Order‐invariant tests for proper calibration of multivariate density forecasts

Jonas Dovern and Hans Manner

Journal of Applied Econometrics, 2020, vol. 35, issue 4, 440-456

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 generalized autoregressive conditional heteroskedasticity‐based multivariate density forecasts for a vector of stock market returns and macroeconomic forecasts from a Bayesian vector autoregression with time‐varying parameters.

Date: 2020
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Citations: View citations in EconPapers (5)

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https://doi.org/10.1002/jae.2755

Related works:
Working Paper: Order Invariant Tests for Proper Calibration of Multivariate Density Forecasts (2018)
Working Paper: Order Invariant Tests for Proper Calibration of Multivariate Density Forecasts (2018)
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