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On the Practice of Bayesian Inference in Basic Economic Time Series Models using Gibbs Sampling
de Pooter, Michiel D.; Segers, René; van Dijk, Herman K. - 2006
Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables model...
Persistent link: https://www.econbiz.de/10010325199
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On the practice of Bayesian inference in basic economic time series models using Gibbs sampling
Pooter, Michiel de; Segers, Rene; Dijk, Herman K. van - 2006
Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables model...
Persistent link: https://www.econbiz.de/10011349180
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Bayesian Inference for Periodic Regime-Switching Models
Ghysels, Eric; McCulloch, Robert E.; Tsay, Ruey S. - Centre Interuniversitaire de Recherche en Analyse des … - 1994
We present a general class of nonlinear time series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for nontrivial dependencies between seasonal,...
Persistent link: https://www.econbiz.de/10005101010
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Forecasting Time Series With Long Memory and Level Shifts : A Bayesian Approach
Di Sanzo, Silvio - 2007
Recent studies have showed that it is troublesome, in practice, to distinguish between long memory and nonlinear processes. Therefore, it is of obvious interest to try to capture both features of long memory and non-linearity into a single time series model to be able to assess their relative...
Persistent link: https://www.econbiz.de/10014050821
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Efficient Bayesian Estimation and Combination of GARCH-Type Models
Ardia, David; Hoogerheide, Lennart F. - 2017
which avoids difficult and time consuming tuning of MCMC strategies. The AdMitIS methodology is illustrated with an …
Persistent link: https://www.econbiz.de/10014198683
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Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations
Ardia, David; Hoogerheide, Lennart F. - 2019
This paper presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious but effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling...
Persistent link: https://www.econbiz.de/10014203852
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Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models
Rombouts, Jeroen V. K.; Stentoft, Lars - 2009
While stochastic volatility models improve on the option pricing error when compared to the Black-Scholes-Merton model, mispricings remain. This paper uses mixed normal heteroskedasticity models to price options. Our model allows for significant negative skewness and time varying higher order...
Persistent link: https://www.econbiz.de/10014212072
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A Flexible Prior Distribution for Markov Switching Autoregressions With Student-T Errors
Deschamps, Philippe J. - 2007
This paper proposes an empirical Bayes approach for Markov switching autoregressions that can constrain some of the state-dependent parameters (regression coefficients and error variances) to be approximately equal across regimes. By flexibly reducing the dimension of the parameter space, this...
Persistent link: https://www.econbiz.de/10014058591
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Identifying Price Jumps from Daily Data with Bayesian vs. Non-Parametric Methods
Fičura, Milan - 2017
parametric approach utilizing a Stochastic-Volatility-Jump-Diffusion (SVJD) model, estimated with MCMC and extended with Particle …-sample estimation does the MCMC based parametric approach significantly outperform the L-Estimator. In the case of the out …-sample estimates, based on a combination of MCMC an Particle Filters, used to sequentially estimate the jump occurrences immediately at …
Persistent link: https://www.econbiz.de/10012964932
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The Gibbs Sampler with Particle Efficient Importance Sampling for State-Space Models
Grothe, Oliver - 2017
We consider Particle Gibbs (PG) as a tool for Bayesian analysis of non-linear non-Gaussian state-space models. PG is a Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the Gibbs procedure to update the latent and potentially...
Persistent link: https://www.econbiz.de/10012970355