Estimating the Number of Factors in Large Dimensional Factor Models
نویسنده
چکیده
This paper develops a new spectral approach to the estimation of the number of latent factors in large dimensional factor models. It shows that by imposing restrictions on the error terms we can derive a consistent procedure with improved finite sample performance in the presence of weak factors. The paper uses free probability theory to derive analytic expressions for the limiting moments of the spectral distribution, which greatly simplifies the computational burden. The new approach performs very well in a series of Monte Carlo simulations against the leading competing approaches. This procedure is also shown to provide realistic estimates of the number of latent factors in applications of factor models to asset pricing and data reduction of macroeconomic indicators. JEL: C33, G11, C46
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