On the use of flexible rolling-window estimation for macroeconomic forecasting

We propose a flexible rolling-window estimation procedure which improves the forecasting accuracy of misspecified linear autoregressive models. The method assigns different weights to data points in the observed sample, which can be useful in the presence of data generating processes featuring structural breaks, complex nonlinearities, or other time-varying properties not easily captured by model design. We show how the window can be regularized by means of cross-validation. In a series of Monte Carlo experiments, we reveal that the estimation method can significantly improve the forecasting accuracy of misspecified models. In our empirical study, we show for a set of key macroeconomic indicators that we improve forecasting accuracy by assigning greater weight to periods with similar business-cycle regimes.