Estimating high-dimensional Markov-switching VARs

TL;DR

This paper introduces sparse penalized maximum likelihood estimators for high-dimensional Markov-switching VAR models, enabling consistent estimation and variable selection even when the number of parameters exceeds the sample size.

Contribution

It develops and demonstrates the effectiveness of Lasso and SCAD penalized estimators for high-dimensional MS-VARs, including a modified EM algorithm and empirical validation.

Findings

Estimators are estimation consistent.

SCAD estimator achieves variable selection consistency.

Large MS-VARs outperform traditional predictors in out-of-sample tests.

Abstract

Maximum likelihood estimation of large Markov-switching vector autoregressions (MS-VARs) can be challenging or infeasible due to parameter proliferation. To accommodate situations where dimensionality may be of comparable order to or exceeds the sample size, we adopt a sparse framework and propose two penalized maximum likelihood estimators with either the Lasso or the smoothly clipped absolute deviation (SCAD) penalty. We show that both estimators are estimation consistent, while the SCAD estimator also selects relevant parameters with probability approaching one. A modified EM-algorithm is developed for the case of Gaussian errors and simulations show that the algorithm exhibits desirable finite sample performance. In an application to short-horizon return predictability in the US, we estimate a 15 variable 2-state MS-VAR(1) and obtain the often reported counter-cyclicality in predictability. The variable selection property of our estimators helps to identify predictors that contribute strongly to predictability during economic contractions but are otherwise irrelevant in expansions. Furthermore, out-of-sample analyses indicate that large MS-VARs can significantly outperform "hard-to-beat" predictors like the historical average.