Working Paper: CEPR ID: DP8234
Authors: Pierre Gurin; Massimiliano Marcellino
Abstract: This paper introduces a new regression model - Markov-switching mixed data sampling (MS-MIDAS) - that incorporates regime changes in the parameters of the mixed data sampling (MIDAS) models and allows for the use of mixed-frequency data in Markov-switching models. After a discussion of estimation and inference for MS-MIDAS, and a small sample simulation based evaluation, the MS-MIDAS model is applied to the prediction of the US and UK economic activity, in terms both of quantitative forecasts of the aggregate economic activity and of the prediction of the business cycle regimes. Both simulation and empirical results indicate that MSMIDAS is a very useful specification.
Keywords: business cycle; forecasting; mixed-frequency data; nonlinear models; nowcasting
JEL Codes: C22; C53; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| MSMIDAS model (C69) | accuracy of predictions (C52) |
| high-frequency variables (C58) | forecast accuracy (C53) |
| regime changes (P39) | predictive ability of high-frequency variables (C58) |
| MSMIDAS model (C69) | better forecasts (C53) |