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Analysing Micro Data in Stata

TStat’s Analysing Micro Data in Stata course offers participants a comprehensive introduction to the principle methodologies used in the analysis of micro data. Micro data, data which contains information at the level of a specific unit (such as individuals, firms or entities), has by its very nature become an increasingly important source of information offering researchers and policy makers an effective tool with which to obtain a more in-depth understanding of an array of political, socio-economic and public health phenomena. As such the collection and subsequent analysis of micro data over recent years has proved to be the key to policy formulation, the targeting of interventions and the subsequent monitoring and measurement of the impact of such interventions and policies. Whilst these techniques have been traditionally more applied in the field of economics, the increasing availability of micro data has over recent years resulted in a steady increase in the analysis of micro data by researchers working in Political and Social Sciences, Biostatistics, Epidemiology and Public health.

 

TStat’s introduction to micro data analysis course focuses from both a theoretical and applied point of view, on the following methodologies: count models, binary dependent variable models, multinomial models, Tobit and Interval Regression models, models with treatment variables and models with Sample Selection. The concluding session focuses on the Control Function approach for the estimation of non-linear models with endogenous continuous variables.

 

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.

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.

Participants are required to have a working knowledge of:

 

the classical OLS regression model: Model Assumptions, Estimation and Inference;

Instrumental Variables (IV) and General Method of Moments (GMM) estimation techniques;

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:

 

Chapter 1 – 7 of  A. Colin Cameron and Pravin K. Trivedi Microeconometrics Using Stata, Second Edition.

SESSION I: COUNT MODELS

Count Model Estimators in Stata: The Poisson Model

Non-Linear Least Squares and GMM Estimators, Maximum Likelihood Estimators in Stata: nl, gmm, poisson
Models with endogenous regressors: gmm and ivpoisson

Estimation and Specification tests in the presence of overdispersion: the Generalized Negative Binomial Model: nbreg, gnbreg
Estimation and interpretation of marginal effects using the Stata post estimation command margins

 

SESSION II: DISCRETE DEPENDENT VARIABLE MODELS 

Estimating linear models with binary dependent variables – Logit, Probit and the Linear Probability Model: probit, logit, regress
The Heteroskedastic Probit Model and tests of heteroskadicity: hetprobit
Measures of Goodness of Fit and Specification Tests: tabulate, estat classification, estat gof
Independent Latent Heterogeneity in Probit Models
Estimating marginal effects: margins
Numerical problems with Logit and Probit

 

SESSION III: PROBIT MODELS WITH ENDOGENOUS REGRESSORS

The Control Function (CF) in the presence of continuous endogenous regressors
Testing for exogeneity in the CF framework
Bootstrap standard error estimation in the CF approach
Maximum likelihood estimation in the presence of continuous endogenous regressors: ivprobit
The multivariate recursive Probit estimator as a solution to the problem of the presence of binary endogenous regressors: biprobit, mvprobit, cmp
Measures of Goodness of Fit: tabulate, estat classification, estat correlation
Estimating marginal effects: margins

 

SESSION IV: MULTINOMIAL MODELS

Ordered categorical variable models (the Ordered Probit and Ordered Logit Estimators): oprobit and ologit
The Heteroskedastic Probit Model and tests of heteroskadicity: hetoprobit
Models with categorical (but unordered) variables – Multinomial Logit and Multinomial Probit estimators: mlogit, mprobit
MacFadden’s Choice Model – categorical variable models with alternative specific regressors: cmclogit, cmcprobit
Measures of Goodness of Fit and Specification Tests
Estimation and interpretation of marginal effects using the Stata post estimation command margins

 

SESSION V: THE TOBIT MODEL, INTERVAL REGRESSION E SAMPLE SELECTION

The Tobit Model – ML and Two-Step Least Squares: tobit, heckman
The Control Function (CF) approach in the presence of continuous endogenous regressors, exogeneity tests and Bootstrap standard errors
The Maximum Likelihood estimator for Tobit models with endogenous regressors: ivtobit
Interval Regression: a generalization of the Tobit Model: intreg
Estimators for Sample Selection Models: heckman
Estimation and interpretation of marginal effects using the Stata post estimation command margins

 

 

COURSE REFERENCES 

Wooldridge, (2010) Econometric Analysis of Cross Section and Panel Data, Second Edition MIT Press
Cameron e Trivedi, (2010) Microeconometrics using Stata, Revised Edition StataPress
Cameron e Trivedi, (2005) Microeconometrics: Methods and Applications, Cameron e Trivedi, Cambridge University Press

We are currently putting the finishing touches to our 2023 training calendar. We therefore ask that you re-visit our website periodically or contact us at training@tstat.eu should the dates for the course which you are interesting in following not yet be published. You will then be contacted via email as soon as the dates are available.

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TStat’s Analysing Micro Data in Stata course offers participants a comprehensive introduction to the principle methodologies used in the analysis of micro data. Micro data, data which contains information at the level of a specific unit (such as individuals, firms or entities), has by its very nature become an increasingly important source of information offering researchers and policy makers an effective tool with which to obtain a more in-depth understanding of an array of political, socio-economic and public health phenomena.