Behrens Christoph, Pierdzioch Christian, Risse Marian
Department of Economics, Helmut Schmidt University, Hamburg, Germany.
J Appl Stat. 2019 Aug 8;47(4):698-723. doi: 10.1080/02664763.2019.1652253. eCollection 2020.
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.
我们使用贝叶斯加法回归树重新审视德国增长和通胀预测的效率。为此，我们使用了四家德国主要经济研究机构在1970年至2016年样本期内的预测。我们拒绝预测效率的强形式，并发现有证据反对长期增长和长期通胀预测的弱形式。我们不能拒绝短期增长和通胀预测以及按机构层面细分的预测的弱效率。我们发现，在预测准确性方面，贝叶斯加法回归树的表现明显优于标准线性效率回归模型。
