On the efficiency of German growth forecasts: An empirical analysis using quantile random forests

Alexander Foltas and Christian Pierdzioch

No 21, Working Papers from German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin

Abstract: We use quantile random forests (QRF) to study the efficiency of the growth forecasts published by three leading German economic research institutes for the sample period from 1970 to 2017. To this end, we use a large array of predictors, including topics extracted by means of computational-linguistics tools from the business-cycle reports of the institutes, to model the information set of the institutes. We use this array of predictors to estimate the quantiles of the conditional distribution of the forecast errors made by the institutes, and then fit a skewed t-distribution to the estimated quantiles. We use the resulting density forecasts to compute the log probability score of the predicted forecast errors. Based on an extensive insample and out-of-sample analysis, we find evidence, particularly in the case of longer-term forecasts, against the null hypothesis of strongly efficient forecasts. We cannot reject weak efficiency of forecasts.

Keywords: Growth forecasts; Forecast efficiency; Quantile-random forests; Density forecasts (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-cmp, nep-for and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:pp1859:21

DOI: 10.18452/21910

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