How far can we forecast? Statistical tests of the predictive content

Jörg Breitung () and Malte Knüppel

No 07/2018, Discussion Papers from Deutsche Bundesbank

Abstract: Forecasts are useless whenever the forecast error variance fails to be smaller than the unconditional variance of the target variable. This paper develops tests for the null hypothesis that forecasts become uninformative beyond some limiting forecast horizon h. Following Diebold and Mariano (DM, 1995) we propose a test based on the comparison of the mean-squared error of the forecast and the sample variance. We show that the resulting test does not possess a limiting normal distribution and suggest two simple modifications of the DM-type test with different limiting null distributions. Furthermore, a forecast encompassing test is developed that tends to better control the size of the test. In our empirical analysis, we apply our tests to macroeconomic forecasts from the survey of Consensus Economics. Our results suggest that forecasts of macroeconomic key variables are barely informative beyond 2-4 quarters ahead.

Keywords: Hypothesis Testing; Predictive Accuracy; Informativeness (search for similar items in EconPapers)
JEL-codes: C12 C32 C53 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Journal Article: How far can we forecast? Statistical tests of the predictive content (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bubdps:072018

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