Factor Models with Local Factors — Determining the Number of Relevant Factors

By S. Freyaldenhoven. Working paper (Federal Reserve Bank of Philadelphia), 2021

54 citations recorded by the source corpus.

Abstract We extend the theory on factor models by incorporating “local” factors into the model. Local factors affect only an unknown subset of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in applications. We derive which factors are pervasive enough to be economically important and which factors are pervasive enough to be estimable using the common principal component estimator. We then introduce a new class of estimators to determine the number of those relevant factors. Unlike existing estimators, our estimators use not only the eigenvalues of the covariance matrix, but also its eigenvectors. We find that incorporating partial sums of the eigenvectors into our estimators leads to significant gains in performance in simulations.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d125bd19404224471bbb2d87f7362090829f4b8b

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In Economics and Econometrics

  1. Serena Ng
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In Statistics and Probability

  1. Serena Ng
  2. Jushan Bai
  3. Mark W. Watson
  4. Liangjun Su
  5. James H. Stock
  6. In Choi
  7. Jianqing Fan
  8. Pierre Perron

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