Testing a Large Number of Hypotheses in Approximate Factor Models

Dante Amengual and Luca Repetto

Working Papers from CEMFI

Abstract: We propose a method to test hypotheses in approximate factor models when the number of restrictions under the null hypothesis grows with the sample size. We use a simple test statistic, based on the sums of squared residuals of the restricted and the unrestricted versions of the model, and derive its asymptotic distribution under different assumptions on the covariance structure of the error term. We show how to standardize the test statistic in the presence of both serial and cross-section correlation to obtain a standard normal limiting distribution. We provide estimators for those quantities that are easy to implement. Finally, we illustrate the small sample performance of these testing procedures through Monte Carlo simulations and apply them to reconsider Reis and Watson (2010)'s hypothesis of existence of a pure inflation factor in the US economy.

Keywords: Approximate factor model; hypothesis testing; principal components; large model analysis; large data sets; inflation. (search for similar items in EconPapers)
JEL-codes: C12 C33 C55 (search for similar items in EconPapers)
Date: 2014-12
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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