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The simulation smoother for time series models
 BIOMETRIKA (1995), 82,2, PP. 33950
, 1995
"... Recently suggested procedures for simulating from the posterior density of states given a Gaussian state space time series are refined and extended. We introduce and study the simulation smoother, which draws from the multivariate posterior distribution of the disturbances of the model, so avoiding ..."
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Cited by 215 (17 self)
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Recently suggested procedures for simulating from the posterior density of states given a Gaussian state space time series are refined and extended. We introduce and study the simulation smoother, which draws from the multivariate posterior distribution of the disturbances of the model, so avoiding
Inference in Linear Time Series Models with Some Unit Roots,”
 Econometrica
, 1990
"... This paper considers estimation and hypothesis testing in linear time series models when some or all of the variables have unit roots. Our motivating example is a vector autoregression with some unit roots in the companion matrix, which might include polynomials in time as regressors. In the genera ..."
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Cited by 390 (14 self)
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This paper considers estimation and hypothesis testing in linear time series models when some or all of the variables have unit roots. Our motivating example is a vector autoregression with some unit roots in the companion matrix, which might include polynomials in time as regressors
Evolutionary Time Series Model
 In Proc. 3rd JapanUS Seminar on Statistical Time Series Analysis
, 2001
"... In this study,we consider a time series model which combines the partial nonGaussian state space model and selforganizing state space model (SOSSM), where the SOSSM has been proposed to an extension of the generalized state space model. The competing different system/observation models for the s ..."
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In this study,we consider a time series model which combines the partial nonGaussian state space model and selforganizing state space model (SOSSM), where the SOSSM has been proposed to an extension of the generalized state space model. The competing different system/observation models
Hierarchical Bayesian Time Series Models
, 1996
"... Notions of Bayesian analysis are reviewed, with emphasis on Bayesian modeling and Bayesian calculation. A general hierarchical model for time series analysis is then presented and discussed. Both discrete time and continuous time formulations are discussed. An brief overview of generalizations of th ..."
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Cited by 22 (5 self)
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Notions of Bayesian analysis are reviewed, with emphasis on Bayesian modeling and Bayesian calculation. A general hierarchical model for time series analysis is then presented and discussed. Both discrete time and continuous time formulations are discussed. An brief overview of generalizations
Markovian Time Series Models for Language
"... In this paper, we compare the performance of several Markovian time series models for language identification of written text. In particular, we focus on the Hidden Markov Model which has only rarely been applied to the language classification problem. We test our classifiers on six languages and a ..."
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In this paper, we compare the performance of several Markovian time series models for language identification of written text. In particular, we focus on the Hidden Markov Model which has only rarely been applied to the language classification problem. We test our classifiers on six languages
1 Time Series Modelling and Forecasting of
"... Pepper is an important agriculture commodity especially for the state of Sarawak. It is important to forecast its price, as this could help the policy makers in coming up with production and marketing plan to improve the Sarawak’s economy as well as the farmers’ welfare. In this paper, we take up ti ..."
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time series modelling and forecasting of the Sarawak black pepper price. Our empirical results show that Autoregressive Moving Average (ARMA) time series models fit the price series well and they have correctly predicted the future trend of the price series within the sample period of study. Amongst a
Bagging Time Series Models∗
, 2004
"... A common problem in outofsample prediction is that there are potentially many relevant predictors that individually have only weak explanatory power. We propose bootstrap aggregation of pretest predictors (or bagging for short) as a means of constructing forecasts from multiple regression models ..."
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Cited by 3 (0 self)
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models with localtozero regression parameters and errors subject to possible serial correlation or conditional heteroskedasticity. Bagging is designed for situations in which the number of predictors (M) is moderately large relative to the sample size (T). We show how to implement bagging
Structural Time Series Models
"... In economics, it is traditional to decompose time series into a variety of components, some or all of which may be present in a particular instance. One is liable to assume that the relative proportions of the components of an aggregate index are maintained, approximately, in spite of the variations ..."
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In economics, it is traditional to decompose time series into a variety of components, some or all of which may be present in a particular instance. One is liable to assume that the relative proportions of the components of an aggregate index are maintained, approximately, in spite
(STRUCTURAL) TIME SERIES MODELS
, 1993
"... This series contains research reports, written by or in cooperation with staff members of the Statistical Research Division, whose content may be of interest to the general statistical research community. The views reflected in these reports are not necessarily those of the Census Bureau nor do they ..."
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This series contains research reports, written by or in cooperation with staff members of the Statistical Research Division, whose content may be of interest to the general statistical research community. The views reflected in these reports are not necessarily those of the Census Bureau nor do
Linear Time Series Models
"... he innovations to be independent N(ffl t j0; v): AR models may be viewed from a purely empirical standpoint; the data are assumed related over time and the AR form is about the simplest class of empirical models for exploring dependencies. A more formal motivation is, of course, based on the genesi ..."
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he innovations to be independent N(ffl t j0; v): AR models may be viewed from a purely empirical standpoint; the data are assumed related over time and the AR form is about the simplest class of empirical models for exploring dependencies. A more formal motivation is, of course, based
Results 1  10
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331,670