MULTIVARIATE NONLINEAR FORECASTING - USING FINANCIAL INFORMATION TO FORECAST THE REAL SECTOR

Previous work shows that financial series contain important informatio n 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 explorin g whether financial variables help to forecast the growth rate of indu strial production. We evaluate linear and nonlinear forecasting method s using out-of-sample forecasting performance. We compare autoregressi ve models, error-correcting models, and multivariate nearest-neighbor regression models, and we explore the use of optimally combined foreca sts. We find that no single forecasting technique appears to outperfor m any other method, and the evidence for persistent nonlinear patterns is weak. However, although nonparametric methods do not offer signifi cant improvements in forecast accuracy by themselves, more accurate fo recasts are obtained when the nonlinear forecasts are optimally combin ed. 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.