Zobrazeno 1 - 3
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pro vyhledávání: '"Siem Jan Koopman"'
Publikováno v:
Journal of Econometrics, 221(2), 542-568. Elsevier BV
Blasques, F, Gorgi, P & Koopman, S J 2021, ' Missing observations in observation-driven time series models ', Journal of Econometrics, vol. 221, no. 2, pp. 542-568 . https://doi.org/10.1016/j.jeconom.2020.07.043
Blasques, F, Gorgi, P & Koopman, S J 2021, ' Missing observations in observation-driven time series models ', Journal of Econometrics, vol. 221, no. 2, pp. 542-568 . https://doi.org/10.1016/j.jeconom.2020.07.043
We argue that existing methods for the treatment of missing observations in time-varying parameter observation-driven models lead to inconsistent inference. We provide a formal proof of this inconsistency for a Gaussian model with time-varying mean.
Publikováno v:
Borowska, A, Hoogerheide, L, Koopman, S J & van Dijk, H K 2020, ' Partially censored posterior for robust and efficient risk evaluation ', Journal of Econometrics, vol. 217, no. 2, pp. 335-355 . https://doi.org/10.1016/j.jeconom.2019.12.007
Journal of Econometrics, 217(2), 335-355. Elsevier BV
Journal of Econometrics, 217(2), 335-355. Elsevier BV
A novel approach to inference for a specific region of the predictive distribution is introduced. An important domain of application is accurate prediction of financial risk measures, where the area of interest is the left tail of the predictive dens
Autor:
Siem Jan Koopman, Gleb Sandmann
Publikováno v:
Journal of Econometrics. 87:271-301
This paper discusses the Monte Carlo maximum likelihood method of estimating stochastic volatility (SV) models. The basic SV model can be expressed as a linear state space model with log chi-square disturbances. The likelihood function can be approxi