Estimation of the dependence parameter in linear regression with long-range-dependent errors

Liudas Giraitis () and Hira Koul

Stochastic Processes and their Applications, 1997, vol. 71, issue 2, 207-224

Abstract: This paper establishes the consistency and the root-n asymptotic normality of the exact maximum likelihood estimator of the dependence parameter in linear regression models where the errors are a nondecreasing function of a long-range-dependent stationary Gaussian process. The spectral density of the Gaussian process is assumed to be unbounded at the origin. The paper thus generalizes some of the results of Dahlhaus (1989) to linear regression models with non-Gaussian long-range-dependent errors.

Keywords: Unbounded; spectral; density; Maximum; likelihood; estimator; n1/2-asymptotic; normality; Logistic; and; double-exponential; marginal; errors; Polynomial; regression (search for similar items in EconPapers)
Date: 1997
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Citations: View citations in EconPapers (6)

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