Regime transplants in GDP growth forecasting: A recipe for better predictions?
Formal testing and estimation of nonlinear relations require a substantial number of observations which are typically lacking in annual models. In this paper, a novel two-step procedure is introduced to model nonlinearities in yearly asset-price based leading indicator models for growth. In the first step, quarterly data are explored to test for the presence of regime switches, the identif ication of transition variables and estimation of the accompanying thresholds. In the second step, we implement the quarterly thresholds in the annual indicator models. Results for the US and the Netherlands show that the annual forecasts improve compared to the linear model, despite the poor out-of-sample performance of the quarterly regime switching models.
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