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Forecasting macro variables with a Qual VAR business cycle turning point index

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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.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/00036840801964732

Affiliations: 1: Federal Reserve Bank of St. Louis, St. Louis, MO 63166, USA 2: Swiss National Bank, CH 8022 Zurich, Switzerland

Publication date: September 1, 2010

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