Estimation and Inference in Large Heterogenous Panels with Cross Section Dependence

Mohammad Pesaran

No 869, CESifo Working Paper Series from CESifo

Abstract: This paper presents a new approach to estimation and inference in panel data models with unobserved common factors possibly correlated with exogenously given individual-specific regressors and/or the observed common effects. The basic idea behind the proposed estimation procedure is to filter the individual-specific regressors by means of (weighted) cross-section aggregates such that asymptotically as the cross-section dimension (N) tends to infinity the differential effects of unobserved commond factors are eliminated. The estimation procedure has the advantage that it can be computed by OLS applied to an auxiliary regression where the observed regressors are augmented by cross sectional averages of the dependent variable and the individual specific regressors. It is shown that the proposed correlated common effects (CCE) estimators for the individual-specific regressors (and its pooled counterpart) are asymptotically unbiased as N approaches infinity, both when T (the time-series dimension) is fixed, and when N and T tend to infinity jointly. A generalization of these results to multi-factor structures is also provided. The estimation and inference in dynamic heterogenous panels with a residual factor structure will be addressed in a companion paper.

Keywords: cross section dependence; large panels; common correlated effects; heterogeneity; estimation and inference (search for similar items in EconPapers)
Date: 2003
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (25)

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Working Paper: Estimation and Inference in Large Heterogeneous Panels with Cross Section Dependence (2003)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_869

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