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LOCALLY STATIONARY FACTOR MODELS: IDENTIFICATION AND NONPARAMETRIC ESTIMATION

Published online by Cambridge University Press:  07 June 2011

Giovanni Motta*
Affiliation:
Maastricht University
Christian M. Hafner
Affiliation:
Université Catholique de Louvain
Rainer von Sachs
Affiliation:
Université Catholique de Louvain
*
*Address correspondence to Giovanni Motta, Department of Quantitative Economics, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; e-mail: dr.giovannimotta@gmail.com.

Abstract

In this paper we propose a new approximate factor model for large cross-section and time dimensions. Factor loadings are assumed to be smooth functions of time, which allows considering the model as locally stationary while permitting empirically observed time-varying second moments. Factor loadings are estimated by the eigenvectors of a nonparametrically estimated covariance matrix. As is well known in the stationary case, this principal components estimator is consistent in approximate factor models if the eigenvalues of the noise covariance matrix are bounded. To show that this carries over to our locally stationary factor model is the main objective of our paper. Under simultaneous asymptotics (cross-section and time dimension go to infinity simultaneously), we give conditions for consistency of our estimators. A simulation study illustrates the performance of these estimators.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2011

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