Macroeconomic Forecasting Using Diffusion Indexes

TLDR

The study investigates forecasting macroeconomic time series variables using a large number of predictors. The authors use principal component analysis to condense 215 predictors into a few indexes, then estimate an approximate dynamic factor model to generate 6‑, 12‑, and 24‑month forecasts for eight monthly U.S. macroeconomic series from 1970 to 1998.

Abstract

This article studies forecasting a macroeconomic time series variable using a large number of predictors. The predictors are summarized using a small number of indexes constructed by principal component analysis. An approximate dynamic factor model serves as the statistical framework for the estimation of the indexes and construction of the forecasts. The method is used to construct 6-, 12-, and 24-monthahead forecasts for eight monthly U.S. macroeconomic time series using 215 predictors in simulated real time from 1970 through 1998. During this sample period these new forecasts outperformed univariate autoregressions, small vector autoregressions, and leading indicator models.