@article {Dueker:2010:0003-6846:2909,
title = "Forecasting macro variables with a Qual VAR business cycle turning point index",
journal = "Applied Economics",
parent_itemid = "infobike://routledg/raef",
publishercode ="routledg",
year = "2010",
volume = "42",
number = "23",
publication date ="2010-09-01T00:00:00",
pages = "2909-2920",
itemtype = "ARTICLE",
issn = "0003-6846",
eissn = "1466-4283",
url = "https://www.ingentaconnect.com/content/routledg/raef/2010/00000042/00000023/art00001",
doi = "doi:10.1080/00036840801964732",
author = "Dueker, Michael and Assenmacher-Wesche, Katrin",
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.",
}