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A test of the joint efficiency of macroeconomic forecasts using multivariate random forests
Author(s) -
Behrens Christoph,
Pierdzioch Christian,
Risse Marian
Publication year - 2018
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2520
Subject(s) - mahalanobis distance , econometrics , multivariate statistics , joint (building) , inflation (cosmology) , consensus forecast , statistics , economics , computer science , mathematics , engineering , architectural engineering , physics , theoretical physics
We contribute to recent research on the joint evaluation of the properties of macroeconomic forecasts in a multivariate setting. The specific property of forecasts that we are interested in is their joint efficiency. We study the joint efficiency of forecasts by means of multivariate random forests, which we use to model the links between forecast errors and predictor variables in a forecaster's information set. We then use permutation tests to study whether the Mahalanobis distance between the predicted forecast errors for the growth and inflation forecasts of four leading German economic research institutes and actual forecast errors is significantly smaller than under the null hypothesis of forecast efficiency. We reject joint efficiency in several cases, but also document heterogeneity across research institutes with regard to the joint efficiency of their forecasts.