The course will cover the basics of panel data analysis and some more advanced extensions, focusing mainly on microeconometric settings with a large number of cross-sectional observations. The statistical package Stata will be used to illustrate all of the methods, including applications to the banking sector.
The common estimators – random effects, fixed effects, and first differencing will be discussed, with emphasis on robust inference and specification tests. During the course modules, the instructor will present in details:
The extensions that allow heterogeneous slopes and trends
Instrumental variables methods
Estimation of dynamic models also will be covered.
Fixed effects estimation and inference with a large number of time periods, applicable to more aggregated data.
The problem of unbalanced panels and how to test for nonrandom sample selection
The panel data methods will be applied to estimate bank cost functions as well as estimating the effect of foreign ownership on market power, as in Delis, Kokas, and Ongena (2016, JMCB). Also, difference-in-differences methods will be illustrated by studying the effects of changes in banking regulations, such as the European Bank and Recovery Resolution Directive, on credit default swaps.
You will learn to use Stata to estimate basic linear panel data models by random effects, fixed effects, first differencing, and instrumental variables versions of these.
You will learn how to use robust specification tests to choose among estimation methods.
You will learn what happens when additional heterogeneity is introduced into the basic model.
You will be introduced to large T panel data sets.
You will understand the consequences of unbalanced panel data sets.
You will understand the tradeoffs between pooled and joint estimation methods for nonlinear models
You will learn about the effects of the Bank Recovery and Resolution Directive (BRRD) on Credit Default Swap (CDS) spreads.