COURSE ID: D-EF10/4 LANGUAGE:

PANEL DATA ANALYSIS IN STATA

Panel data contains information on many cross-sectional units, which are observed at regular intervals across time. Panel data, by its very nature, can therefore be highly informative regarding heterogeneous subjects and thus it is increasingly used in econometrics, financial analysis, medicine and the social sciences.

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.

The focus is on the 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; instrumental-variables estimators; sample selection and attrition; non-linear models. In the closing sessions, the more recently developed Extended-Regression-Model (ERM) command to simultaneously control for issues of endogeneity and sample selection are discussed.

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 workshop, 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 workshop.

The panel data workshop 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, Political Sciences and the 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 the OLS regression model and the statistical software Stata. Knowledge at the argoments illustrated in TStat’s course Introduction to Microeconometrics with Stata will also prove to be a advantage.

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 Mondlak 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

 

SESSION V: NON-LINEAR PANEL DATA MODELS

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, xtintreg

Random effects
Correlated effects modelled as group means (a la Mundlak)

Postestimation analysis:

Average marginal effects: margins
Goodness-of-fit measures: predict

 

SESSION VI: LINEAR PANEL DATA MODELS WITH SAMPLE SELECTION

Estimators for linear random-effect models with sample selection:

xtheckman

Estimators for linear fixed-effect models with sample selection:

procedures in Wooldridge (2010)

Attrition: procedures in Wooldridge (2010)

 

SESSION VII: PANEL DATA MODELS WITH ENDOGENOUS REGRESSORS AND TREATMENT ASSIGNMENT AND SAMPLE SELECTION

Extended linear models (xteregress)
Extended Probit models (xteprobit, xteoprobit)
Extended interval regression models (xteintreg)

 

 

COURSE REFERENCES

Microeconometrics using Stata, Revised Edition, (2010) di A. C. Cameron e P. K. Trivedi, Stata Press
Econometric Analysis of Cross Section and Panel Data (2010) di J. Wooldridge, MIT Press

The course will be held in Frankfurt am Main from the 25th to the 28th May 2020.

Student*: € 1540.00
Academic: € 2140.00
Commercial: € 2815.00

*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year.

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.

Please note that a non-refundable deposit of €100.00 for students and €200.00 for Academic and Commercial participants, is required to secure a place and is payable upon registration. The number of participants is limited to 12. Places will be allocated on a first come, first serve basis.

Course fees cover: teaching materials (handouts, Stata do files and datasets to used during the course), a temporary licence of Stata valid for 30 days from the beginning of the workshop, light lunch and coffee breaks.

To maximize the usefulness of this workshop, we strongly recommend that participants bring their own laptops with them, to enable them to actively participate in the empirical sessions.

Individuals interested in attending this workshop must return their completed registration forms by email (training@tstat.eu) to TStat by the 5th May 2020.


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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.