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Density forecast evaluation in unstable environments

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  • González-Rivera, Gloria
  • Sun, Yingying

Abstract

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.

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  • González-Rivera, Gloria & Sun, Yingying, 2017. "Density forecast evaluation in unstable environments," International Journal of Forecasting, Elsevier, vol. 33(2), pages 416-432.
  • Handle: RePEc:eee:intfor:v:33:y:2017:i:2:p:416-432
    DOI: 10.1016/j.ijforecast.2016.10.003
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    1. Emilio Zanetti Chini, 2018. "Forecasters’ utility and forecast coherence," CREATES Research Papers 2018-23, Department of Economics and Business Economics, Aarhus University.
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    3. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.

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