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Descriptor realizations of autoregressive representations
 IMA JOURNAL OF MATHEMATICAL CONTROL AND INFORMATION
, 2004
"... ... In this paper, it is shown how to reduce an ARrepresentation to a fundamental equivalent realization in descriptor form. ..."
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... In this paper, it is shown how to reduce an ARrepresentation to a fundamental equivalent realization in descriptor form.
order moving average and autoregressive representations
"... Abstract. An invertible causal linear process is a process which has infinite order moving average and autoregressive representations. We assume that the coefficients in these representations depend on a Euclidean parameter, while the corresponding innovations have an unknown centered distribution ..."
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Abstract. An invertible causal linear process is a process which has infinite order moving average and autoregressive representations. We assume that the coefficients in these representations depend on a Euclidean parameter, while the corresponding innovations have an unknown centered distri
Finite state Markovchain approximations to univariate and vector autoregressions
 Economics Letters
, 1986
"... The paper develops a procedure for finding a discretevalued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1. ..."
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Cited by 472 (0 self)
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The paper develops a procedure for finding a discretevalued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1.
Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models
 Journal of Business and Economic Statistics
, 2002
"... Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled wi ..."
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Cited by 684 (17 self)
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Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two step methods based on the likelihood function. It is shown that they perform well in a variety of situations and provide sensible empirical results.
A NoArbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables
, 2002
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Stationary space–time Gaussian fields and their time autoregressive representation
, 2002
"... We compare two different modelling strategies for continuous space discrete time data. The first strategy is in the spirit of Gaussian kriging. The model is a general stationary space–time Gaussian field where the key point is the choice of a parametric form for the covariance function. In the main, ..."
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Cited by 7 (0 self)
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, covariance functions that are used are separable in space and time. Nonseparable covariance functions are useful in many applications, but construction of these is not easy. The second strategy is to model the time evolution of the process more directly. We consider models of the autoregressive type where
Stationary space–time Gaussian elds and their time autoregressive representation
"... Abstract: We compare two different modelling strategies for continuous space discrete time data. The rst strategy is in the spirit of Gaussian kriging. The model is a general stationary space–time Gaussian eld where the key point is the choice of a parametric form for the covariance function. In the ..."
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. In the main, covariance functions that are used are separable in space and time. Nonseparable covariance functions are useful in many applications, but construction of these is not easy. The second strategy is to model the time evolution of the process more directly. We consider models of the autoregressive
Stationary Space Time Gaussian Fields and their time autoregressive representation
, 2002
"... We compare two different modelling strategies for continuous space discrete time data. The first strategy is in the spirit of Gaussian kriging. The model is a general stationary spacetime Gaussian field where the key point is the choice of a parametric form for the covariance function. Mostly, cova ..."
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We compare two different modelling strategies for continuous space discrete time data. The first strategy is in the spirit of Gaussian kriging. The model is a general stationary spacetime Gaussian field where the key point is the choice of a parametric form for the covariance function. Mostly, covariance functions which are used are separable in space and time. Nonseparable covariance functions are useful in many applications, but construction of such is not easy.
Testing for Common Trends
 Journal of the American Statistical Association
, 1988
"... Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests involve the roots of the ordinary least squares coefficient matrix ..."
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Cited by 455 (7 self)
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matrix obtained by regressing the series onto its first lag. Critical values for the tests are tabulated, and their power is examined in a Monte Carlo study. Economic time series are often modeled as having a unit root in their autoregressive representation, or (equivalently) as containing a stochastic
Results 1  10
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