Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

On the consistency of the two-step estimates of the MS-DFM: a Monte Carlo study

Abstract : The Markov-Switching Dynamic Factor Model (MS-DFM) has been used in different applications, notably in the business cycle analysis. When the cross-sectional dimension of data is high, the Maximum Likelihood estimation becomes unfeasible due to the excessive number of parameters. In this case, the MS-DFM can be estimated in two steps, which means that in the first step the common factor is extracted from a database of indicators, and in the second step the Markov-Switching autoregressive model is fit to this extracted factor. The validity of the two-step method is conventionally accepted, although the asymptotic properties of the two-step estimates have not been studied yet. In this paper we examine their consistency as well as the small-sample behavior with the help of Monte Carlo simulations. Our results indicate that the two-step estimates are consistent when the number of cross-section series and time observations is large, however, as expected, the estimates and their standard errors tend to be biased in small samples.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

Cited literature [38 references]  Display  Hide  Download
Contributor : Caroline Bauer Connect in order to contact the contributor
Submitted on : Monday, September 25, 2017 - 3:05:50 PM
Last modification on : Monday, July 11, 2022 - 9:54:57 AM
Long-term archiving on: : Tuesday, December 26, 2017 - 1:49:13 PM


Files produced by the author(s)


  • HAL Id : halshs-01592863, version 1


Catherine Doz, Anna Petronevich. On the consistency of the two-step estimates of the MS-DFM: a Monte Carlo study. 2017. ⟨halshs-01592863⟩



Record views


Files downloads