COURSE ID: D-EF13 LANGUAGE:

MICROECONOMETRICS USING STATA

Microeconometrics in Stata offers participants with a comprehensive applied and theoretical overview of the principle methodologies implemented in the analysis of microeconomic data. More specifically, the course focuses on instrumental variable analysis, non-linear least squares estimation, binary variable models, multi-nominal models, Tobit models and count data models, panel data models, IV estimators and GMM estimators. Although the course is entitled “Microeconometrics in Stata”, as the examples discussed relate to economic data, the techniques developed through the courses can of course are extensively implemented in other social sciences.

In common with TStat’s course 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.

Researchers and professionals working in biostatistics, economics, epidemiology, finance, psychology, social and political sciences needing to acquire the necessary statistical requisites required to independently conduct empirical analysis using micro data.

It is assumed that course participants have at some point followed a basic course in econometrics or statistics. Previous exposure to Stata or other statistical software packages would also be an advantage.

SESSION I: PRELIMINARY TOPICS

Stata 15 – a quick review

Linear and non-linear models in Econometrics

Estimators and tests for linear models with endogenous variables: Instrumental Variables and Generalized Method of Moments (ivregress, ivreg2, gmm, treatreg)

Estimators and tests for non-linear models

Estimating marginal effects with margins

 

SESSION II: COUNT MODELS

The Poisson model

Estimators: Non-Linear Least Squares (nl), GMM (gmm), Maximum likelihood (poisson)

Endogenous regressors (gmm and ivpoisson)

Overdispersion: the Negative Binomial Model (nbreg)

 

SESSION III: Discrete dependent variable models

Univariate models

Linear Probability Model, Probit and Logit (regress, probit, logit)

Ordered models (oprobit, ologit)

Multivariate models

Bivariate and multivariate Probit models (biprobit, mvprobit, cmp)

Multinomial models

(Conditionally) independent latent heterogeneity in probit models

Estimation of average partial effects

Endogenous regressors in probit models

The control function approach (CFA) to continuous endogenous regressors: test and estimation

Bootstrap standard errors and covariance matrix in the CFA

Maximum likelihood estimation with continuous endogenous regressors (ivprobit)

A multivariate probit solution to binary endogenous regressors (biprobit, mvprobit, cmp)

 

SESSION IV: PROBIT AND LOGIT PANEL-DATA MODELS

The ancillary parameter problem in non-linear models with correlated latent heterogeneity (LH)

Logit and probit panel data models with LH

Models with independent LH: Random effect models (xtlogit, xtprobit)

Models with correlated LH: Fixed effect models

The Chamberlain-Mundlak approach for probit models

The Fixed effect logit model (xtlogit)

 

SESSION V: ModelS with Censoring and sample selection

Censoring

Tobit models: ML and Two-step Least Squares (tobit)

The CFA to continuous endogenous regressors: test and estimation

Maximum likelihood estimation with continuous endogenous regressors (ivtobit)

Panel data tobit models with LH

Sample selection

Tests and corrections a la Heckman (heckman) for linear models

Tests and corrections for linear panel-data models

Attrition in panel-data models: Inverse Probability weighting (IPW)

Bootstrap standard errors with IPW

 

 

The course will be held in Frankfurt am Main at Fleming’s Deluxe Hotel Frankfurt-City, Eschenheimer Tor 2, from the 6th to 8th November 2017.

 

FLEMING’S DELUXE HOTEL FRANKFURT-CITY ♦ Eschenheimer Tor 2, Bleichstraße 64-66, 60318 Frankfurt am Main

Students*: € 735.00

Academic: € 1225.00

Government / Nonprofit: € 1513.00

Commercial: € 1800.00

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

All fees are subject to VAT (applied at the current Italian rate of 22%).

The number of participants is limited to 15. Places will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 8 individuals are enrolled.

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

In order to maximize the usefulness of this workshop, we recommend that participants bring their own laptops with them, to be able to actively participate in the empirical sessions.

 

Please note:

  • A 15% discount will be applied to all additional participants from the same company or institution enrolling on the same course.
  • TStat’s Training Packages: individuals attending more than one of our training courses during the course of 2017 are entitled to a 15% discount off subsequent course fees.

Individuals interested in attending the training course, must return their completed registration forms either by email (training@tstat.eu) or by fax (+39 0864 206014) to TStat by the 20th of October 2017.


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Microeconometrics in Stata offers participants with a comprehensive applied and theoretical overview of the principle methodologies implemented in the analysis of microeconomic data. More specifically, the course focuses on instrumental variable analysis, non-linear least squares estimation, binary variable models, multi-nominal models, Tobit models and count data models, panel data models, IV estimators and GMM estimators. Although the course is entitled “Microeconometrics in Stata”, as the examples discussed relate to economic data, the techniques developed through the courses can of course are extensively implemented in other social sciences.