An empirical investigation of direct and iterated multistep conditional forecasts

Michael McCracken and Joseph T. McGillicuddy

Journal of Applied Econometrics, 2019, vol. 34, issue 2, 181-204

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 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 et al. (Journal of Econometrics, 2006, 135, 499–526) 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 Marcellino et al.: 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.

Date: 2019
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Citations: View citations in EconPapers (10)

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https://doi.org/10.1002/jae.2668

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Working Paper: An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts (2017)
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