| Literature DB >> 35707494 |
Christoph Behrens1, Christian Pierdzioch1, Marian Risse1.
Abstract
We use Bayesian additive regression trees to reexamine the efficiency of growth and inflation forecasts for Germany. To this end, we use forecasts of four leading German economic research institutes for the sample period from 1970 to 2016. We reject the strong form of forecast efficiency and find evidence against the weak form of forecast efficiency for longer-term growth and longer-term inflation forecasts. We cannot reject weak efficiency of short-term growth and inflation forecasts and of forecasts disaggregated at the institute level. We find that Bayesian additive regression trees perform significantly better than a standard linear efficiency-regression model in terms of forecast accuracy.Entities:
Keywords: Bayesian modeling; C53; E31; E32; E37; Forecast efficiency; regression trees
Year: 2019 PMID: 35707494 PMCID: PMC9041732 DOI: 10.1080/02664763.2019.1652253
Source DB: PubMed Journal: J Appl Stat ISSN: 0266-4763 Impact factor: 1.416