Working Paper: CEPR ID: DP4649
Authors: Graham Elliott; Allan G. Timmermann
Abstract: This Paper proposes a new forecast combination method that lets the combination weights be driven by regime switching in a latent state variable. An empirical application that combines forecasts from survey data and time series models finds that the proposed regime switching combination scheme performs well for a variety of macroeconomic variables. Monte Carlo simulations shed light on the type of data generating processes for which the proposed combination method can be expected to perform better than a range of alternative combination schemes. Finally, we show how time-variations in the combination weights arise when the target variable and the predictors share a common factor structure driven by a hidden Markov process.
Keywords: forecast combination; Markov switching; survey data; time varying combination weights
JEL Codes: C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Regime Switching Method (C22) | Improved Forecasting Performance (C53) |
| Regime Switching Method (C22) | Forecast Accuracy (C53) |
| Shared Factor Structure (C38) | Regime Switching Method Performance (C22) |
| Hidden Markov Process (C69) | Shared Factor Structure (C38) |