Panel Data Models: Dynamic Linear Models

This estimator in LIMDEP is built in two parts:

Hausman and Taylor instrumental variable estimator for the linear panel model

Hausman and Taylor’s estimator for the random effects model overcomes the possible correlation between the independent variables and the random effects. The random effects model is formulated with the possibility that there may be time invariant independent variables.

           

There are four sets of variables in the model, the xs which are time varying and the fs which are not time varying, and variables subscripted ‘1’ which are uncorrelated with u_i and the remainder which may be correlated with u_i. A three step procedure which ends with generalized instrumental variables estimation is used for estimation.

Arellano/Bond/Bover estimator for the dynamic model

The Arellano/Bond/Bover estimator is for the dynamic random effects model

           

This extends the Hausman and Taylor estimator. Two step GMM is used to estimate the model. You have a choice of covariance structures for u_i (uncorrelated, random effect, freely correlated across time) and a choice of different sets of instrumental variables for the GMM estimator (variations in the number of future and lagged values). The optimal weighting matrix is computed at the second step. This is their efficient estimator. Barghava and Sargan’s specification test statistic is presented after estimation.