Correlated random effects methods for panel data models with heterogeneous time effects

Jeffrey Wooldridge

London Stata Conference 2020 from Stata Users Group

Abstract: I propose a correlated random effects (CRE) approach to linear panel data models with heterogeneous time effects. The setting is microeconometric, where the number of time periods is small relative to the number of cross-sectional units. Given T time periods, T different sources of heterogeneity are allowed, and each is allowed to be correlated with time-constant features of the covariates. In the leading case, the CRE approach extends the Mundlak regression by allowing each heterogeneity term to be correlated with the time averages of the time-varying covariates. Additional flexibility is allowed by extracting unit-specific trends from the covariates and using those in the CRE approach. Estimation requires (many) linear regressions. For small T, the approach is an alternative to factor models, which require nonlinear estimation in addition to pre-testing to determine the number of factors. I show straightforward implementation of the new estimators in Stata.

Date: 2020-09-11
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Persistent link: https://EconPapers.repec.org/RePEc:boc:usug20:06

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