Locally Stationary Factor Models: Identification and Nonparametric Estimation

G. Motta, C.M. Hafner, R. von Sachs

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)1279-1319
Number of pages41
JournalEconometric Theory
Volume27
Issue number6
DOIs
Publication statusPublished - Dec 2011

Keywords

  • DYNAMIC-FACTOR MODEL
  • TIME-SERIES
  • BANDWIDTH CHOICE
  • NONSTATIONARITIES
  • ARBITRAGE
  • VARIANCE
  • NUMBER
  • RISK

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