Financial Conditions and Density Forecasts for US Output and Inflation
Piergiorgio Alessandri and
No 715, Working Papers from Queen Mary University of London, School of Economics and Finance
When do financial markets help in predicting economic activity? With incomplete markets, the link between financial and 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 US 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 and bad times. Such models could have sent a credible advance warning ahead of the Great Recession. Furthermore, the discrepancies between models are themselves predictable, which allows the forecaster to formulate reasonable real-time guesses on which model is likely to be more accurate in the next future.
Keywords: Financial frictions; Predictive densities; Great Recession; Threshold VAR (search for similar items in EconPapers)
JEL-codes: C53 E32 E44 G01 (search for similar items in EconPapers)
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Journal Article: Financial conditions and density forecasts for US output and inflation (2017)
Working Paper: Financial conditions and density forecasts for US output and inflation (2014)
Working Paper: Financial Conditions and Density Forecasts for US Output and Inflation (2014)
Working Paper: Financial conditions and density forecasts for US Output and inflation (2013)
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