A Class of Estimators of Regression Coefficient for Sign Change Problem in Measurement Error Models
Shalabh and A.T.K. Wan (2007). A Class of Estimators of Regression Coefficient for Sign Change Problem in Measurement Error Models. Journal of Statistical Research, Vol. 41, No. 2, pp. 63-72.
Srivastava and Shalabh (1997a, Journal of Econometrics) proposed a class of Stein-like consistent estimators for estimating the slope coefficient in a single explanatory variable ultrastructural model. This paper studies the sign reversal problem of this class of estimators and proposes an alternative class of improved estimators along the lines of the double-k class estimators of Ullah and Ullah (1978, Econometrica) that overcomes this problem. Large sample asymptotic properties of the proposed estimators are studied for the case where the distributions of the measurement errors are not necessarily normal.