We obtain a conditional prediction mean squared error for a state space model with estimated parameters. An important application of our results is the derivation of conditional forecast and interpolation mean squared errors for autoregressive-moving average models with estimated parameters. We also obtain the conditional mean squared error for filtered and smoothed estimates of the state vector
[[abstract]]Best mean square prediction for moving average time series models is generally non-linea...
In this article we consider the problem of prediction for a general class of Gaussian models, which ...
This paper contributes to the problem of estimation of state space model parameters by proposing est...
This paper aims to discuss some problems on state space models with estimated parameters. While exis...
[[sponsorship]]統計科學研究所[[note]]已出版;[SCI];有審查制度;不具代表性[[note]]http://gateway.isiknowledge.com/gateway/G...
Three general classes of state space models are presented, using the single source of error formulat...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usin...
The main objective of this paper is to provide analytical expressions for forecast variances that ca...
Prediction intervals in state space models can be obtained by assuming Gaussian innovations and usin...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usi...
We propose simple parametric and nonparametric bootstrap methods for estimating the prediction mean ...
The asymptotic expression for the mean-squared prediction error is discussed for the near-unit-root ...
Measurement errors can have dramatic impact on the outcome of empirical analysis. In this article we...
January 2004; revised September 2006This paper is based on a portion of Chapter 3 of the author's Ph...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usin...
[[abstract]]Best mean square prediction for moving average time series models is generally non-linea...
In this article we consider the problem of prediction for a general class of Gaussian models, which ...
This paper contributes to the problem of estimation of state space model parameters by proposing est...
This paper aims to discuss some problems on state space models with estimated parameters. While exis...
[[sponsorship]]統計科學研究所[[note]]已出版;[SCI];有審查制度;不具代表性[[note]]http://gateway.isiknowledge.com/gateway/G...
Three general classes of state space models are presented, using the single source of error formulat...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usin...
The main objective of this paper is to provide analytical expressions for forecast variances that ca...
Prediction intervals in state space models can be obtained by assuming Gaussian innovations and usin...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usi...
We propose simple parametric and nonparametric bootstrap methods for estimating the prediction mean ...
The asymptotic expression for the mean-squared prediction error is discussed for the near-unit-root ...
Measurement errors can have dramatic impact on the outcome of empirical analysis. In this article we...
January 2004; revised September 2006This paper is based on a portion of Chapter 3 of the author's Ph...
Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and usin...
[[abstract]]Best mean square prediction for moving average time series models is generally non-linea...
In this article we consider the problem of prediction for a general class of Gaussian models, which ...
This paper contributes to the problem of estimation of state space model parameters by proposing est...