Robust Evaluation of Multivariate Density Forecasts

Konferenzbeitrag

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 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: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2016: Demographischer Wandel - Session: Forecasting and Information Transmission ; No. B01-V2

Classification and Topics

• Classification: Wirtschaft ; Hypothesis Testing: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Forecasting Models; Simulation Methods

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Dovern, Jonas ; Manner, Hans
• Event: Veröffentlichung
• (who): ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft
• (where): Kiel und Hamburg
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/145547
• Last update: 10.03.2025, 11:42 AM CET