Multivariate Refression Models for Paned Data

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Abstract
Under stationarity, the heterogeneous stoahastic processes are the non-ergodic ones. We show that if a distributed lag is of finite order, then its coefficients are unconditional means of the underlying random coefficients. This result is applied to linear transformations of the process. The estimation framework is a multivariate wide-sense regression function. The identification analysis requires certain restrictions on the coefficients. The actual regression function is nonlinear, and so we provide a theory of inference for linear approximations. It rests on obtaining the asymptotic distribution of functions of sample moments. Restrictions are imposed by using a minimum distance estimator; it is generally more efficient than the conventional estimators.
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