Identification and estimation of the error-in-variables model (EVM) in structural form

It is shown that the EVM in structural form is identifiable if serial correlation is present in the independent variables. Least Squares, Instrumental Variable and Maximum Likelihood techniques for the identification and estimation of serial correlations and other EVM parameters are given. The techniques used are based on State Vector Models, Kalman Filtering and Innovation representations. Generalizations to EVM involving multiple regressions and randomly time-varying coefficients are also discussed.

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