Strong consistency of estimators for heteroscedastic partly linear regression model under dependent samples
In this paper we are concerned with the heteroscedastic regression model y i = x i β + g(t i ) + σ i e i 1 ≤ i ≤ n) under correlated errors e i , where it is assumed that σ i 2 = f(u i ), the design points (x i , t i , u i ) are known and nonrandom, and g and f are unknown functions. The interest li...
| Summary: | In this paper we are concerned with the heteroscedastic regression model y i = x i β + g(t i ) + σ i e i 1 ≤ i ≤ n) under correlated errors e i , where it is assumed that σ i 2 = f(u i ), the design points (x i , t i , u i ) are known and nonrandom, and g and f are unknown functions. The interest lies in the slope parameter β. Assuming the unobserved disturbance e i are negatively associated, we study the issue of strong consistency for two different slope estimators: the least squares estimator and the weighted least squares estimator. ©2002 by North Atlantic Science Publishing Company. |
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