Sumários

TP12 - Dynamic Panel Data

30 Abril 2025, 13:30 Luís Silveira Santos

Illustration 4 (conclusion).

Dynamic Panel Data: introduction, advantages and potential pitfalls, statistical properties of the classic panel data estimators (Pooled OLS, Random Effects, Fixed Effects, First Differences), introduction to Two-Stage estimation and definition of an instrumental variable, Arellano-Bond and Blundell-Bond estimators, Hansen-Sargan overidentification test and Arellano-Bond autocorrelation tests. Examples. Illustration 5.


TP11 - Panel Data

23 Abril 2025, 13:30 Luís Silveira Santos

Panel Data: introduction, advantages and potential pitfalls, relationship between N and T (micropanels vs. macropanels); structure of the panel (balanced vs. unbalanced); two-way error component model specification; panel estimators (Pooled OLS, Random Effects, Fixed Effects, First Differences) and their statistical properties (considering the classical assumptions of the linear regression model). Examples. Illustration 4.


TP10 - LRM

9 Abril 2025, 13:30 Luís Silveira Santos

Linear regression: in depth interpretation of the regression coefficients with binary regressors (interactions vs. non-interactions), Chow test, Heteroskedasticity (definition, testing and correction). Examples. Illustration 3.


TP09 - LRM

2 Abril 2025, 13:30 Luís Silveira Santos

Linear regression: Gauss-Markov theorem, statistical properties of the OLS estimator, t-test of individual significance, F-test of global and overall significance, testing linear restrictions, effects of multicolinearity (on the estimated variance of the OLS estimator and on statistical inference), omitted explanatory variables and irrelevant explanatory variables (exogeneity vs. endogeneity and efficiency), binary explanatory variables and their interactions (with quantitative explanatory variables), topics on the Chow test, RESET test, definition of heteroskedasticity. Examples.


TP08 - LRM

26 Março 2025, 13:30 Luís Silveira Santos

Linear regression: quality of the adjustment, assumptions of the LRM, more topics on statistical inference. Examples.

Empirical Project #2.