Evaluating density forecasts via the copula approach

Evaluating Density Forecasts via the Copula Approach

In this paper, we develop parametric tests for the correct density forecasts. Similar to Berkowitz (2001), we construct our tests by nesting a series of i.i.d. uniform random variables in a class of stationary Markov processes. Unlike Berkowitz (2001), the class of Markov processes in this paper is constructed via the copula approach, which allows the separate modeling of the marginal distribut...

Evaluating density forecasts: a comment

Evaluating the Bank of England Density Forecasts of Inflation

We consider evaluating the UK Monetary Policy Committee’s inflation density forecasts using probability integral transform goodness-of-fit tests and measures related to a decision/cost based approach. In implementing the decision-based approach a number of difficulties arise in the absence of full information on payoffs, and when the actual and forecast inflation probabilities may depend on the...

Comparing the accuracy of multivariate density forecasts in selected regions of the copula support

This paper develops a testing framework for comparing the predictive accuracy of competing multivariate density forecasts with different predictive copulas, focusing on specific parts of the copula support. The tests are framed in the context of the Kullback– Leibler Information Criterion, using (out-of-sample) conditional likelihood and censored likelihood in order to focus the evaluation on t...

Comparing the Accuracy of Copula- Based Multivariate Density Forecasts in Selected Regions of Support

This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample) conditional likelihood and censored likelihood in order to focus the evaluation on the region of interest. Mo...