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.

COURSE STRUCTURE
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 Sample Selection and the Control function approach.

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.

COURSE OUTCOME
At the end of the course, participants are expected to be able to autonomously implement (with the help of the Stata routine templates specifically developed for the course) the appropriate estimation techniques, given both the nature of their data and the analysis in hand.

TARGET AUDIENCE
Researchers and Ph.D. Students, Professionals working in biostatistics, economics, epidemiology, finance, public health, psychology, social and political sciences needing to acquire the necessary statistical requisites required to independently conduct empirical analysis using micro data.

PREREQUISITE
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:

Cameron, A. C. & Trivedi, P. K. (2022). Microeconometrics Using Stata, Volume I: Cross-Sectional and Panel Regression Methods. Second Edition. Stata Press. Chapters: 1-7.

PROGRAM
SESSION I: COUNT MODELS
SESSION II: DISCRETE DEPENDENT VARIABLE MODELS 
SESSION III: PROBIT MODELS WITH ENDOGENOUS REGRESSORS
SESSION IV: MULTINOMIAL MODELS
SESSION V: THE TOBIT MODEL, INTERVAL REGRESSION E SAMPLE SELECTION

SUGGESTED READINGS
Cameron, A. C. & Trivedi, P. K. (2022). Microeconometrics Using Stata, Volume I: Cross-Sectional and Panel Regression Methods. Second Edition. Stata Press.
Cameron, A. C. & Trivedi, P. K. (2022). Microeconometrics Using Stata, Volume II: Nonlinear Models and Casual Inference Methods. Second Edition. Stata Press.
Woodridge, J. (2010). Econometric Analysis of Cross Section and Panel Data. MIT Press.
Cameron, A. C. & Trivedi, P. K. (2005). Microeconometrics: Methods and Applications. Cambridge University Press.

DATE AND LOCATION
The 2023 edition of this training course will be offered online on a part-time basis on the 16th-17th, 23rd-24th, 30th of November – 1st, 4th of December from 10 am to 1.30 pm Central European Time (CET).

FEES AND REGISTRATION
Full-time Students*: € 1185.00
Ph.D. Students: € 1515.00
Academic: € 1760.00
Commercial: € 2360.00

*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 Stata valid for 30 days from the day before the course commences.

Individuals interested in attending this workshop must return their completed registration forms by email (training@tstat.eu) to TStat by the 6th November 2023.