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

Michael Smith () and Worapree Maneesoonthorn

International Journal of Forecasting, 2018, vol. 34, issue 3, 389-407

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.

Keywords: Copulas; Nonlinear time series; Bayesian methods; Nonlinear serial dependence; Density forecasts; Inflation forecasting (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:34:y:2018:i:3:p:389-407

DOI: 10.1016/j.ijforecast.2018.01.002

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