An empirical investigation of direct and iterated multistep conditional forecasts
| Autor: | Michael W. McCracken, Joseph McGillicuddy |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Economics and Econometrics
Great Moderation media_common.quotation_subject 05 social sciences Monetary policy Bivariate analysis Interest rate Economic data Economic indicator Iterated function 0502 economics and business Economics Econometrics 050207 economics Social Sciences (miscellaneous) Stock (geology) 050205 econometrics media_common |
| Zdroj: | Journal of Applied Econometrics. 34:181-204 |
| ISSN: | 1099-1255 0883-7252 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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