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Efficient estimation of conditionally linear and Gaussian state space models

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  • Moura, Guilherme V.
  • Turatti, Douglas Eduardo

Abstract

An efficient estimation procedure for conditionally linear and Gaussian state space models is developed. Efficient importance sampling together with a Rao-Blackwellization step are used to construct a highly efficient estimation method that produces continuous approximations to the likelihood function, greatly enhancing simulated maximum likelihood estimation. An application where the unobserved component stochastic volatility model is used to model inflation is proposed and parameter estimates for all G7 countries are shown to be statistically different from calibrated values used in the literature. The estimated model is used to forecast inflation of these countries.

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  • Moura, Guilherme V. & Turatti, Douglas Eduardo, 2014. "Efficient estimation of conditionally linear and Gaussian state space models," Economics Letters, Elsevier, vol. 124(3), pages 494-499.
    • Handle: RePEc:eee:ecolet:v:124:y:2014:i:3:p:494-499
    DOI: 10.1016/j.econlet.2014.07.019
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    References listed on IDEAS

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    7. Jean-Francois Richard, 2007. "Efficient High-Dimensional Importance Sampling," Working Paper 321, Department of Economics, University of Pittsburgh, revised Jan 2007.
    8. Roman Liesenfeld & Guilherme V. Moura & Jean-François Richard & Hariharan Dharmarajan, 2013. "Efficient Likelihood Evaluation of State-Space Representations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(2), pages 538-567.
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    Cited by:

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    3. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.

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