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Nonlinear and NonGaussian StateSpace Modeling with Monte Carlo Techniques: A Survey and Comparative Study
 In Rao, C., & Shanbhag, D. (Eds.), Handbook of Statistics
, 2000
"... Since Kitagawa (1987) and Kramer and Sorenson (1988) proposed the filter and smoother using numerical integration, nonlinear and/or nonGaussian state estimation problems have been developed. Numerical integration becomes extremely computerintensive in the higher dimensional cases of the state vect ..."
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Cited by 18 (4 self)
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Since Kitagawa (1987) and Kramer and Sorenson (1988) proposed the filter and smoother using numerical integration, nonlinear and/or nonGaussian state estimation problems have been developed. Numerical integration becomes extremely computerintensive in the higher dimensional cases of the state vector. Therefore, to improve the above problem, the sampling techniques such as Monte Carlo integration with importance sampling, resampling, rejection sampling, Markov chain Monte Carlo and so on are utilized, which can be easily applied to multidimensional cases. Thus, in the last decade, several kinds of nonlinear and nonGaussian filters and smoothers have been proposed using various computational techniques. The objective of this paper is to introduce the nonlinear and nonGaussian filters and smoothers which can be applied to any nonlinear and/or nonGaussian cases. Moreover, by Monte Carlo studies, each procedure is compared by the root mean square error criterion.
TSP 5.0 Userâ€™s Guide
, 2005
"... First edition (Version 4.0) published 1980. TSP is a software product of TSP International. The information in this document is subject to change without notice. TSP International assumes no responsibility for any errors that may appear in this document or in TSP. The software described in this docu ..."
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Cited by 1 (0 self)
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First edition (Version 4.0) published 1980. TSP is a software product of TSP International. The information in this document is subject to change without notice. TSP International assumes no responsibility for any errors that may appear in this document or in TSP. The software described in this document is protected by copyright. Copying of software for the use of anyone other than the original purchaser is a violation of federal law. Time Series Processor and TSP are trademarks of TSP International.
ESTIMATING TIMEVARYING PARAMETERS IN LINEAR REGRESSION MODELS USING A TWOPART DECOMPOSITION OF THE OPTIMAL CONTROL FORMULATION
"... SUMMARY. This paper discusses an econometric technique based on optimal control theory which, by employing a variation of the nearneighbourhood search problem, is seen to be suitable for the type of research that requires estimating timevarying parameters for linear regression models. The methodol ..."
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SUMMARY. This paper discusses an econometric technique based on optimal control theory which, by employing a variation of the nearneighbourhood search problem, is seen to be suitable for the type of research that requires estimating timevarying parameters for linear regression models. The methodology is based on the characterization of the timevarying parameter (TVP) problem as an optimal control problem, with an explicit allowance for welfare loss considerations, which leads to an algorithm capable of updating the values of the timevarying parameters as well as their covariance matrices. The technique adopts an instrumentstargets approach, with the initial condition and the emphasis on parameter flexibility being the instruments; and the total welfare loss and the norm of the error vector being the targets. The methodology is a blend of the flexible least squares and Kalman filter techniques. By determining all the required priors endogenously, it is seen to overcome some of the drawbacks associated with these two earlier approaches to the TVP problem. The method works on the premise that the dynamics of the system are determined by the system itself without being specified by the user in an arbitrary fashion. 1.
The TimeVarying Parameter Model Revisited
"... The Kalman filter formula, given by the linear recursive algorithm, is usually used for estimation of the timevarying parameter model. The filtering formula, introduced by Kalman (1960) and Kalman and Bucy (1961), requires the initial state variable. The obtained state estimates are influenced by t ..."
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The Kalman filter formula, given by the linear recursive algorithm, is usually used for estimation of the timevarying parameter model. The filtering formula, introduced by Kalman (1960) and Kalman and Bucy (1961), requires the initial state variable. The obtained state estimates are influenced by the initial value when the initial variance is not too large. To avoid the choice of the initial state variable, in this paper we utilize the di#use prior for the initial density. Moreover, using the Gibbs sampler, random draws of the state variables given all the data are generated, which implies that random draws are generated from the fixedinterval smoothing densities. Using the EM algorithm, the unknown parameters included in the system are estimated. As an example, we estimate a traditional consumption function for both the U.S. and Japan.
Preface
, 1999
"... This text provides an introduction to spatial econometric theory along with numerous applied illustrations of the models and methods described. The applications utilize a set of MATLAB functions that implement a host of spatial econometric estimation methods. The intended audience is faculty,student ..."
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This text provides an introduction to spatial econometric theory along with numerous applied illustrations of the models and methods described. The applications utilize a set of MATLAB functions that implement a host of spatial econometric estimation methods. The intended audience is faculty,students and practitioners involved in modeling spatial data sets. The MATLAB functions described in this book have been used in my own research as well as teaching both undergraduate and graduate econometrics courses. They are available on the Internet at
KALMAN (BPRIOR=prior vector,
"... VBPRIOR=variance of prior, VMEAS=variance factor in measurement equation, VTRANS=variance factor in transition equation, XFIXED=X matrix for measurement equation) list of dependent variables [  list of independent variables]; ..."
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VBPRIOR=variance of prior, VMEAS=variance factor in measurement equation, VTRANS=variance factor in transition equation, XFIXED=X matrix for measurement equation) list of dependent variables [  list of independent variables];