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Federal Reserve Bank of St. Louis
Working Papers
An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts
Michael W. McCracken
Joseph McGillicuddy
Abstract

When constructing unconditional point forecasts, both direct- and iterated-multistep (DMS and IMS) approaches are common. However, in the context of producing conditional forecasts, IMS approaches based on vector autoregressions (VAR) are far more common than simpler DMS models. This is despite the fact that there are theoretical reasons to believe that DMS models are more robust to misspecification than are IMS models. In the context of unconditional forecasts, Marcellino, Stock, and Watson (MSW, 2006) investigate the empirical relevance of these theories. In this paper, we extend that work to conditional forecasts. We do so based on linear bivariate and trivariate models estimated using a large dataset of macroeconomic time series. Over comparable samples, our results reinforce those in MSW: the IMS approach is typically a bit better than DMS with significant improvements only at longer horizons. In contrast, when we focus on the Great Moderation sample we find a marked improvement in the DMS approach relative to IMS. The distinction is particularly clear when we forecast nominal rather than real variables where the relative gains can be substantial.


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Michael W. McCracken & Joseph McGillicuddy, An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts, Federal Reserve Bank of St. Louis, Working Papers 2017-40, 01 Nov 2017.
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Keywords: Prediction; forecasting; out-of-sample
DOI: 10.20955/wp.2017.040
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