State Space Representation of Mixture Autoregressive Model

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Yeganegi Mohammad Reza; CHINIPARDAZ RAHIM

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Journal: JOURNAL OF STATISTICAL SCIENCES Year:2019 | Volume:13 | Issue:1 Page(s): 235-259

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Information Journal Paper

Title

State Space Representation of Mixture Autoregressive Model

Author(s)

Yeganegi Mohammad Reza | CHINIPARDAZ RAHIM | Issue Writer Certificate

Keywords

Conditional Dynamic Linear ModelQ2

EM AlgorithmQ1

Mixture Autoregressive ModelQ1

Mixture ARMA ModelQ1

Mixture Kalman FilterQ1

Nonlinear State Space ModelQ1

Abstract

This paper is investigating the Mixture Autoregressive Model with constant mixing weights in state space form and generalization to ARMA mixture model. Using a sequential Monte Carlo method, the forecasting, filtering and smoothing distributions are approximated and parameters f the model is estimated via the EM Algorithm. The results show the dimension of parameter vector in state space representation reduces. The results of the simulation study show that the proposed filtering algorithm has a steady state close to the real values of the state vector. Moreover, according to simulation results, the mean vectors of filtering and smoothing distribution converges to state vector quickly.

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APA: Copy

Yeganegi, Mohammad Reza, & CHINIPARDAZ, RAHIM. (2019). State Space Representation of Mixture Autoregressive Model. JOURNAL OF STATISTICAL SCIENCES, 13(1 ), 235-259. SID. https://sid.ir/paper/124122/en

Vancouver: Copy

Yeganegi Mohammad Reza, CHINIPARDAZ RAHIM. State Space Representation of Mixture Autoregressive Model. JOURNAL OF STATISTICAL SCIENCES[Internet]. 2019;13(1 ):235-259. Available from: https://sid.ir/paper/124122/en

IEEE: Copy

Mohammad Reza Yeganegi, and RAHIM CHINIPARDAZ, “State Space Representation of Mixture Autoregressive Model,” JOURNAL OF STATISTICAL SCIENCES, vol. 13, no. 1 , pp. 235–259, 2019, [Online]. Available: https://sid.ir/paper/124122/en

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