When do financial markets help in predicting economic activity? With incomplete markets, the link between the financial and the real economy is state-dependent and financial indicators may turn out to be useful particularly in forecasting "tail" macroeconomic events. We examine this conjecture by studying Bayesian predictive distributions for output growth and inflation in the United States between 1983 and 2012, comparing linear and nonlinear VAR models. We find that financial indicators significantly improve the accuracy of the distributions. Regime-switching models perform better than linear models thanks to their ability to capture changes in the transmission mechanism of financial shocks between ‘good times’ and ‘hard times’. Such models could have sent a credible advance warning of the Great Recession. Furthermore, the discrepancies between models are themselves predictable, which allows the forecaster to formulate reasonable real-time guesses as to which model is likely to be more accurate in the near future.
Published in 2017 in: Review of Economic Dynamics, v. 24, pp. 66-78