1,721,281 research outputs found
Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models
Get PDFWe propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent Metropolis-Hastings algorithm. A particular feature of our approach is that smoothed estimates of the states and the marginal likelihood are obtained directly as an output of the algorithm. Our method provides a computationally efficient alternative to several recently proposed algorithms. We present extensive simulation evidence for stochastic volatility and stochastic intensity models. For our empirical study, we analyse the performance of our method for stock returns and corporate default panel data. (This paper is an updated version of the paper that appeared earlier as Barra, I., Hoogerheide, L.F., Koopman, S.J., and Lucas, A. (2013) "Joint Independent Metropolis-Hastings Methods for Nonlinear Non-Gaussian State Space Models". TI Discussion Paper 13-050/III. Amsterdam: Tinbergen Institute.
Three applications to French national electricity load
Get PDFKoopman, S.J. [Promotor]Ooms, M. [Copromotor
Essays on Monte Carlo Methods for State Space Models
Get PDFKoopman, S.J. [Promotor]Lucas, A. [Promotor
Time-Varying Parameter Models for Discrete Valued Time Series
Get PDFLucas, A. [Promotor]Koopman, S.J. [Promotor
Interbank Lending Relationships, Financial Crises, and Monetary Policy
Get PDFLucas, A. [Promotor]Koopman, S.J. [Promotor
State space modelling in macroeconomics and finance using SsfPack in S+Finmetrics
No full textAbstract This article surveys some common state space models used in macroeconomics and finance and shows how to specify and estimate these models using the SsfPack library of algorithms implemented in the S-PLUS module S+FinMetrics. Examples include recursive regression models, time varying parameter models, exact autoregressive moving average models and calculation of the Beveridge-Nelson decomposition, unobserved components models, stochastic volatility models, and term structure models. Introduction The first version of SsfPack appeared in 1998 and was developed further during the years that the last author was working with Jim Durbin on their 2001 textbook on state space methods. The fact that SsfPack functions are now a part of the S-PLUS software is partly due to Jim Durbin. He convinced Doug Martin that SsfPack would be very beneficial to S-PLUS. Indeed the persuasive arguments of Jim Durbin has initiated the development of SsfPack functions for S-PLUS as part of the S+FinMetrics module. It is therefore an honour for us, the developers of SsfPack for S+FinMetrics, to contribute to this volume with the presentation of various applications in economics and finance that require the use of SsfPack for S+FinMetrics in empirical research. State space modelling in economics and finance has become widespread over the last decade. Textbook treatments of state space models are given in Harvey (1989, 1993), Hamilton (1994), West and Harrison (1997), Kim and Nelson (1999), Shumway and Stoffer (2000), Durbin and Koopman (2001) and Chan (2002). However, until recently there has not been much flexible software for the statistical analysis of general models in state space form
Developments in Measuring and Modeling Financial Volatility
Get PDFKoopman, S.J. [Promotor]Bos, C.S. [Copromotor
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