Forecasting the Distribution of Economic Variables in a Data-Rich Environment

Abstract

In this paper we investigate the relevance of considering a large number of macroeconomic indicators to forecast the complete distribution of a variable. The baseline time series model is a semi-parametric specification based on the Quantile Auto-Regressive (QAR) model that assumes that the quantiles depend on the lagged values of the variable. We then augment the time series model with macroeconomic information from a large dataset by including principal components or a subset of variables selected by LASSO. We forecast the distribution of the h-month growth rate for four economic variables from 1975 to 2011 and evaluate the forecast accuracy with score functions tailored to evaluate quantile, areas of the distribution, and intervals. The results for the output and employment measures indicate that the multivariate models outperform the time series forecasts, in particular at short horizons and low quantiles, while for the inflation variables the improved performance occurs mostly at the one-year horizon. We also consider an application of the distribution forecasts to predict the probability of a future decline in output and employment and illustrate their practical use at three dates during the last recession.