Economics at your fingertips  

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 parameters are estimated jointly by EM algorithm, which in the current context only requires iteratively calculating regime probabilities 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 right after the switching point with only one observation. Simulation results show good performance of the proposed method. An application to the FRED-MD dataset demonstrates the potential of the proposed method for quick 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: 2022-05-07
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) original version (application/pdf)

Related works:
Working Paper: Estimation and Inference for High Dimensional Factor Model with Regime Switching (2023) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().

Page updated 2023-04-11
Handle: RePEc:pra:mprapa:113172