 Forecasting macro variables with a Qual VAR business cycle turning point indexMichael Dueker and Katrin Assenmacher Applied Economics, 2010, vol. 42, issue 23, 2909-2920 Abstract: One criticism of Vector Autoregression (VAR) forecasting is that macroeconomic variables tend not to behave as linear functions of their own past around business cycle turning points. A large amount of literature therefore focuses on nonlinear forecasting models, such as Markov switching models, which only indirectly capture the relation with turning points. This article investigates a direct approach to using information on turning points from the National Bureau of Economic Research (NBER) chronology to model and forecast macroeconomic data. Our Qual VAR model includes a truncated normal latent business cycle index that is negative during NBER recessions and positive during expansions. We motivate our forecasting exercise by demonstrating that if starting from a linear specification, a truncated normal variable is an omitted variable, then forecasts of the remaining variables will become nonlinear functions of their own past. We apply the Qual VAR model to recursive out-of-sample forecasting and find that the Qual VAR improves on out-of-sample forecasts from a standard VAR. Date: 2010 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:42:y:2010:i:23:p:2909-2920 Ordering information: This journal article can be ordered from DOI: 10.1080/00036840801964732 Access Statistics for this article Applied Economics is currently edited by Anita Phillips More articles in Applied Economics from Taylor & Francis Journals |
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