论文信息 - A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix

A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix

This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions. (This abstract was borrowed from another version of this item.)

W. Newey | K. West | Whitney Newey

[1] T. Anderson. Statistical analysis of time series , 1974 .

[2] G. Wahba,et al. A completely automatic french curve: fitting spline functions by cross validation , 1975 .

[3] Chris Chatfield,et al. Introduction to Statistical Time Series. , 1976 .

[4] L. Hansen,et al. Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models , 1982 .

[5] L. Hansen. LARGE SAMPLE PROPERTIES OF GENERALIZED METHOD OF , 1982 .

[6] John Huizinga,et al. Two-Step Two-Stage Least Squares Estimation in Models with Rational Expectations , 1983 .

[7] H. White,et al. Nonlinear Regression with Dependent Observations , 1984 .

[8] C. Jiménez,et al. Nonnegative Definiteness of the Sample Autocovariance Function , 1984 .

[9] H. White. Asymptotic theory for econometricians , 1985 .

[10] K. West. A Variance Bounds Test of the Linear Quadratic Inventory Model , 1985, Journal of Political Economy.

[11] M. Eichenbaum,et al. A Time Series Analysis of Representative Agent Models of Consumption Andleisure Choice Under Uncertainty , 1986 .

[12] K. West. A Specification Test for Speculative Bubbles , 1986 .