Previous work shows that financial series contain important
information on the current state of the economy and expectations for the
future. Further, numerous papers find links between the financial sectors and
the real sectors of the economy. We add to those findings by exploring whether
financial variables help to forecast the
growth rate of industrial production.
We evaluate linear and nonlinear forecasting methods using out-of-sample
forecasting performance. We compare
autoregressive models, error-correcting
models, and multivariate nearest-neighbor
regression models, and we explore the
use of optimally combined forecasts. We find that no single forecasting
technique appears to outperform any other method, and the evidence for
persistent nonlinear patterns is weak.
However, although nonparametric methods
do not offer significant improvements in
forecast accuracy by themselves, more accurate forecasts are obtained when the
nonlinear forecasts are optimally combined. Our results indicate that
financial information can statistically improve the forecasts of the real
sector in these combined models, but the magnitude of the improvement in
root-mean-squared error is small.