Evaluating Restricted Common Factor models for non-stationary data

Francesca Di Iorio and Stefano Fachin

No 2017/2, DSS Empirical Economics and Econometrics Working Papers Series from Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome

Abstract: We propose to evaluate restrictions on the loadings of approximate Factor models comparing the estimated number of factors of the unconstrained and constrained models. A difference between the two estimates is evidence against the constraints, which should thus be rejected. To take into account possible finite sample bias of the model selection procedure, we develop a bootstrap algorithm for the estimation of the probability of rejecting cor- rect constraints. For non-stationary factor models we show analytically that the algorithm is asymptotically valid, and by simulation that the evaluation procedure has good small sample properties.

Keywords: Non-stationary factor model; principal components; loadings restrictions; large data sets; stationary bootstrap. (search for similar items in EconPapers)
JEL-codes: C12 C33 C55 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2017-03
New Economics Papers: this item is included in nep-ecm and nep-ore
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http://www.dss.uniroma1.it/RePec/sas/wpaper/20172_DIF.pdf First version, 2017 (application/pdf)

Related works:
Journal Article: Evaluating restricted common factor models for non-stationary data (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:sas:wpaper:20172

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