Constrained principal components estimation of large approximate factor models

Rachida Ouysse ()

No 2017-12, Discussion Papers from School of Economics, The University of New South Wales

Abstract: Principal components (PC) are fundamentally feasible for the estimation of large factor models because consistency can be achieved for any path of the panel dimensions. The PC method is however inefficient under cross-sectional dependence with unknown structure. The approximate factor model of Chamberlain and Rothschild [1983] imposes a bound on the amount of dependence in the error term. This article proposes a constrained principal components (Cn-PC) estimator that incorporates this restriction as external information in the PC analysis of the data. This estimator is computationally tractable. It doesn't require estimating large covariance matrices, and is obtained as PC of a regularized form of the data covariance matrix. The paper develops a convergence rate for the factor estimates and establishes asymptotic normality. In a Monte Carlo study, we find that the Cn-PC estimators have good small sample properties in terms of estimation and forecasting performances when compared to the regular PC and to the generalized PC method (Choi [2012]).

Keywords: High dimensionality; unknown factors; principal components; cross-sectional correlation; shrinkage regression; out-of-sample forecasting (search for similar items in EconPapers)
JEL-codes: C11 C13 C33 C53 C55 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2017-04
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
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Working Paper: Constrained principal components estimation of large approximate factor models (2019)
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