An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts
Michael McCracken () and
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Joseph McGillicuddy: Federal Reserve Bank of St. Louis
No 2017-40, Working Papers from Federal Reserve Bank of St. Louis
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.
Keywords: Prediction; forecasting; out-of-sample (search for similar items in EconPapers)
JEL-codes: C12 C32 C52 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Journal Article: An empirical investigation of direct and iterated multistep conditional forecasts (2019)
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