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.
Econometrics, Statistics and Quantitative Methods (JEL C)
Urban, Rural and Transportation Economics (JEL R)
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.
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.
Public Economics & Economic Policy (JEL H)
Economic Development, Innovation and Growth (JEL O)
Macroeconomics and Monetary Economics (JEL E)
Agricultural and Environmental Economics (JEL Q)
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.
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.
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.
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)
The global credit crunch of 2007-2008 has had significant implications for the lives of many people around the world. This short course aims to provide students with an understanding of conventional macroeconomic thinking so that they can understand the essential principles of how the macroeconomy of nation states functions in a globalised world. Once students have completed this course they will be better placed to understand the causes of inflation and unemployment and the relevance of government monetary and fiscal policy in dealing with such economic problems. Therefore, students will be able to use the knowledge gained to understand everyday macroeconomic events. Furthermore, students will have a solid foundation upon which they can progress to higher level studies in Macroeconomics as well as being able to apply the knowledge gained to other online courses such as ‘Globalisation'and the ‘New Economic Powers’. For information on how the courses work, and a link to our course demonstration site, please click here. Recommended reading To participate in this course you will need to have regular access to the Internet and you will need to buy the following book: Goodwin, N., Nelson, J.A., and Harris, J., Macroeconomics in Context 2nd edition (M.E. Sharpe, 2014) Certification To earn credit (CATS points) for your course you will need to register and pay an additional £10 fee for each course you enrol on. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online. If you do not register when you enrol, you have up until the course start date to register and pay the £10 fee. For more information on CATS point please click on the link below: http://www.conted.ox.ac.uk/studentsupport/faq/cats.php Coursework is an integral part of all online courses and everyone enrolled will be expected to do coursework, but only those who have registered for credit will be awarded CATS points for completing work at the required standard. If you are enrolled on the Certificate of Higher Education you need to indicate this on the enrolment form but there is no additional registration fee. Assignments are not graded but are marked either pass or fail. All students who successfully complete this course, whether registered for credit or not, are eligible for a Certificate of Completion. Completion consists of submitting both course assignments and actively participating in the course forums. Certificates will be available, online, for those who qualify after the course finishes.
his course provides a detailed introduction to, and encompasses the fundamentals of macroeconomics. On completion of the course you will have the ability to critically assess real-world macroeconomic developments at first-year undergraduate level. Moreover, students will be able to better understand the material in other online economics courses such as the ‘New Economic Powers'and ‘Globalisation’.
Non-Linear Panel Data Models in Stata - Live Online Training Course
COURSE DESCRIPTION Panel data analysis is increasingly used in econometrics, financial analysis, public health, political and social sciences, since it enable researchers to allow for different types of latent heterogeneity between observations. Allowing researchers for example, to control for variables that cannot be directly observed or measured, such as cultural factors or differences in business practices across companies. The use of Panel Data in empirical studies also tends to result in a considerable improvement in the accuracy of the obtained estimates, since panel data sets usually contain more sample variability and higher degrees of freedom. Finally, panel data allows for the estimation of dynamic models, with causal relationships which may not arise instantaneously, but over a period of time. This course follows on from our Linear Panel Data Models in Stata course to offer the necessary theoretical background and the applied skills to enable participants to: i) independently employ non-linear micro panel data techniques to their own research topics, and ii) to understand and evaluate micro panel data analyses published in the academic literature. The focus is therefore on non-linear estimation techniques (more specifically Poisson, Probit, Logit and Tobit panel data models) and issues of sample selection and attrition. In common with TStat’s training philosophy, each individual session is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained), and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the course, theoretical sessions are reinforced by case study examples, in which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. The intuition behind the choice and implementation of a specific technique is of the utmost importance. In this manner, the course leader is able to bridge the “often difficult” gap between abstract theoretical methodologies, and the practical issues one encounters when dealing with real data. At the end of the course, participants are expected to be able to autonomously implement the theories and methodologies discussed during the course. TARGET AUDIENCE TStat Training’s Non-Linear Panel Data training course is of particular interest to Master and Ph.D. Students, researchers in public and private research centres and professionals working in the following fields: Agricultural Economics, Economics, Finance, Management, Public Health, and the Political and Social Sciences seeking to acquire the “introductory” applied and theoretical toolset to enable them to undertake independent empirical research using panel data. PREREQUISITES Participants are required to have a working knowledge of: the classical OLS regression model: Model Assumptions, Estimation and Inference; the arguments covered in TStat’s Linear Panel Data in Stata; the arguments illustrated in TStat’s course Analysis Micro Data in Stata; the statistical software Stata: including familiarity with Stata variable creation commands and Stata do files. Those needing to refresh these concepts are referred to the reading lists on the respective course pages and to: Cameron, A. C. & Trivedi, P. K. (2022). Microeconometrics Using Stata, Volume I: Cross-Sectional and Panel Regression Methods. Second Edition. Stata Press. Chapters: 1-9 PROGRAM The incidental parameter problem in non-linear models Poisson panel data models: poisson, xtpoisson - Random effects - Correlated effects (conditional poisson) Probit panel data models: probit, xtprobit, oprobit, xtoprobit - Random-effect models - Correlated effects modelled as group means (a la Mundlak) Logit panel data models: logit, xtlogit, ologit, xtologit - Random effects - Correlated effects (conditional logit) Tobit and interval regression models: tobit, xttobit, intreg, xintreg - Random effects - Correlated effects (modelled as group means (a la Mundlak) Postestimation analysis: - Average marginal effects: margins - Goodness-of-fit measures: predict SUGGESTED READINGS Cameron, A. C., & Trivedi, P. K. (2022). Microeconometrics Using Stata, Second Edition Volume I: Cross-Sectional and Panel Regression Methods, Stata Press Cameron, A. C., & Trivedi, P. K. (2022). Microeconometrics Using Stata, Second Edition Volume II: Nonlinear Models and Casual Inference Methods, Stata Press Woodridge, J. (2010). Econometric Analysis of Cross Section and Panel Data, MIT Press DATE AND LOCATION The 2026 edition of this training course will be offered online on a part-time basis on the 22nd, 26th-27th of May from 9:30 am to 1:00 pm CEST. REGISTRATION FEE AND REGISTRATION FORM Full-Time Students*: € 585.00 Ph.D. Students: € 750.00 Academic: € 830.00 Commercial: € 1100.00 *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 12th of May 2026. To request further information or the registration form, please fill in the form on the TStat website, or send an email to training@tstat.eu
This course follows on from our Linear Panel Data Models in Stata course to offer the necessary theoretical background and the applied skills to enable participants to: i) independently employ non-linear micro panel data techniques to their own research topics, and ii) to understand and evaluate micro panel data analyses published in the academic literature. 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 22nd, 26th-27th of May.
BSE Competition Economics Courses - Executive Education
The 2025-2026 Academic Year is here! Explore the courses we have this year and subscribe to receive the latest news! Competition in Digital Markets | October 20-23, 2025 | CLOSED Explore the challenges of competition in fast-moving digital markets, including platforms, market power, and regulation. Quantifying Damages in Competition Cases | November 26-28, 2025 | CLOSED Learn methodologies to estimate and quantify damages in antitrust and competition cases, with practical applications. Competition Economics | March 9-13, 2026 | CLOSED Explore the latest competition economics, review key cases, and engage in debates on antitrust and regulation. Quantitative Methods for Competition Analysis | May 18-22, 2026 | Open for applications Master cutting-edge quantitative methods to analyze competition, market power, and anticompetitive practices. Discover our complete course lineup on our website, and check if your desired course is scheduled for the upcoming period. Why choose BSE Competition Economics Courses? Learn from the top minds in the field, including Prof. Massimo Motta and faculty with years of academic and regulatory experience. Master practical tools and techniques used by competition authorities, consultancies, and legal experts worldwide. Apply advanced methods and real case studies in areas like digital market regulation, merger analysis, and damage quantification. Designed for immediate impact—put your new skills to work as soon as you return to your organization. Flexible delivery (online or in-person), while maintaining a rich, engaging learning experience. Courses are short and intensive, perfect for busy professionals seeking high-value learning in a compact format. "The courses we offer at the Barcelona School of Economics are the result of careful design informed by leading academics, senior professionals, and years of experience and interactions with participants. The knowledge you’ll take away includes the latest research, tools, and cases, and you will use it in your day-to-day practice. I don’t think you’ll find any other course out there that can beat this combination." - Massimo Motta Who Takes These Courses? Competition and antitrust authorities from around the globe Leading consulting firms, including Copenhagen Economics, E. CA Economics, RBB Economics, and Deloitte International organizations like OECD and World Bank Top-tier companies and institutions BSE: Academic Excellence with Real-World Impact Courses are designed and delivered by members of a research community ranked among the Top 5 in Europe and Top 12 globally (RePEc, March 2025). Trusted by global organizations to train teams with the latest in competition economics. Build the skills, tools, and insights you need to address today’s most pressing competition and regulation challenges.
Advance your expertise with the BSE Executive Courses in Competition, Markets, and Regulation—a series of short, intensive programs designed for professionals and economists working in competition policy, regulation, and related fields. Delivered in-person or online, these courses combine academic rigor with practical insight to provide skills you can apply immediately. Led by Prof. Massimo Motta, former Chief Competition Economist at the European Commission, and supported by a world-class faculty of top international scholars and practitioners, each course is structured around real case work, interactive sessions, and cutting-edge research.
Linear Panel Data Models in Stata - Live Online Training Course
COURSE DESCRIPTION Panel data analysis is increasingly used in econometrics, financial analysis, public health, political and social sciences, since it enable researchers to allow for different types of latent heterogeneity between observations. Allowing researchers for example, to control for variables that cannot be directly observed or measured, such as cultural factors or differences in business practices across companies. The use of Panel Data in empirical studies also tends to result in a considerable improvement in the accuracy of the obtained estimates, since panel data sets usually contain more sample variability and higher degrees of freedom. Finally, panel data allows for the estimation of dynamic models, with causal relationships which may not arise instantaneously, but over a period of time. This introductory course offers participants the opportunity to acquire the necessary theoretical background and the applied skills to enable them to: i) independently employ micro panel data techniques to their own research topics, and ii) to understand and evaluate micro panel data analyses published in the academic literature. This course focuses on the linear model techniques adopted for the analysis of a typical micro panel-data set with a large number of individuals and a small number of time periods. Such techniques include: fixed and random effects models; robust inference and instrumental-variables estimators. In common with TStat’s training philosophy, each individual session is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained), and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the course, theoretical sessions are reinforced by case study examples, in which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. The intuition behind the choice and implementation of a specific technique is of the utmost importance. In this manner, the course leader is able to bridge the “often difficult” gap between abstract theoretical methodologies, and the practical issues one encounters when dealing with real data. At the end of the course, participants are expected to be able to autonomously implement the theories and methodologies discussed during the course. TARGET AUDIENCE The panel data training course is of particular interest to Master and Ph.D. Students, researchers in public and private research centres and professionals working in the following fields: Agricultural Economics, Economics, Finance, Management, Public Health, and the Political and Social Sciences seeking to acquire the “introductory” applied and theoretical toolset to enable them to undertake independent empirical research using panel data. PREREQUISITES Participants are required to have a good working knowledge of: the classical linear regression model: Model Assumptions, Estimation and Inference; and the statistical software Stata: including familiarity with Stata variable creation commands and Stata do files. Those needing to refresh these concepts are referred to: Cameron, A. C., & Trivedi, P. K. (2022). Microeconometrics Using Stata, Second Edition Volume I: Cross-Sectional and Panel Regression Methods, Stata Press. Chapters 1-9 PROGRAM SESSION I: INTRODUCTION Panel data: benefits for estimation and inference Preliminary commands: xtset, xtdescribe SESSION II: LINEAR PANEL DATA MODELS WITH EXOGENOUS VARIABLES One-way and two-way fixed effect estimators: xtreg, fe Random Effects Estimators: xtreg, re, xtmixed SESSION III: LINEAR PANEL DATA MODELS WITH EXOGENOUS VARIABLES: ROBUST INFERENCE Robust covariance estimators The first-difference estimator Testing for non i.i.d. errors Testing Random Effects against Fixed Effects: non-robust approach using Hausman robust approach using Mundlak auxiliary regression (Wooldridge, 2010) SESSION IV: LINEAR PANEL DATA MODELS WITH ENDOGENOUS VARIABLES Fixed and Random Effect IV Estimators: xtivreg Hausman and Taylor’s estimator: xthtaylor USEFUL REFERENCES Cameron, A. C., & Trivedi, P. K. (2022). Microeconometrics Using Stata, Second Edition Volume I: Cross-Sectional and Panel Regression Methods, Stata Press Cameron, A. C., & Trivedi, P. K. (2022). Microeconometrics Using Stata, Second Edition Volume II: Nonlinear Models and Casual Inference Methods, Stata Press Woodridge, J. (2010). Econometric Analysis of Cross Section and Panel Data, MIT Press DATE AND LOCATION The 2026 edition of this training course will be offered online on a part-time basis on the 23rd-24th, 28th-29th of April from 9:30 am to 1:00 pm CEST. REGISTRATION FEE AND REGISTRATION FORM Full-time Students*: € 780.00 Ph.D. Students: € 1000.00 Academic: € 1160.00 Commercial: € 1555.00 *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 13th of April 2026. To request further information or the registration form, please fill in the following form or send an email to training@tstat.eu
This course focuses on the linear model techniques adopted for the analysis of a typical micro panel-data set with a large number of individuals and a small number of time periods. Such techniques include: fixed and random effects models; robust inference and instrumental-variables estimators. 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 23rd-24th, 28th-29th of April.
Maximising the Potential of Stata’s new Python Capabilities - Live Online Course
COURSE DESCRIPTION TStat’s “Maximising the Potential of Stata’s New Python Capabilities” course offers participants an excellent opportunity to acquire the introductory programming skills required to integrate Python’s capability into Stata. The course opens with an introductory session focusing on the Python programming basics required by users wishing to exploit the Stata – Python connectivity, before moving on to illustrate how to use Python in a Stata environment and the vice versa. In the closing session a series of practical applications will be discussed in order to highlight WHEN and HOW one should exploit the connectivity between Python and Stata for one’s research. At the end of the course, participants are expected to be able, with the aid of the Stata routines implemented during the sessions, to independently implement the methodologies and techniques illustrated during the course by adopting the Stata routines to their own particular research needs. In common with TStat’s training philosophy, each session is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained) and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the course, theoretical sessions are reinforced using applied case studies, in which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. TARGET AUDIENCE This course is of particular interest to sociologists, mathematicians, economists, ethnologists, epidemiologists and political scientists wishing to acquire the basic tools necessary to use Python routines within Stata. PREREQUISITES Participants should having a working knowledge of Stata. No prior knowledge of Python is necessary, although it will be an advantage. PROGRAM The potential of Stata/Python connectivity: an overview Python programming basics Alternative ways to implement Python in Stata: the PyStata Module Calling Python from within Stata Calling Stata from within Python Mata/Python integration Practical examples Stata integration of Python Scikit-learn for Machine Learning Least squares regression in Mata/Python Stata/Python data visualization DATE AND LOCATION The 2026 edition of this training course will be offered online on a part-time basis on the 9th-10th of April from 9:30 am to 2:00 pm CEST. COURSE LEADERS Dr. Giovanni Cerulli IRCrES-CNR Research Institute on Sustainable Economic Growth National Research Council of Italy – Rome REGISTRATION FEE AND REGISTRATION FORM Full-Time Students*: € 325.00 Ph.D. Students: € 425.00 Academic: € 770.00 Commercial: € 900.00 *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 31st of March 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 opens with an introductory session focusing on the Python programming basics required by users wishing to exploit the Stata – Python connectivity, before moving on to illustrate how to use Python in a Stata environment and the vice versa. 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 9th-10th of April from 9:30 am to 2:00 pm CEST.
BSE Microeconometrics Courses - Executive Education
Courses for the 2025-2026 Academic Year We are now accepting applications for the upcoming edition: Causal Inference for Economics and Policy Making | February 16-27, 2026 | Live Online Learn to identify and estimate causal effects using methods such as matching, regression adjustment, instrumental variables, and selection models, with direct application to policy evaluation. Modern Approaches to Difference-in-Differences | March 2-13, 2026 | Live Online Master state-of-the-art DiD techniques, including staggered treatment timing, event-study designs, and synthetic control-style estimators, to conduct credible empirical research. Visit our website to explore our complete course catalog, and don't hesitate to request more information about specific courses or inquire about tailored training. Why Choose the BSE Microeconometrics Course? Rigorous, research-driven content taught by world-class faculty engaged in frontier macroeconomic research. Focus on methodologies and forecasting tools that are essential in today’s volatile economies. Clear paths to apply models to your real work: policy analysis, forecasting, risk assessment. Flexible schedule: live online courses that allow participation without travel. What You’ll Gain A practical foundation in causal inference methods, from basics to advanced Confidence applying modern DiD estimators in complex treatment settings Tools to critically evaluate empirical studies and conduct your own research Applied experience using statistical software with micro-level economic data BSE's reputation is built on its research community, consistently ranked among the Top 5 departments in Europe and the Top 12 worldwide (Source: RePEc, March 2025).
Advance your expertise in policy evaluation and applied economics with BSE Executive Courses in Microeconometrics: Our short, intensive programs designed for professionals working with micro-level data in government, academia, and the private sector. Directed by Prof. Joan Llull (IAE-CSIC and BSE), an expert in labor markets and econometric methods, these courses provide rigorous training in the tools used to estimate causal effects, evaluate interventions, and extract insights from individual-level data. BSE Microeconometrics School brings together renowned experts to deliver a comprehensive learning experience meanwhile each program blends theory with real applications, giving you the skills to answer complex economic questions with confidence and precision.
state-of-the-art causal inference method
microeconometrics, machine learning, policy evaluation
BSE Data Science for Economics Course - Executive Education
Course Offering for 2025-2026: · Data Science for Economics | February 16-27, 2026 | Live Online Learn how to organize, analyze, and extract insights from large-scale economic datasets using modern data science and machine learning methods. The course emphasizes hands-on implementation, real applications, and cases tailored to economic research. Visit our website to explore our complete course catalog, and don't hesitate to request more information about specific courses or inquire about tailored training for your entire team. Why Choose BSE Data Science Executive Education? · Economics focus: Learn how data science and economics intersect at one of Europe's top institutions. · Gain practical skills you can apply immediately to your research or professional projects. · Comprehensive Data Science Training: Covers data handling, visualization, supervised and unsupervised learning tailored for economic applications. · Hands-on Practice with Jupyter Notebooks: Practical coding experience in Python and R, key for data science roles. · Instruction from Expert Faculty: Led by academics with robust experience in data science and economics. Flexible Format: Available online to suit different schedules and time-zones. Join Us and Transform Your Career: BSE's reputation is built on its research community, consistently ranked among the Top 5 departments in Europe and the Top 12 worldwide (Source: RePEc, October 2025). Equip yourself with the tools of modern data science and apply them directly to the economic and policy challenges you face.
Data science and machine learning have revolutionized how we analyze data and carry out research over the last 10 years. This short-intensive live online course is tailor-made for professionals looking to get up to date with current practices in economic research. Unlike other courses, our program covers basic machine learning and data science material, specifically geared towards those working with economic datasets. Directed by Prof. Lorenzo Cappello, Assistant professor in the Statistics group at Universitat Pompeu Fabra and affiliated professor at the Barcelona School of Economics, the program offers an unparalleled learning experience.
BSE Macroeconometrics Courses - Executive Education
Courses for the 2025-2026 Academic Year We are now accepting applications for the upcoming edition: Bayesian Macroeconometrics | February 16-27, 2026 Explore Bayesian methods applied to macroeconomic modelling: priors, posterior inference, model comparison, and uncertainty quantification. Dynamic Causal Effects with VARs and FAVARs | February 16-27, 2026 Estimate responses to economic shocks using VARs and FAVARs, and learn how factor models help capture latent structure in macroeconomic data. Spectral Analysis in Macroeconomics | February 16-27, 2026 Use frequency-domain tools to understand cycles, co-movement, persistence, and spectral decomposition in macro time series. Dynamic Causal Effects with BVARs and Local Projections | March 2-13, 2026 Combine Bayesian VARs and local projection approaches to assess dynamic policy effects and forecast transitions under uncertainty. Local Projections in Macroeconomics | March 2-13, 2026 Deep dive into local projection methods for estimating impulse responses, policy impacts, and handling heterogeneous effects over time. Nowcasting and Forecasting Extreme Events | March 2-13, 2026 Learn cutting-edge techniques to forecast rare or extreme macroeconomic events — recessions, crises, etc. — and improve early warning systems. Visit our website to explore our complete course catalog, and don't hesitate to request more information about specific courses or inquire about tailored training for your entire team. Why Choose BSE Macroeconometrics Executive Courses? Year after year, professionals from central banks and leading institutions worldwide choose BSE to update their knowledge and acquire cutting-edge techniques, tools, and methodologies. Our courses deliver immediate real-world value, enhancing your day-to-day work. Rigorous, research-driven content taught by world-class faculty engaged in frontier macroeconomic research. Focus on methodologies and forecasting tools that are essential in today’s volatile economies. Clear paths to apply models to your real work: policy analysis, forecasting, risk assessment. Flexible schedule: live online courses that allow participation without travel. Boost Your Research Journey: Ph.D. students find our research-led courses invaluable. They accelerate their thesis progress by applying new knowledge, engaging in hands-on sessions, and exchanging insights with international peers facing similar challenges. BSE: Academic Excellence with Real-World Impact Taught to the same high standard as BSE’s Master’s programs. Delivered by faculty with strong research credentials in macroeconometrics, forecasting, structural analysis. Certificate of attendance provided. Flexible format for broader engagement. BSE courses are designed and taught by members of a research community that ranks in the Top 5 departments in Europe and the Top 12 worldwide (Source: RePEc, March 2025).
Advance your expertise in macroeconomic modeling and forecasting with BSE Executive Courses in Macroeconometrics. These short, intensive online programs are designed for researchers, economists, and practitioners who need robust tools to analyze time series, estimate causal effects, and anticipate shocks in uncertain economic environments. Directed by Prof. Luca Gambetti (UAB and BSE), and taught by leading experts such as Andrea Carriero, Gabriel Pérez-Quirós, Ivan Petrella, Giovanni Ricco, and Luca Sala, the program combines cutting-edge research with methods you can apply immediately in policy, finance, and research settings.
Discover new data analysis techniques through engaging data science projects Whether you’re beginning a career in data science or want to understand your organisation’s data better, this course will strengthen your knowledge of data analysis tools and techniques. You’ll complete practical projects that will demonstrate real-world applications of data science. This will allow you to assess different data science scenarios and choose the best approach, grounded in a broad knowledge of data analysis methodology. Explore data visualisation tools and methods You’ll delve into different types of data analysis and explore how to create effective data visualisation using MySQL. Using case studies as a starting point, you’ll discover step-by-step data visualisation processes, from gathering the data to displaying the output. With this knowledge, you’ll be able to solve problem statements with data visualisation and be able to apply this knowledge to your own work. Develop your data manipulation and interpretation skills Having explored data analysis methods, you’ll then learn to evaluate and interpret this data in a meaningful way. With a solid understanding of how to collect, review, and evaluate data, you’ll be able to explain your findings and the supporting methodology. By the end of this course, you’ll understand applied data science methods and concepts. You’ll have gained insights into a variety of analytical approaches to data and be able to use these to interpret data effectively. What topics will you cover? This video course will help you to learn the important concepts and theories of applied data science that you need to know to store, manipulate, and visualize huge chunks of data. The course starts with an introduction to applied data science and a tutorial on how to set up a Jupyter notebook. You’ll then go on to understand linear regression using Boston data. As you advance, you’ll discover data visualization techniques and explore time series and data evaluation. You’ll also get to grips with extended data analysis with the help of a temperature analysis activity. Toward the end, you’ll be introduced to k-means clustering and gain a solid understanding of decision trees. When would you like to start? Start straight away and join a global classroom of learners. Available now Join - €79 Learning on this course On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments. What will you achieve? By the end of the course, you‘ll be able to... Classify effective data science techniques Explore relevant data science case studies Practice your learnings on your own projects Who is the course for? This course is designed for anyone interested in learning about data science, particularly those looking to begin a career in data science of analytics. It would also be suitable for those wanting to better understand their organisation’s data and how to use and interpret it effectively. What software or tools do you need? You’ll need to download and install the Anaconda software to your Windows, MacOS, or Linux system. We’ll show you how to do this on the course. Who developed the course? Packt Founded in 2004 in Birmingham, UK, Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.
Data science allows organisations to understand and interpret swathes of data and gain insights that allow for smarter decision making, driven by data. This two-week course provides an overview of the key concepts in data science, from understanding regression to using K-means clustering.
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. There are 4 modules in this course Fundamentals of Data Manipulation with Python Basic Data Processing with Pandas More Data Processing with Pandas Answering Questions with Messy Data This course is part of the <a class="cds-119 cds-113 cds-115 css-7jm8m6 cds-142" href="https://imp.i384100.net/KjnKbv" data-click-key="unified_description_page.consumer_course_page.click.career_outcomes_program_link" data-click-value="{"href":"/specializations/data-science-python","namespace":{"action":"click","app":"unified_description_page","component":"career_outcomes_program_link","page":"consumer_course_page"},"schema_type":"FRONTEND"}" data-track="true" data-track-app="unified_description_page" data-track-page="consumer_course_page" data-track-action="click" data-track-component="career_outcomes_program_link" data-track-href="/specializations/data-science-python" to="/specializations/data-science-python">Applied Data Science with Python Specialization This Introduction to Data Science in Python course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python. When you enroll in this course, you'll also be enrolled in this Specialization. Learn new concepts from industry experts Gain a foundational understanding of a subject or tool Develop job-relevant skills with hands-on projects Earn a shareable career certificate
What you'll learn Understand techniques such as lambdas and manipulating csv files Describe common Python functionality and features used for data science Query DataFrame structures for cleaning and processing Explain distributions, sampling, and t-tests