COURSE ID: D-EF10-A-OL LANGUAGE:

Linear Panel Data Models in Stata

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.

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.

Participants are required to have a good working knowledge of:

  • 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:

SESSION I: INTRODUCTION

1. Panel data: benefits for estimation and inference

2. Preliminary commands: xtset, xtdescribe 

 

SESSION II: LINEAR PANEL DATA MODELS WITH EXOGENOUS VARIABLES

1. One-way and two-way fixed effect estimators: xtreg, fe

2. Random Effects Estimators: xtreg, re, xtmixed

 

SESSION III: LINEAR PANEL DATA MODELS WITH EXOGENOUS VARIABLES: ROBUST INFERENCE        

1.  Robust covariance estimators

2. The first-difference estimator

3. Testing for non i.i.d. errors

4. 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

1. Fixed and Random Effect IV Estimators: xtivreg 

2. Hausman and Taylor’s estimator: xthtaylor 

 

COURSE REFERENCES

The 2023 edition of this training course will be offered ONLINE on a part-time basis on the 14th-15th and 20th-21st of March from 10:00 am to 1:30 pm Central European Time (CET).

Full-time Students*: € 710.00
Ph.D. Students: € 909.00
Academic: € 1060.00
Commercial: € 1420.00

 

*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: teaching materials (handouts, Stata do files and datasets to used during the course) and a temporary licence of Stata valid for 30 days from the beginning of the course.

 

Individuals interested in attending this course must return their completed registration  forms by email (training@tstat.eu) to TStat by the 4th March 2023.


To request further information or the registration form, please fill in the following form or send an email to training@tstat.it


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ONLINE COURSE

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.

 

Due to the current pandemic situation, the 2023 edition of this training course will now be offered ONLINE, on a part-time basis on the 14th-15th and 20th-21st of March.