Abstract:
In this paper we test the forecasting ability of three estimated financial conditions indices (FCIs) with respect to key macroeconomic variables of output growth, inflation and interest rates. We do this by forecasting the aforementioned macroeconomic variables based on the information contained in the three
alternative FCIs using a Bayesian VAR (BVAR), nonlinear logistic vector smooth transition auto regression (VSTAR) and non parametric (NP) and semi-parametric (SP) regressions, and compare the results with the standard benchmarks of random-walk, uni variate auto regressive and classical VAR models. The three FCIs are constructed using rolling-window principal component analysis (PCA), dynamic model averaging
(DMA) in the context of a time-varying parameter factor-augmented vector auto regressive (TVP-FAVAR) model, and a time-varying parameter vector auto regressive (TVP-VAR) model with constant factor
loadings. Our results suggest that the VSTAR model performs best in the case of forecasting manufacturing production and inflation, while a SP specification proves to be the best for forecasting the
interest rate. More importantly, statistics testing for significant differences in forecast errors across models corroborate the finding of superior predictive ability of the nonlinear models.
