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CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects

Michael Vogt, Christopher Walsh and Oliver Linton

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: Interactive fixed effects are a popular means to model unobserved heterogeneity in panel data. Models with interactive fixed effects are well studied in the low-dimensional case where the number of parameters to be estimated is small. However, they are largely unexplored in the high-dimensional case where the number of parameters is large, potentially much larger than the sample size itself. In this paper, we develop new econometric methods for the estimation of high-dimensional panel data models with interactive fixed effects. Our estimator is based on similar ideas as the very popular common correlated effects (CCE) estimator which is frequently used in the low-dimensional case. We thus call our estimator a high-dimensional CCE estimator. We derive theory for the estimator both in the large-T-case, where the time series length T tends to infinity, and in the small-T-case, where T is a fixed natural number. The theoretical analysis of the paper is complemented by a simulation study which evaluates the finite sample performance of the estimator.

Keywords: CCE estimator; high-dimensional model; interactive fixed effects; lasso; panel data (search for similar items in EconPapers)
JEL-codes: C13 C23 C55 (search for similar items in EconPapers)
Date: 2022-06-28
New Economics Papers: this item is included in nep-ecm
Note: obl20
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