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.
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
Economic Development, Innovation and Growth (JEL O)
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)
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)
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)
Macroeconomics and Monetary Economics (JEL E)
Agricultural and Environmental Economics (JEL Q)
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)
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.
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.
Introduction to Sustainable Finance (Online Course)
We will analyze approaches to sustainable finance, evaluate the correlation between risk and return, and gain insight into the vital features responsible for successfully meeting environmental and social initiatives. We will also explore and evaluate the role of the public sector in sustainable finance, and analyze disruptive trends and the innovations necessary to manage them. At the end of this course, you will complete a case study that outlines how your organization can create your own sustainable finance initiatives and how you can be a key player in meeting those goals. Become a Sustainable Finance expert with a purpose by learning to integrate people, profits, and the planet into the bottom line. Who this course is for: Finance professionals currently in the industry, as well as those seeking to break in, will gain insight into how to best invest capital in order to promote a sustainable future. Instructor: Dominick Mitchell Dominick Mitchell is a leading expert in sustainable finance, with a passion for finance, international development, and sustainability. He has been recognized as the Youth Entrepreneur of the Year and has written a book on the topic, Finance Made Sustainable. In his work, Dominick assists social enterprises with their sustainable finance initiatives, helping them to solve social problems through engagement with the private sector. He also manages WVÉS, a water-focused impact fund that supports investees in achieving both social and financial returns. Dominick's ultimate goal is to mobilize the private sector to become more sustainably conscious and inclusive on a global scale.
Finance is widely seen as an obstacle to a better world. Financial services make up one of the economy's most important and influential sectors globally. Although results vary, most estimates place the financial services sector at around 20-25% of the world economy. Finance fosters growth, promotes entrepreneurship, favors education, alleviates poverty, and reduces inequality. In this online course, you will learn the essential role the financial system plays in reaching the United Nations Sustainable Development Goals and creating a globally sustainable economy. Students will learn how the current financial systems run, the factors that impact them, and the circulation of capital.
Workshop on Institutional Analysis Date: December 6-12, 2026 Location: Mexico City, Mexico Co-sponsored by Ronald Coase Institute & Instituto Technológico Autónomo de Mexico (ITAM) Apply by July 31, 2026 Attend this workshop on institutional analysis to: 1) Present your research in new ways in a small, focused setting. Share intensive discussions and get feedback from faculty and other participants: first within a small group, then formally before the entire workshop. Ten full-time faculty will provide intensive mentoring. 2) Hear the faculty discuss their own research and strategies to formulate questions, develop analysis, and communicate results. 3) Network closely with faculty and other young scholars who have an enduring interest in institutional analysis. Upon graduation, become part of a scholarly network with alumni from 78 countries. The pace is intense and rewarding. Past participants have described these workshops as a “superlative experience”; “demanding, intensive and mentally exhausting … also one of the most rewarding”; filled with “vibrant academic discussions and informal exchanges”; and a source of “incredibly useful advice … and contacts.” Who is eligible? Social scientists early in their academic/professional careers and advanced graduate students, in economics, political science, law, and other social sciences. Work already published is not eligible, and previous participants are not eligible to apply again. Selection Twenty-six participants will be selected competitively on the basis of their research abstracts, CVs, and references. Abstracts will be evaluated on the clarity and importance of the research question, analysis, and institutional focus. Projects with empirically testable implications are preferred. To apply, submit your research abstract (350 words max), 1-page CV, statement of purpose, and the names of two references. Admissions decisions will be announced in August 2026. https://www.coase.org/workshopapplication.htm Costs and fellowships The cost of the workshop – tuition, meals, and housing – is $2500 USD. Many fellowships will be awarded competitively in varying amounts to scholars for whom this cost is a hardship. If you wish to be considered for a fellowship, submit your fellowship request with your application. Note that all participants must stay in workshop housing. All participants are responsible for paying their own travel expenses. Faculty A distinguished faculty including Nobel laureate Roger Myerson will serve as mentors and lecturers in this workshop. The core faculty will include Emilio Depetris-Chauvin (Pontificia Universidad Católica de Chile), Sebastian Galiani (University of Maryland), Marc Goñi (University of Bergen, Norway), Philip Keefer (Inter-American Development Bank), Gary Libecap (University of California – Santa Barbara, Emeritus), Cheryl Xiaoning Long (Xiamen University and Ronald Coase Institute), Claude Menard (University of Paris 1, Panthéon-Sorbonne, Emeritus), Roger Myerson (University of Chicago, Nobel laureate), Mary Shirley (Ronald Coase Institute), and Alberto Simpser (ITAM). Co-sponsors Instituto Tecnológico Autónomo de México (ITAM) Important Dates Application opening date: June 10, 2026 Application deadline: July 31, 2026 Admissions decisions announced: August 2026 Workshop dates: December 6–12, 2026 More Information https://www.coase.org/whatsnew.htm Ronald Coase Institute: Workshop Application Organizers and administrators The organizers are Alexandra Benham (Ronald Coase Institute), Cheryl Xiaoning Long (Xiamen University and Ronald Coase Institute), Mary Shirley (Ronald Coase Institute), and Alberto Simpser (ITAM). The administrators are Darin Hargis (Ronald Coase Institute) and Ana María González Franco (ITAM).
Attend this workshop to present your own research in new ways in a small, focused setting, receiving several rounds of feedback from faculty and other participants. Learn more about institutional analysis, and network with scholars worldwide. Ten distinguished, full-time faculty including a Nobel Laureate will provide intensive mentoring and will discuss their own research and strategies to formulate questions, develop analysis, and communicate results. Social scientists early in their academic/professional careers and advanced graduate students are eligible to apply. Admission is competitive and limited to 26 participants. To apply, submit a 1-page research abstract, 1-page CV, 2 references, and a statement of purpose. Many fellowships will be awarded competitively in varying amounts. The workshop will be held in Mexico City, Mexico and is co-sponsored by the Instituto Tecnológico Autónomo de México (ITAM). Applications are due July 31, 2026. Ronald Coase Institute: Workshop Application
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.
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.
The art of uncovering insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and accurately predicted the Nile River's flooding every year. Since then, people have continued to use data to derive insights and predict outcomes. Recently, they have carved out a unique and distinct field for the work they do. This field is data science. In today's world, we use Data Science to find patterns in data and make meaningful, data-driven conclusions and predictions. This course is for everyone and teaches concepts like how data scientists use machine learning and deep learning and how companies apply data science in business. You will meet several data scientists, who will share their insights and experiences in data science. By taking this introductory course, you will begin your journey into this thriving field. What you'll learn Define data science and its importance in today’s data-driven world. Describe the various paths that can lead to a career in data science. Summarize advice given by seasoned data science professionals to data scientists who are just starting out. Explain why data science is considered the most in-demand job in the 21st century. There are 4 modules in this course Defining Data Science and What Data Scientists Do Data Science Topics Applications and Careers in Data Science Data Literacy for Data Science (Optional)
Do you want to know why data science has been labeled the sexiest profession of the 21st century? After taking this course, you will be able to answer this question, understand what data science is and what data scientists do, and learn about career paths in the field.
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