Order-Invariant Tests for Proper Calibration of Multivariate Density Forecasts
Dovern J, Manner H (2020)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2020
Journal
Book Volume: 35
Pages Range: 440-456
Article Number: 4
DOI: 10.1002/jae.2755
Open Access Link: https://onlinelibrary.wiley.com/doi/full/10.1002/jae.2755
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 and macroeconomic forecasts from a BVAR with time-varying parameters.
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Austria (AT)How to cite
APA:
Dovern, J., & Manner, H. (2020). Order-Invariant Tests for Proper Calibration of Multivariate Density Forecasts. Journal of Applied Econometrics, 35, 440-456. https://dx.doi.org/10.1002/jae.2755
MLA:
Dovern, Jonas, and Hans Manner. "Order-Invariant Tests for Proper Calibration of Multivariate Density Forecasts." Journal of Applied Econometrics 35 (2020): 440-456.
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