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Edgeworth Expansion for the OLS Estimator in a Time Series Regression Model

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  • Maekawa, Koichi

Abstract

In this paper we consider the situation in which ordinary least squares (OLS) is used to estimate an ARMA (1,1) model with one exogenous variable. Applying Edgeworth expansion techniques, we examine the misspecification errors and the approximate distributions of the OLS estimator. Extensive numerical studies were performed and selected results are shown graphically. In addition, a technical device is developed to calculate the Edgeworth coefficients.

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  • Maekawa, Koichi, 1985. "Edgeworth Expansion for the OLS Estimator in a Time Series Regression Model," Econometric Theory, Cambridge University Press, vol. 1(2), pages 223-239, August.
    • Handle: RePEc:cup:etheor:v:1:y:1985:i:02:p:223-239_01
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    Cited by:

    1. Francesca Rossi & Peter M. Robinson, 2020. "Higher-Order Least Squares Inference for Spatial Autoregressions," Working Papers 04/2020, University of Verona, Department of Economics.
    2. Rossi, Francesca & Robinson, Peter M., 2023. "Higher-order least squares inference for spatial autoregressions," Journal of Econometrics, Elsevier, vol. 232(1), pages 244-269.

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