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Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling

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  • Geweke, John
  • Tanizaki, Hisashi

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  • Geweke, John & Tanizaki, Hisashi, 2001. "Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling," Computational Statistics & Data Analysis, Elsevier, vol. 37(2), pages 151-170, August.
    • Handle: RePEc:eee:csdana:v:37:y:2001:i:2:p:151-170
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    1. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589833, October.
    2. John Geweke, "undated". "Posterior Simulators in Econometrics," Computing in Economics and Finance 1996 _019, Society for Computational Economics.
    3. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(3), pages 409-431, August.
    4. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    5. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589819, October.
    6. Geweke, John, 1996. "Monte carlo simulation and numerical integration," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 15, pages 731-800, Elsevier.
    7. Carter, C.K. & Kohn, R., "undated". "Markov Chain Monte Carlo in Conditionally Gaussian State Space Models," Statistics Working Paper _003, Australian Graduate School of Management.
    8. Tanizaki, Hisashi & Mariano, Roberto S., 1998. "Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 263-290.
    9. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589826, October.
    10. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1.
    11. Mariano,Roberto & Schuermann,Til & Weeks,Melvyn J. (ed.), 2000. "Simulation-based Inference in Econometrics," Cambridge Books, Cambridge University Press, number 9780521591126, October.
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    4. Fabio Canova & Fernando J. Pérez Forero, 2012. "Estimating Overidentified, Nonrecursive Time-Varying Coefficients Structural VARs," Working Papers 637, Barcelona School of Economics.
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    7. Delle Monache, Davide & Petrella, Ivan, 2019. "Efficient matrix approach for classical inference in state space models," Economics Letters, Elsevier, vol. 181(C), pages 22-27.
    8. Moon Jung Choi & Geun-Young Kim & Joo Yong Lee, 2015. "An Analysis of Trade Patterns in East Asia and the Effects of the Real Exchange Rate Movements," Working Papers 2015-29, Economic Research Institute, Bank of Korea.
    9. Sy-Miin Chow & Zhaohua Lu & Andrew Sherwood & Hongtu Zhu, 2016. "Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation–Maximization (SAEM) Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 102-134, March.
    10. Linardi, Fernando & Diks, Cees & van der Leij, Marco & Lazier, Iuri, 2020. "Dynamic interbank network analysis using latent space models," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    11. Christian Hotz‐Behofsits & Florian Huber & Thomas Otto Zörner, 2018. "Predicting crypto‐currencies using sparse non‐Gaussian state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 627-640, September.
    12. Wang, Guiming, 2007. "On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models," Ecological Modelling, Elsevier, vol. 200(3), pages 521-528.
    13. Liu Xiangdong & Li Xianglong & Zheng Shaozhi & Qian Hangyong, 2020. "PMCMC for Term Structure of Interest Rates under Markov Regime Switching and Jumps," Journal of Systems Science and Information, De Gruyter, vol. 8(2), pages 159-169, April.
    14. Veyssiere, Luc Pierre, 2009. "A three essays dissertation on agricultural and environmental microeconomics," ISU General Staff Papers 200901010800001958, Iowa State University, Department of Economics.
    15. Su, Zhenming & Peterman, Randall M., 2012. "Performance of a Bayesian state-space model of semelparous species for stock-recruitment data subject to measurement error," Ecological Modelling, Elsevier, vol. 224(1), pages 76-89.
    16. Almeida, Carlos & Czado, Claudia, 2012. "Efficient Bayesian inference for stochastic time-varying copula models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1511-1527.
    17. Koenker, Roger & Yoon, Jungmo, 2009. "Parametric links for binary choice models: A Fisherian-Bayesian colloquy," Journal of Econometrics, Elsevier, vol. 152(2), pages 120-130, October.
    18. Gao, Rui & Li, Yaqiong & Lin, Lisha, 2019. "Bayesian statistical inference for European options with stock liquidity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 312-322.
    19. Helio Migon & Alexandra Schmidt & Romy Ravines & João Pereira, 2013. "An efficient sampling scheme for dynamic generalized models," Computational Statistics, Springer, vol. 28(5), pages 2267-2293, October.
    20. Na Xia & Qinan Zhi & Menghua He & Yunqing Hong & Huazheng Du, 2020. "A navigation satellite selection algorithm for optimized positioning based on Gibbs sampler," International Journal of Distributed Sensor Networks, , vol. 16(6), pages 15501477209, June.
    21. Hasegawa, Takanori & Niida, Atsushi & Mori, Tomoya & Shimamura, Teppei & Yamaguchi, Rui & Miyano, Satoru & Akutsu, Tatsuya & Imoto, Seiya, 2016. "A likelihood-free filtering method via approximate Bayesian computation in evaluating biological simulation models," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 63-74.

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