Order Invariant Evaluation of Multivariate Density Forecasts

Arbeitspapier

We derive new tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms. These tests have the advantage that they i) do not depend on the ordering of variables in the forecasting model, ii) are applicable to densities of arbitrary dimensions, and iii) have superior power relative to existing approaches. We furthermore develop adjusted tests that allow for estimated parameters and, consequently, can be used as in-sample specification tests. We demonstrate the problems of existing tests and how our new approaches can overcome those using Monte Carlo Simulation as well as two applications based on multivariate GARCH-based models for stock market returns and on a macroeconomic Bayesian vectorautoregressive model.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Discussion Paper Series ; No. 608

Classification and Topics

• Classification: Wirtschaft
• Subject: density calibration ; goodness-of-fit test ; predictive density ; Rosenblatt transformation ; Stetige Verteilung ; Statistische Methodenlehre

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Dovern, Jonas ; Manner, Hans
• Event: Veröffentlichung
• (who): University of Heidelberg, Department of Economics
• (where): Heidelberg
• (when): 2016

Further information

• DOI: doi:10.11588/heidok.00020376
• Handle: http://hdl.handle.net/10419/162951
• URN: urn:nbn:de:bsz:16-heidok-203762
• Last update: 10.03.2025, 11:42 AM CET