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.