Estimation and Inference in Large Heterogeneous Panels with Cross Section Dependence
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Authors
Pesaran, M. Hashem
Publication Date
2004-06-16Series
Cambridge Working Papers in Economics
Publisher
Faculty of Economics
Language
en_GB
Type
Working Paper
Metadata
Show full item recordCitation
Pesaran, M. H. (2004). Estimation and Inference in Large Heterogeneous Panels with Cross Section Dependence. https://doi.org/10.17863/CAM.5111
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 of unobserved common 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 ? 8, both when T (the time-series dimension) is fixed, and when N and T tend to infinity jointly. Further, the CCE estimators are asymptotically normal for T fixed as N ? 8, and when (N,T) ? 8, jointly provided vT/N ? 0 as (N,T) ? 8. A generalisation of these results to multi-factor structures is also provided.
Keywords
Classification-JEL: C12, C13, C33, cross section dependence, large panels, common correlated effects, heterogeneity, estimation and inference
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.5111
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