On Testing for Equicorrelation in Random Coefficient Models

Abstract

summnryRecently Bmm (1993) considered an efficient estimation of random coefficient model based on survey data. The main objective of this paper is to construct one sided test for testing equicorrelation coefficient in presence of random coefficients using optimal testing procedure. The test statistic is a ratio of quadratic forms in normal variables which is most powerful and point optimal invariant.

Point optimal testing: A survey of the post 1987 literature

“…Since 1987, point optimal invariant tests have been proposed for a wide range of testing problems involving the covariance matrix in the linear regression model. These include (i) testing for autocorrelation in the presence of missing observations (Shively, 1993), (ii) testing for first order autoregressive (AR 1)disturbances when the data is made up of the aggregate of a large number of small samples (Bhatti, 1992), (iii) testing for spatial autocorrelation in the disturbances (Martellosio, 2010(Martellosio, , 2012, (iv) testing for block effects caused by random coefficients (Bhatti and Barry, 1995), (v) testing for quarter-dependent simple fourth-order autoregressive (AR(4)) disturbances (Wu and King, 1996), (vi) testing for joint AR(1)-AR(4) disturbances against joint MA(1)-MA 4disturbances (Silvapulle and King, 1993) and (vii) testing for the presence of a particular error component (El- Bassiouni and Charif, 2004). Hwang and Schmidt (1996) extended the work of Dufour and King (1991) Dufour and King's (1991) tests, the main difference being the treatment of the initial observation.…”