Regression and
autoregressive mixed models are classical models used to analyze the
relationship between time series response variable and other covariates. The
coefficients in traditional regression and autoregressive mixed models are
constants. However, for complicated data, the coefficients of covariates may
change with time. In this article, we propose a kind of partial time-varying
coefficient regression and autoregressive mixed model and obtain the local
weighted least-square estimators of coefficient functions by the local
polynomial technique. The asymptotic normality properties of estimators are
derived under regularity conditions, and simulation studies are conducted to
empirically examine the finite-sample performances of the proposed estimators.
Finally, we use real data about Lake Shasta inflow to illustrate the
application of the proposed model.
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