Fixed design regression for time series: Asymptotic normality

George G. Roussas, Lanh T. Tran and D. A. Ioannides

Journal of Multivariate Analysis, 1992, vol. 40, issue 2, 262-291

Abstract: Consider the fixed regression model with general weights, and suppose that the error random variables are coming from a strictly stationary stochastic process, satisfying the strong mixing condition. The asymptotic normality of the proposed estimate is established under weak conditions. The applicability of the results obtained is demonstrated by way of two existing estimates, the Gasser-Müller estimate and that of Priestley and Chao. The asymptotic normality of these estimates is further illustrated by means of a concrete example from the class of autoregressive processes.

Keywords: regression; fixed; design; weights; asymptotic; normality; strict; stationarity; strong; mixing; bounded; case; unbounded; case (search for similar items in EconPapers)
Date: 1992
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Citations: View citations in EconPapers (41)

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