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.
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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number
4649.
Find related papers by JEL classification: C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
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