Activity Number:
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201
- Estimation and Inference in Complex Systems
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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Abstract #322109
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Title:
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Multivariate Nonlinear Autoregressive Time Series Model Estimation: A Semiparametric Approach
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Author(s):
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Mahtab Hajebi* and S. Yaser Samadi
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Companies:
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University of Central Florida and Southern Illinois University Carbondale
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Keywords:
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Semiparametric Estimation;
Kernel Approach;
Nonparametric Adjustment;
Multivariate Taylor Series Expansion;
Maximum Likelihood Estimation;
Vector Autoregressive Model
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Abstract:
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A semiparametric method is introduced to estimate vector autoregressive function in the nonlinear vector time series model. We consider a combination of parametric and nonparametric estimation approach to estimate the nonlinear vector autoregressive function for both independent and dependent errors. The multivariate Taylor series expansion is applied to approximate the vector regression function up to the second order. After the unknown parameters are estimated by the maximum likelihood estimation procedure, the obtained nonlinear vector autoregressive function is adjusted by a nonparametric diagonal matrix. The proposed adjusted matrix is estimated using nonparametric kernel method. Asymptotic consistency properties of the proposed estimators are established. Simulation studies are conducted to evaluate the performance of the proposed semiparametric method. Finally, we demonstrate the application of the proposed approach with an empirical example.
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Authors who are presenting talks have a * after their name.