Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/180285 
Year of Publication: 
2018
Series/Report no.: 
CESifo Working Paper No. 7023
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
Established tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms can be manipulated by changing the order of variables in the forecasting model. We derive order invariant tests. The new tests are applicable to densities of arbitrary dimensions and can deal with parameter estimation uncertainty and dynamic misspecification. Monte Carlo simulations show that they often have superior power relative to established approaches. We use the tests to evaluate GARCH-based multivariate density forecasts for a vector of stock market returns.
Subjects: 
density calibration
goodness-of-fit test
predictive density
Rosenblatt transformation
JEL: 
C12
C32
C52
C53
Document Type: 
Working Paper
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