Nonlinear Forecasting Using Large Datasets: Evidences on US and Euro Area Economies

The primary objective of this paper is to propose two nonlinear extensions for macroeconomic forecasting using large datasets. First, we propose an alternative technique for factor estimation, i.e., kernel principal component analysis, which allows the factors to have a nonlinear relationship to the input variables. Second, we propose artificial neural networks as an alternative to the factor augmented linear forecasting equation. These two extensions allow us to determine whether, in general, there is empirical evidence in favor of nonlinear methods and, in particular, to verify whether the nonlinearity occurs in the estimation of the factors or in the functional form that links the target variable to the factors. In an effort to verify the empirical performances of the methods proposed, we conducted several pseudo forecasting exercises on the industrial production index and consumer price index for the Euro area and US economies. These methods were employed to construct the forecasts at 1-, 3-, 6-, and 12-month horizons using a large dataset containing 259 predictors for the Euro area and 131 predictors for the US economy. The results obtained from the empirical study suggest that the estimation of nonlinear factors, using kernel principal components, significantly improves the quality of forecasts compared to the linear method, while the results for artificial neural networks have the same forecasting ability as the factor augmented linear forecasting equation.