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A Semiparametric Estimation For The Nonlinear Vector Autoregressive Time Series Model, Rahman Farnoosh, Mahtab Hajebi, Seyed J. Mortazavi

Applications and Applied Mathematics: An International Journal (AAM)

In this paper, the nonlinear vector autoregressive model is considered and a semiparametric method is proposed to estimate the nonlinear vector regression function. We use Taylor series expansion up to the second order which has a parametric framework as a representation of the nonlinear vector regression function. After the parameters are estimated through the least squares method, the obtained nonlinear vector regression function is adjusted by a nonparametric diagonal matrix, and the proposed diagonal matrix is also estimated through the nonparametric smooth-kernel approach. Estimating the parameters can yield the desired estimate of the vector regression function based on the data. …