Strong Consistency of Estimators for Heteroscedastic Partly Linear Regression Model under Dependent Samples

Han-Ying Liang and Bing-Yi Jing

International Journal of Stochastic Analysis, 2002, vol. 15, 1-13

Abstract:

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.

Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnijsa:537094

DOI: 10.1155/S1048953302000187

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