Inversion copulas from nonlinear state space models with an application to inflation forecasting

Michael Stanley Smith, Worapree Maneesoonthorn

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14 Citations (Scopus)

Abstract

We propose the construction of copulas through the inversion of nonlinear state space models. These copulas allow for new time series models that have the same serial dependence structure as a state space model, but with an arbitrary marginal distribution, and flexible density forecasts. We examine the time series properties of the copulas, outline serial dependence measures, and estimate the models using likelihood-based methods. Copulas constructed from three example state space models are considered: a stochastic volatility model with an unobserved component, a Markov switching autoregression, and a Gaussian linear unobserved component model. We show that all three inversion copulas with flexible margins improve the fit and density forecasts of quarterly U.S. broad inflation and electricity inflation.

Original languageEnglish
Pages (from-to)389-407
Number of pages19
JournalInternational Journal of Forecasting
Volume34
Issue number3
DOIs
Publication statusPublished - Jul 2018
Externally publishedYes

Keywords

  • Bayesian methods
  • Copulas
  • Density forecasts
  • Inflation forecasting
  • Nonlinear serial dependence
  • Nonlinear time series

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