This paper provides sufficient conditions for the identification of both static and dynamic models containing endogenous regressors from panel data by utilizing the restrictions across time periods on the parameters. It is shown that identification is achieved under quite weak conditions even in the presence of a general pattern of correlation between the errors and the time-varying variables. Efficient estimation procedures for the models considered and some specification tests are outlined. Finally, static formulations relating individuals' intakes of nutrients in the previous twenty-four hours to household income are estimated using (ICRISAT) panel data from rural India.

This paper provides a framework for testing for a unit root in an observed time series against some alternatives considered previously by Anderson (1948). Some new tests for the unit root null hypothesis for the errors affecting a classical regression model against the non-stationary (including explosive) alternative hypothesis are developed. The previous results of Sargan and Bhargava (1983) and the new test statistics are then applied to test the simple random walk and the random walk with a constant drift null hypotheses against stationary and non-stationary one-sided alternatives. In each case, the test statistic is simplified in order that it could be viewed as a von Neumann type ratio and the exact significance points are tabulated. Finally, the unit root null hypotheses are tested using U.S. data on the velocity of money and the Michigan PSID.

The hypothesis that consumption evolves over time as a martingale process is tested on household panel data for three villages in south India. A novel feature of the methodology is that it gives consistent estimates of dynamic effects in short panels. The estimated coefficients of lagged consumption are generally smaller than unity and a number of the lagged income and wealth variables are statistically significant. The results are inconsistent with the proposition that consumption equals permanent income. This is also true when the data are disaggregated by household wealth.

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