What Causes The Forecasting Failure of Markov-Switching Models? A Monte Carlo Study
-
Marie Bessec
This paper explores the forecasting abilities of Markov-Switching models. Although MS models generally display a superior in-sample fit relative to linear models, the gain in prediction remains small. We confirm this result using simulated data for a wide range of specifications by applying several tests of forecast accuracy and encompassing robust to nested models. In order to explain this poor performance, we use a forecasting error decomposition. We identify four components and derive their analytical expressions in different MS specifications. The relative contribution of each source is assessed through Monte Carlo simulations. We find that the main source of error is due to the misclassification of future regimes.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Artikel in diesem Heft
- Article
- Economic Growth and Revealed Social Preference
- A Test of the Martingale Hypothesis
- Solving Ramsey Problems with Nonlinear Projection Methods
- A Note on the Hiemstra-Jones Test for Granger Non-causality
- Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model
- What Causes The Forecasting Failure of Markov-Switching Models? A Monte Carlo Study
- Joint Tests for Non-linearity and Long Memory: The Case of Purchasing Power Parity
Artikel in diesem Heft
- Article
- Economic Growth and Revealed Social Preference
- A Test of the Martingale Hypothesis
- Solving Ramsey Problems with Nonlinear Projection Methods
- A Note on the Hiemstra-Jones Test for Granger Non-causality
- Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model
- What Causes The Forecasting Failure of Markov-Switching Models? A Monte Carlo Study
- Joint Tests for Non-linearity and Long Memory: The Case of Purchasing Power Parity
