Financial conditions and density forecasts for US output and inflation

Abstract

When do financial markets help in predicting economic activity? With incomplete markets, the link between financial and real economy is statedependent 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.JEL classification: C53, E32, E44, G01.

Together in bad times? Connectedness and spillovers in recession and boom

“…Following Alessandri and Mumtaz () in this section we introduce the TVAR model defined as:where…”

“…3.1.1 TVAR Model. Following Alessandri and Mumtaz (2017) in this section we introduce the TVAR model defined as:…”

Comparing the forecasting ability of financial conditions indices: The case of South Africa

“…To this end, recent literature has explored the development of financial conditions indices (FCIs). In this regard, the reader is referred to Koop and Korobilis (2014), Alessandri and Mumtaz (2014) and Thompson, Van Eyden and Gupta (forthcoming (a)) for an overview of the recent literature on FCIs. One of the key objectives of designing an FCI is for policymakers to use it as an early-warning tool of future crises.…”

“…In addition to the standard benchmarks such as a random-walk (RW), univariate autoregressive (AR) models and classical VAR models, we also look at Bayesian VARs, nonlinear logistic vector smooth transition autoregression (VSTAR) models, non-parametric (NP) and semi-parametric (SP) models, which incorporate the three different FCIs along with the three key variables to be predicted. Note that in case of the VSTAR and in the nonparametric part of the semi-parametric regressions, we use the FCI as the switch variable, or rather the source of nonlinearity as in Alessandri and Mumtaz (2014). The decision to look at models that capture the nonlinear effects of FCIs on the three macroeconomic variables emanate from the recent work by Balcilar, Thompson, Gupta and Van Eyden (2014).…”

Essays on business cycles in emerging markets

“…using an adaptive random walk Metropolis scheme. Following Alessandri and Mumtaz (2013), we set the mean of the prior as the mean value of y * c for each c with a value of 10 for variance.…”