English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper proposes maximum (quasi)likelihood estimation for high dimensionalfactor models with regime switching in the loadings. The model parameters areestimated jointly by the EM (expectation maximization) algorithm, which in thecurrent context only requires iteratively calculating regime probabilities andprincipal components of the weighted sample covariance matrix. When regimedynamics are taken into account, smoothed regime probabilities are calculatedusing a recursive algorithm. Consistency, convergence rates and limitdistributions of the estimated loadings and the estimated factors areestablished under weak cross-sectional and temporal dependence as well asheteroscedasticity. It is worth noting that due to high dimension, regimeswitching can be identified consistently after the switching point with onlyone observation. Simulation results show good performance of the proposedmethod. An application to the FRED-MD dataset illustrates the potential of theproposed method for detection of business cycle turning points.

Estimation and Inference for High Dimensional Factor Model with Regime Switching