Order Invariant Tests for Proper Calibration of Multivariate Density Forecasts

Jonas Dovern and Hans Manner

No 7023, CESifo Working Paper Series from CESifo

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.

Keywords: density calibration; goodness-of-fit test; predictive density; Rosenblatt transformation (search for similar items in EconPapers)
JEL-codes: C12 C32 C52 C53 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ets and nep-for
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Related works:
Journal Article: Order‐invariant tests for proper calibration of multivariate density forecasts (2020)
Working Paper: Order Invariant Tests for Proper Calibration of Multivariate Density Forecasts (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_7023

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