Are There Any Reliable Leading Indicators for US Inflation and GDP Growth?

In this paper we evaluate the relative merits of three approaches to information extraction
from a large data set for forecasting, namely, the use of an automated model selection
procedure, the adoption of a factor model, and single-indicator-based forecast pooling. The
comparison is conducted using a large set of indicators for forecasting US inflation and GDP
growth. We also compare our large set of leading indicators with purely autoregressive
models, using an evaluation procedure that is particularly relevant for policy making. The
evaluation is conducted both ex-post and in a pseudo real time context, for several forecast
horizons, and using both recursive and rolling estimation. The results indicate a preference for
simple forecasting tools, with a good relative performance of pure autoregressive models, and
substantial instability in the leading characteristics of the indicators.