Estimation and Inference for High Dimensional Factor Model with Regime Switching

Giovanni Urga and Fa Wang

MPRA Paper from University Library of Munich, Germany

Abstract: This paper proposes maximum (quasi)likelihood estimation for high dimensional factor models with regime switching in the loadings. The model para- meters are estimated jointly by the EM (expectation maximization) algorithm, which in the current context only requires iteratively calculating regime prob- abilities and principal components of the weighted sample covariance matrix. When regime dynamics are taken into account, smoothed regime probabilities are calculated using a recursive algorithm. Consistency, convergence rates and limit distributions of the estimated loadings and the estimated factors are established under weak cross-sectional and temporal dependence as well as heteroscedasticity. It is worth noting that due to high dimension, regime switching can be identified consistently after the switching point with only one observation. Simulation results show good performance of the proposed method. An application to the FRED-MD dataset illustrates the potential of the proposed method for detection of business cycle turning points.

Keywords: Factor model; Regime switching; Maximum likelihood; High dimension; EM algorithm; Turning points (search for similar items in EconPapers)
JEL-codes: C13 C38 C55 (search for similar items in EconPapers)
Date: 2022-05-08, Revised 2023-04-10
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