection, gender, agriculture, tourism, water, and regional economic issues. Through EcoMod ONLINE, participants learn how to design, develop, and apply quantitative economic models for scenario analysis, impact assessment, forecasting, and policy simulations. All courses are asynchronous—allowing you to study at your own pace and from any location—while maintaining the rigor, structure, and practical orientation that define EcoMod training. Instruction is delivered by senior experts with extensive hands-on experience as heads of modeling units and research divisions in major institutions. Course Open - EcoMod ONLINE - Introduction to General Equilibrium Modeling using GAMS This immersive online course enables participants to master Computable General Equilibrium (CGE) modeling using GAMS, following the successful EcoMod framework—an internationally recognized standard in applied economic modeling. Participants will build models from the ground up, developing practical skills in partial and general equilibrium analysis, household behavior, production technologies, macro‐economic policy, government interventions, labor markets, savings-investment dynamics, and international trade. Advantages of EcoMod ONLINE Learn from anywhere, at any time, with full flexibility for professional schedules Progress at your own pace through structured, high-quality modules Access the same level of expert instruction as our in-person schools Apply learning immediately through real datasets, models, and hands-on exercises Join a global network of peers and experts across institutions and regions EcoMod Legacy and Expertise EcoMod ONLINE builds on the EcoMod Network’s 25-year legacy as the global leader in training on CGE, macroeconometric, financial, and environmental modeling. The EcoMod School of Modeling and Data Science is recognized worldwide for its step-by-step, practice-oriented teaching methodology, enabling participants to construct and work with operational models under expert guidance. Our ONLINE courses preserve this proven learning approach through carefully designed modules, practical exercises, and real-world model applications. EcoMod also maintains the largest alumni network in the world in this specialized field, with participants from more than 2,000 institutions in 180 countries. By joining EcoMod ONLINE, you become part of this global community of policymakers, economists, and analysts who rely on EcoMod’s training to support evidence-based decision making. Header Image Credit: Unsplash / Additional Image Credits: Ecomod
Join a Global Community of Modeling Professionals through the EcoMod ONLINE program.
Public Economics & Economic Policy (JEL H)
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)
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.
Economic Development, Innovation and Growth (JEL O)
Agricultural and Environmental Economics (JEL Q)
The Kiel Institute offers external PhD students and professionals the opportunity to participate in advanced courses of its high-level ASP Kiel Ph.D program. The courses are at the frontier of current economic research and are taught by leading economists in their field. Participation in these courses is a great way to be inspired by renown scholars who work at the research frontier and to learn new methods and skills. Over the years, hundreds of external participants have attended our ASP courses, in particular PhD students from all over the world, as well as professionals from international institutions, ministries, central banks, think tanks or the financial sector. All courses are taught in English and at our institute facilities in Kiel. All candidates can apply for participation in one or several courses of the program highlighted below. Colleagues and students from the Kiel Institute and partner Institutions can express their interest in participating to an advanced course by sending an email to advancedstudies@ifw-kiel.de. All other candidates need to fill in the form available on the specific course webpage. If you are a Ph.D student/candidate, you are entitled to a substantially reduced course fee and you can apply for low cost accommodation at the Kiel Institute´s student residence. Do not hesitate to contact us if you need any additional information about forthcoming courses or which options might best fit your needs. Dates Course Title Teacher Location 03 - 09 Sep 2026 Math Camp Kiel 14-18 Sep 2026 Computational Methods and AI for Research Friedrich Geiecke Kiel 22 - 25 Sep 2026 Real Business Cycles and Quantitative Methods Sergio de Ferra Kiel 05-09 Oct 2026 Growth and Search and Matching Federica Romei Kiel 09-20 Nov 2026 Empirical International Finance Christoph Trebesch Kiel 07-11 Dec 2026 New Keynesian Models Andrea Ferrero Kiel Fees and logistics: The fee for individual courses is 1,800 Euros. Doctoral students can get a discount of EUR 900. Kiel doctoral students can join or advanced courses as part of their PhD curriculum. We can offer subsidized housing for selected few external participants at our “Haus Weltclub”, with preference given to students and a price per stay between EUR 200 and EUR 230 for a nice and clean 11-13sqm room with shared but lockable bathroom. For more courses, outlines and application forms visit our webpage.
The Kiel Institute offers external PhD students and professionals the opportunity to participate in advanced courses of its high-level ASP Kiel Ph.D program
Kiel Institute for the World Economy (IFW)
Economic Development, Innovation, Technological Change, and Growth
Agricultural and Natural Resource Economics - Environmental and Ecological Economics
International Doctoral Courses and Seminars in Health Economics and Policy
The objectives The International doctoral Courses and Seminars in Health Economics and Policy are intended for PhD students from Swiss universities as well as foreign universities who wish to enhance their specific knowledge and sharpen their capabilities in the field of health economics and policy. Researchers with a PhD in economics and related disciplines as well as health researchers with a significant interest in economics who wish to deepen their knowledge of some topics can also be admitted Certification Students receive a certificate for each course that they attend Each completed course is awarded 3 ECTS Students who pass an exam receive a certificate of success and a grade report A certificate for having achieved the PhD specialisation in Health Economics will be awarded for having achieved 15 credits (5 courses). The approval will be signed by the program director More information Please, send us an email if you need more information: phd@sggoe.ch Overview of Courses: 8 - 12 Jun 2026 Lucerne Machine Learning in Health Economics Prof. Helmut Farbmacher apply 22 - 26 Jun 2026 Lucerne Empirical Policy Evaluation in Health Prof. Maarten Lindeboom, Prof. Bas van der Klaauw apply 24 - 28 Aug 2026 Basel Experimental Health Economics Prof. Jeannette Brosig-Koch apply Application Requirements: Graduate degree in related discipline Personal data/curriculum vitae Enrollment Form Doctoral Program Entry Requirements Applicants of the International Doctoral Courses and Seminars in Health Economics and Policy need to: be registered as candidates of a PhD in economics or in a related discipline demonstrate sufficient proficiency in microeconomics, and demonstrate sufficient proficiency in econometrics or statistics. A full resume, along with the enrollment form will have to be submitted to the administration of the program. A final decision with regards to admission will be taken after a thorough and individual assessment of the application. Students can apply via http://www.sggoe.ch
The program The goal of the program is to offer advanced and highly specialized courses in health economics and policy, as well as seminars and workshops with leading international experts. This program is being offered within the framework of the Swiss School of Public Health (SSPH+) and the various partners of SSPH+ (Basel, Lausanne, Lucerne, Lugano and Zurich) will offer specialized courses for the participants.
Swiss Society of Health Economics (sggö)
Health Inequality Measurement: Concepts, Methods and Applications (3 days)
This course is taught by three leading researchers in measuring health inequalities and inequities in health care. This is a 3-day course run by Melbourne Health Economics within the Centre for Health Policy at the University of Melbourne. It aims to provide participants with a solid grounding in the theoretical and empirical dimensions of health inequality measurement. By the end of the course, participants will be able to: -Understand and apply core concepts in health inequality measurement, including relative and absolute measures, and rank- and level-dependent approaches -Measure and interpret health poverty using established methodological frameworks -Design and conduct health inequality analyses in practice, including defining research questions, sourcing data, and addressing measurement challenges related to socio-economic status -Distinguish between avoidable and unavoidable health inequalities and apply advanced measurement techniques to real-world evidence -Critically evaluate and report health inequality studies in line with current empirical standards Who is this course designed for? This course is designed for those who are working in or alongside the health sector, including researchers, policy analysts, public health professionals, health economists, and postgraduate students, who wish to deepen their understanding of health inequality measurement. It is particularly suited to those looking to build practical skills in applying theoretical frameworks to real-world data and research contexts. Course Outline The course will involve a series of two modules that build on each other to provide an overview of the steps required for understanding, measuring, and analysing inequality in health and health care. Module 1: Understanding Health Inequality: Concepts, Measurement and Health Poverty An introduction to the core concepts underpinning health inequality measurement, covering relative and absolute measures, rank- and level-dependent inequality indices, methods for measuring health poverty, and decomposition approaches to understanding the drivers of health inequalities. Module 2: From Theory to Practice: Measuring Health Inequality in Real-World Settings A practical exploration of health inequality analysis, including types of inequality comparisons across cross-sections and over time, defining research questions and identifying appropriate data sources, and addressing common challenges in estimating and reporting health inequality studies. Module 3: Fairness, Equity and the Boundaries of Avoidable Health Inequality An advanced module examining the distinction between avoidable and unavoidable health inequality, with a focus on theoretical considerations, measurement frameworks, fairness and socio-economic inequality in health, and the application of these concepts to real-world empirical examples.
Delivered online across three days, this course covers the theory and practice of health inequality measurement, exploring relative and absolute measures and health poverty.
To combat climate change, many countries have committed to achieving net-zero emissions by 2050. The successful transition towards a zero-emission economy will depend on the ability to power energy-related processes with electricity from carbon-free renewable energy sources. This workshop will present state-of-the-art computable general equilibrium (CGE) models that are widely used in applied economic research to study the impacts of emissions reductions policies. The standard top down framework of CGE models will be complemented by a bottom-up representation of the electricity system to capture the pivotal role of carbon-free supply and demand technologies for decarbonizing the economy as a whole. The course will build on seminal peer-reviewed publications combining methodological expertise on hybrid bottom-up/top-down modeling with policy-relevant applications, ranging from technology-specific regulations (e.g. green subsidies, renewable portfolio standards, energy efficiency standards, technology bans) to economy-wide emissions pricing and revenue-rebating schemes (green tax reforms). Guided by a series of lectures and hands-on modeling exercises, participants will develop step by step the capacity to re-formulate model codes towards their own needs in academic research or policy consultancy. Mathematically, the models are formulated and implemented as so-called “mixed complementarity problems” (MCP), stating complementarity of economic equilibrium conditions with associated economic decision variables. The fundamental strength of the MCP format (beyond the standard approach of treating equilibrium conditions as a system of nonlinear equations) is the ability to handle corner solutions and regime shifts that are central to the analysis of sorting decisions, such as discrete technology choices. For the computer implementation of the numerical models the course uses the high-level programming language GAMS (Generalized Algebraic Modeling System) whose notation closely follows standard matrix algebra. The fundamental strength of GAMS lies in the ease with which mathematically defined models can be formulated and solved. The explicit algebraic formulation of general equilibrium conditions and the parameterization of functional forms to characterize technologies and preferences can become very tedious and error-prone, in particular for more complex production and consumption patterns. MPSGE (Mathematical Programming System for General Equilibrium) – which runs as a subsystem under GAMS – provides a short-hand non-algebraic representation for general equilibrium models, releasing economists from the need to write down complicated equilibrium conditions explicitly as well as from the need to set up tedious calibration routines for the parameterization of demand and supply functions. The workshop will show in detail how to transform algebraic CGE models into non-algebraic MPSGE syntax, which can substantially lower the entry barriers and time cost of CGE analysis. Material and teaching is in English. Explore our website for more details! Content: Part 1: Economic Equilibrium, Mixed Complementarity and Mathematical Programming Economic equilibrium in mixed complementarity format Mathematical programs: duality and complementarity Part 2: Bottom-up activity analysis of the electricity system Stylized models of electricity supply and emissions regulations Optimal electricity dispatch under CO2 emissions pricing Part 3: Integration of bottom-up electricity market models into top-down CGE models Standard CGE representation of an energy-economy system Model calibration to observed economic data (functional forms) A hybrid bottom-up/top-down model for climate policy analysis Part 4: MPSGE implementation of CGE models MPSGE syntax Converting CGE models from algebraic MCP formulations into MPSGE From static to dynamic policy analysis Part 5: CGE-based impact assessment of net-zero emissions policies Multi-sector multi-region (baseline) projections until 2050 Build your own large-scale bottom-up/top-down CGE model calibrated to baseline data CGE analysis: the role of carbon backstop and negative emissions technologies for reaching net-zero emissions Instructors: Christoph Böhringer (University of Oldenburg, Germany) Volker Clausen (University of Duisburg-Essen, Germany) Payment: The fee for participating in the training workshop is 2,500 Euro and includes lectures, course material and lunches. Academic participants from accredited universities or research institutions will be admitted on a space-available basis for a discount of 20%. Graduate students from accredited academic institutions are likewise admitted on a space-available basis for a discount of 40%. Please fax or email a copy of your student ID to get the discount. There will be a limited number of scholarships (excluding travel and subsistence expenses) that have been set aside for qualified participants from developing countries. Deadline for the application for a scholarship is June 12, 2026. Preference will be given to applicants who have documented previous experience in general equilibrium modelling with GAMS. To apply for a scholarship in the form of a tuition waiver, send your CV and a research paper via email to the course coordinator Mehmet Burak Akren. A decision on the allocation of scholarships will be made until June 19, 2026, in order to allow for an early arrangement of flights, visa etc. Registration: The registration deadline is September 11, 2026. The maximum number of participants is restricted to 16! Slots are guaranteed only upon full payment of fees through the course coordinator. Cancellations will be fully refunded if made prior to September 11, 2026. No refunds will be made after the registration deadline. For registration or any inquiries, please contact the course coordinator: M. A. Mehmet Burak Akren, email: Mehmet.Akren@vwl.uni-due.de . University of Duisburg-Essen, Department of Economics, 45117 Essen, Germany
20th Ruhr Graduate Summer School: Economic Analysis of Net-Zero Emissions Policies Using GAMS and MPSGE
Ruhr Graduate School in Economics (RGS Econ)
AI Methods in Economics and Finance Research
Learning Objective Understand the fundamentals of machine learning models and large language models Design and implement a basic machine learning pipeline, including data preparation, model training, and evaluation Apply large language models in practical research settings Target Audience This course is designed for researchers with a traditional background in economics and finance who wish to become acquainted with emerging AI tools. Rather than focusing on cutting-edge implementations, the course aims to provide a clear sense of what can be achieved with these techniques, along with initial hands-on practical experience. Dates Course Dates: 25/05/2026 - 27/05/2026 Registration deadline: 06/05/2026 Location EUI Premises in Florence, Italy Level Intermediate, Advanced Approach Quantitative Contact Institution: Florence School of Banking and Finance Address: Villa Schifanoia, Via Giovanni Boccaccio, 121, 50133 Firenze (FI) City: Florence Country: Italy Country Code: IT Fees FULL FEES*: € 950 – Academics € 1900 – Public sector € 2100 – Private sector *Please note that the payment must be settled one week before the start of the course. The fee includes tuition, access to all course materials and pedagogic activities, coffee and lunch breaks and social activities, as well as a certificate of attendance after the course has been completed. The fee does not include travel and accommodation expenses or other local transportation costs (taxis, private cars). Please submit a certificate attesting your status of Professor, PhD Student or Research Associate to fbf@eui.eu before registering. FBF secretariat will provide you with a code to register. Seats for academics are limited. GROUP DISCOUNT (-15%)
General description Research methods have evolved significantly over the past decade, first with the rise of machine learning (ML) approaches and more recently through the use of generative AI tools, such as those based on large language models (LLMs). This course begins with a whirlwind tour of “traditional” AI before progressing to the practical implementation of state-of-the-art LLM workflows.
DAISY Summer School 2026: Data Analytics for Innovation and Sustainability
WHEN AND WHERE September 7-11, 2026 Taranto, Italy APPLY HERE OVERVIEW Recent advances in data availability and analytical techniques have profoundly transformed empirical research in economics. At the same time, innovation and sustainability have become central topics in both academic and policy debates. DAISY responds to these developments by offering a coherent training program that explicitly connects data sources, empirical methods, and applications in recent academic papers. A distinctive feature of DAISY is its strong orientation toward applications and academic papers. Lectures and hands-on sessions are closely linked to recent contributions in leading journals, allowing participants to engage directly with real research examples and empirical strategies. The program combines lectures, practical sessions, and interactive discussions in an international and collaborative learning environment. DAISY is an international summer school designed to train PhD students and early-career researchers in the data, methods, and empirical applications to study innovation and sustainability in Economics. SPEAKERS Gaétan de Rassenfosse (EPFL) Marco Vivarelli (Università Cattolica) Francesco Quatraro (University of Turin) Valeria Costantini (University of Roma Tre) Elisa Giuliani (University of Pisa) Sergio Petralia (Utrecht University) Fabrizio Fusillo (University of Turin) Claudia Ghisetti (University of Milan Bicocca) Gianluca Orsatti (University of Turin) Alessandra Scandura (University of Turin) Martina Iori (Università Cattolica) Alessandra Catozzella (Boston Consulting Group) Massimiliano Coda Zabetta (University of Turin) THE VENUE The venue of the Summer School is the Farm House Tenuta del Barco. Tenuta del Barco Wine Restaurant and Hospitality is located in the heart of Puglia, in a 16th-century farmhouse with a historic courtyard, just steps from the beautiful beaches of the Ionian Sea and surrounded by Primitivo and Negroamaro DOC vineyards. APPLICATION PACKAGE PhD students, postdocs, and young researchers willing to attend the summer school can apply by submitting: A short CV A motivation letter for participating in the summer school A research proposal (maximum 5 pages) or a full paper to present during the student presentation sessions READY TO APPLY? CLICK HERE KEY DEADLINES Deadline for application: 15 June, 2026 Notification of acceptance: 30 June, 2026 Registration and accommodation fee payment: 13 July, 2026 PARTICIPATION FEE Summer school fees for participants (650 euros) include the registration to the summer school (access to all summer school lectures and presentations, 9 breaks, 5 lunches, 2 summer school dinners), and hotel accommodation at the Tenuta del Barco (twin rooms accommodation, i.e., 2 participants per room, with breakfast included, 4 nights, arrival on Monday, departure on Friday).
An international summer school designed for PhD students and early-career researchers focused on data analytics in innovation and sustainability.
Econometrics with EViews: Time Series, and Panel Data
Econometrics with EViews: Time Series, and Panel Data Created by Dr. Nasir Munir What you'll learn Understanding the concepts of data. Explaining the properties of the OLS. Estimating the different econometric tools used in time series data. Determining the basic economics models and interpreting them. This course includes: 13 hours on-demand video Assignments Access on mobile and TV Certificate of completion Course content 16 sections • 88 lectures • 13h 8m total length Description In this course, you will learn how to collect, import, and structure data (World Bank, IMF, WDI, Panel Data), understand types of data and preparing datasets for analysis, and more. Who Should Enroll? Undergraduate & Graduate Students in Economics, Finance, and Business PhD Scholars & Researchers preparing econometric models for publications Policy Analysts & Data Professionals applying econometrics in real-world decision-making By the End of This Course, you will be able to confidently analyze time series and panel data, detect econometric issues, apply advanced models, and present results like a professional researcher. Course Pricing Current price: €19.99 30-Day Money-Back Guarantee Full Lifetime Access Instructor Dr. Nasir Munir is an Assistant Professor at SZABIST University.
Master Econometrics from Basics to Advanced: OLS, ARDL, VAR, GARCH, Cointegration, Causality & Panel Data Analysis.
Microeconomics: The Power of Markets (Online Course)
Instructor: Rebecca Stein 5 modules Gain insight into a topic and learn the fundamentals. 4.7 (1,638 reviews) 2 weeks to complete at 10 hours a week Flexible schedule; Learn at your own pace 95% Most learners liked this course Skills you'll gain: Resource Allocation, Economics, Market Analysis, Policy Analysis, Supply And Demand, Market Dynamics, Decision Making, Tax, Consumer Behaviour, Cost Benefit Analysis Details to know Shareable certificate Add to your LinkedIn profile Assessments 18 assignments Taught in English 22 languages available Course Modules: The Concept of Scarcity: 2 hours to complete Specialization & Trade: 1 hour to complete Supply and Demand: 3 hours to complete Understanding Markets: Elasticities, Market Surplus, Efficiency, and Equity: 3 hours to complete When Government Intervenes: 3 hours to complete This course is an introduction to the microeconomic theory of markets: why we have them, how they work, what they accomplish. We will start with the concept of scarcity and how specialization according to comparative advantage helps us achieve more than we could alone. Next, we model a market using the tools of Supply and Demand and learn what well-working markets accomplish and what their limits are. We end by exploring the impact of government intervention on perfect markets. Examples are taken from everyday life, from goods and services that we all purchase and use. We will apply the theory to current events and policy debates through weekly exercises, empowering you to be an educated, critical thinker who can understand, analyze, and evaluate market outcomes. Instructor Instructor ratings: 4.8 (313 ratings) Rebecca Stein, University of Pennsylvania 3 Courses • 117,222 learners Offered by University of Pennsylvania Recommended Microeconomics: Foundations and Insights Microeconomics: When Markets Fail Course rating 1,638 reviews 5 stars: 79.42% 4 stars: 17.15% 3 stars: 2.62% 2 stars: 0.42% 1 star: 0.36%
Instructor: Rebecca Stein. Enroll for free.
Summer School on Bayesian Methods for Experimental Economics
Applications for the two events are open! (deadline Feb 20) Summer School: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsummerschoolbmee.netlify.app%2F&data=05%7C02%7COumaima.DALI%40um6p.ma%7C418b3664cb4d4fdbd46508de63fa422b%7C39626157a047468987a26fa645cb5cb7%7C1%7C0%7C639058123662241403%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=UIbEsrPzlVFrWPHtdT4tqvixNyOo48VRSTIC90tYQVU%3D&reserved=0" target="_blank">https://summerschoolbmee.netlify.app/ ESA Africa : <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fairess.fgses-um6p.ma%2FESA-Africa2026&data=05%7C02%7COumaima.DALI%40um6p.ma%7C418b3664cb4d4fdbd46508de63fa422b%7C39626157a047468987a26fa645cb5cb7%7C1%7C0%7C639058123662269984%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=vKI11oX5felKrG2sQe2QxoHRcASQ70V9%2BvYh1L2XkNA%3D&reserved=0" target="_blank">https://airess.fgses-um6p.ma/ESA-Africa2026 The four-day summer school focuses on Bayesian methods in experimental economics—decision models, heterogeneity, structural estimation, RCT analysis, and meta-analysis. Hosted on UM6P’s lovely campus with great amenities (pool, courts, gym): a perfect setting to learn and connect. Confirmed teachers include: Thomas Epper, CNRS, on structural estimations Taisuke Imai, University of Osaka, on meta analyses Fabio Maccheroni, University of Bocconi, on decision science Rafe Meager, University of New South Wales, on Bayesian RCT The summer school is sponsored by ESA and organized in partnership with the Paris School of Economics, and discounted fees apply for students participating in both the UM6P and PSE summer schools. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.parisschoolofeconomics.eu%2Fen%2Fsummer-school%2Fexperimental-economics%2F&data=05%7C02%7COumaima.DALI%40um6p.ma%7C418b3664cb4d4fdbd46508de63fa422b%7C39626157a047468987a26fa645cb5cb7%7C1%7C0%7C639058123662288797%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=j4DyPAkcoyMLykSS0WuOZ6rxFf5nnB7l6rh2gcRrOUE%3D&reserved=0" target="_blank">https://www.parisschoolofeconomics.eu/en/summer-school/experimental-economics/ If you think these summer schools could be relevant to students or colleagues in your network, please feel free to share this announcement. We will be delighted to welcome you on the beautiful campus of UM6P Rabat in June!
The summer school “Bayesian Methods for Experimental Economics” will be held at University Mohammed VI Polytechnic (UM6P), Rabat, on June 18–21, 2026, immediately following ESA Africa (June 15–17) at the same venue.
Summer School on Modern Topics in Time Series Analysis 2026
5th Klagenfurt-Bielefeld Summer School on Modern Topics in Time Series Analysis Main Facts Time: September 7-11, 2026 Venue: University of Klagenfurt, Austria Capacity: 40 participants Fee: EUR 550 (PhD students); EUR 700 (others) Target Audience The summer school’s target audience are primarily PhD students. However, the school also welcomes young post-doctoral researchers (from universities, research institutes or (central) banks) and professionals with a strong formal background seeking to extend their knowledge base in time series analysis. A good preliminary knowledge of the main work-horses of time series analysis, e.g., estimation and specification of vector autoregressive models, is expected. Important Dates May 15, 2026: Deadline for Application June 1, 2026: Notification of Acceptance June 15, 2026: Deadline for Registration Cancellation Policy Until August 2, 2026: free of charge | up to August 23, 2026: 50% of the fee will be retained | after August 23, 2026: no reimbursement possible. Programme / Schedule The summer school provides a PhD-level introduction to a range of modern topics in time series analysis taught by a selection of top researchers in Europe: Introduction and Overview: Dietmar Bauer (Bielefeld University) and Martin Wagner (University of Klagenfurt) Econometric Modelling of Climate Change: Eric Hillebrand (Aarhus University) Bootstrap: Jens-Peter Kreiß (TU Braunschweig) Structural VAR: Martin Bruns (University of East Anglia) Nonlinear VAR: James Duffy (Oxford University) Bayesian Methods for (Macro and Financial) Time Series Analysis: Gregor Kastner (University of Klagenfurt) Tensor and Network Econometrics: Roberto Casarin (Ca’ Foscari University of Venice) Hidden Markov Models: Roland Langrock (Bielefeld University) Each half-day will be dedicated to one topic in two 90-minute lectures. Presentations will roughly follow a common layout introducing the main theory of the area, potentially providing examples and discussing software available and finally indicating relevant questions at the research frontier. Schedule The summer school will start on Monday, September 7, 2026 at 9:00 (registration at 8:30) and will close on Friday, September 11, 2026 in the early afternoon. Details will be communicated directly to the participants. Application PhD students provide: - A motivation letter including the subject of their PhD thesis and name of their supervisor - An up-to-date CV listing all time series and statistics-related courses taken so far - Proof of enrolment in PhD studies Others: - A motivation letter - An up-to-date CV listing all time series analysis-related experience Please pack all documents into a single PDF file (size limit 5MB). Applications are to be sent to QED [at] aau [dot] at. Practical Information The conference fee covers social events, accommodation and transport. Some impressions from MTSA 2022 and 2024 include positive feedback about the balance between lectures and breaks, networking opportunities, and impactful presentations by instructors. Contact Information University of Klagenfurt Universitätsstraße 65-67 9020 Klagenfurt am Wörthersee, Austria +43 463 2700 4102 www.aau.at
The Summer School on Modern Topics in Time Series Analysis is held at the University of Klagenfurt from September 7-11, 2026. This program targets PhD students and offers many insights from leading researchers on time series analysis. Enjoy the engaging athmosphere, learn and mingle with fellow PhD students. Connect to your peers as well as the experts providing introductions to modern topics in time series analysis.
Python and Statistics for Financial Analysis
Instructor: Xuhu Wan Course Details 4 modules Flexibility: 1 week at 10 hours a week, learn at your own pace Intermediate level Taught in English, available in 29 languages (AI Translation) Skills You'll Gain Data Visualization Financial Trading Statistical Analysis Data Import/Export Statistical Inference Regression Analysis Financial Data Financial Modeling Jupyter Model Evaluation Course Overview Python is now becoming the number 1 programming language for data science, especially in the financial industry. This course combines both Python coding and statistical concepts and applies them to analyze financial data, such as stock data. Objectives By the end of the course, you can achieve the following using Python: Import, pre-process, save, and visualize financial data into pandas Dataframe. Manipulate financial data by generating new variables using multiple columns. Apply key statistical concepts into financial contexts. Build and evaluate a trading model using linear regression. Course Structure Module 1: Visualizing and Munging Stock Data - 3 hours to complete Module 2: Random Variables and Distribution - 2 hours to complete Module 3: Sampling and Inference - 3 hours to complete Module 4: Linear Regression Models for Financial Analysis - 4 hours to complete Instructor Xuhu Wan from The Hong Kong University of Science and Technology. Additional Information There are 5 assignments included in this course. Enroll now for free!
Enroll in this course to gain essential skills in Python and statistics for financial analysis.
Hong Kong University of Science and Technology
Course offered by: Stanford University In the first week we consider the problem of aggregating different agents' preferences, discussing voting rules and the challenges faced in collective decision making. We present some of the most important theoretical results in the area: notably, Arrow's Theorem, which proves that there is no "perfect" voting system, and also the Gibbard-Satterthwaite and Muller-Satterthwaite Theorems. We move on to consider the problem of making collective decisions when agents are self interested and can strategically misreport their preferences. We explain "mechanism design" -- a broad framework for designing interactions between self-interested agents -- and give some key theoretical results. Our third week focuses on the problem of designing mechanisms to maximize aggregate happiness across agents, and presents the powerful family of Vickrey-Clarke-Groves mechanisms. The course wraps up with a fourth week that considers the problem of allocating scarce resources among self-interested agents, and that provides an introduction to auction theory.
Popularized by movies such as "A Beautiful Mind", game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Over four weeks of lectures, this advanced course considers how to design interactions between agents in order to achieve good social outcomes. Three main topics are covered: social choice theory (i.e., collective decision making and voting systems), mechanism design, and auctions.