Scanning Multivariate Conditional Densities with Probability Integral Transforms
AbstractThis paper introduces new ways to construct probability integral transforms of random vectors that complement the approach of Diebold, Hahn, and Tay (1999) for evaluating multivariate conditional density forecasts. Our approach enables us to "scan" multivariate densities in various di.erent ways. A simple bivariate normal example is given that illustrates how "scanning" a multivariate density from particular angles leads to tests with no power or high power. An empirical example is also given that applies several di.erent probability integral transforms to specification testing of Engle's (2002) dynamic conditional correlation model for multivariate financial returns time series with multivariate normal and t errors.
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Bibliographic InfoPaper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-369.
Length: 28 pages
Date of creation: Sep 2005
Date of revision:
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Other versions of this item:
- Isao Ishida, 2005. "Scanning Multivariate Conditional Densities with Probability Integral Transforms," CARF F-Series CARF-F-045, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
- NEP-ALL-2005-10-15 (All new papers)
- NEP-ECM-2005-10-15 (Econometrics)
- NEP-FOR-2005-10-15 (Forecasting)
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