Density forecast evaluation in unstable environments
Gloria Gonzalez-Rivera () and
International Journal of Forecasting, 2017, vol. 33, issue 2, 416-432
We propose a density forecast evaluation method in the presence of instabilities, which are defined as breaks in any conditional moment of interest and/or in the functional form of the conditional density of the process. Within the framework of the autocontour-based tests proposed by González-Rivera et al. (2011) and González-Rivera and Sun (2015), we construct Sup- and Ave-type tests, calculated over a collection of subsamples in the evaluation period. These tests have asymptotic distributions that are nuisance-parameter free and they are correctly sized and very powerful for detecting breaks in the parameters of the conditional mean and conditional variance. A power comparison with the tests of Rossi and Sekhposyan (2013) shows that our tests are more powerful across the models considered in their work. We analyze the stability of a dynamic Phillips curve and find that the best one-step-ahead density forecast of changes in inflation is generated by a Markov switching model that allows state shifts in the mean and variance of inflation changes as well as in the coefficient that links inflation and unemployment.
Keywords: Generalized autocontour-based testing; Structural breaks; Phillips curve (search for similar items in EconPapers)
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Working Paper: Density Forecast Evaluation in Unstable Environments (2016)
Working Paper: Density Forecast Evaluation in Unstable Environments (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:33:y:2017:i:2:p:416-432
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