Article plaidant en faveur de l'utilisation d'estimateurs d'equations simultanees pour estimer les modeles d'effets aleatoires dynamiques d'un panel de donnees

This paper considers the null hypothesis that the errors on a regression equation form a random walk. By using the standard Durbin-Watson assumptions, we derive three test statistics that are uniformly most powerful against the alternative hypothesis that the errors are being generated by the stationary first order Markoff process. Unfortunately, the tabulated lower and upper bounds are too wide apart and so we compare the powers of the three tests using simulated as well as economic data. It is then recommended that the Imhof routine should be attached to standard regression programs to calculate the exact limit of the Berenblut-Webb statistic.

J. D. Sargan, Alok Bhargava, Maximum Likelihood Estimation of Regression Models with First Order Moving Average Errors when the Root Lies on the Unit Circle, Econometrica, Vol. 51, No. 3 (May, 1983), pp. 799-820