TY - JOUR
T1 - Inversion copulas from nonlinear state space models with an application to inflation forecasting
AU - Smith, Michael Stanley
AU - Maneesoonthorn, Worapree
N1 - Funding Information:
The work of Michael Smith was supported by Australian Research Council Grant FT110100729 . We thank the Editor, the Associate Editor and two anonymous referees for positive and constructive comments that helped to improve the paper.
Publisher Copyright:
© 2018 International Institute of Forecasters
PY - 2018/7
Y1 - 2018/7
N2 - 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.
AB - 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.
KW - Bayesian methods
KW - Copulas
KW - Density forecasts
KW - Inflation forecasting
KW - Nonlinear serial dependence
KW - Nonlinear time series
UR - http://www.scopus.com/inward/record.url?scp=85044528904&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2018.01.002
DO - 10.1016/j.ijforecast.2018.01.002
M3 - Article
AN - SCOPUS:85044528904
SN - 0169-2070
VL - 34
SP - 389
EP - 407
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 3
ER -