The LIML estimator has finite moments! |
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| Authors: | TW Anderson |
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| Institution: | Department of Economics, Stanford University, Stanford, CA 94305, United States; Department of Statistics, Stanford University, Stanford, CA 94305, United States |
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| Abstract: | The Limited Information Maximum Likelihood estimator of the vector of coefficients of a structural equation in a simultaneous equation model is the vector that defines the linear combination maximizing the effect variance relative to the error variance. If this “eigenvector” solution is normalized by setting a designated coefficient equal to 1, the second-order moment of the estimator may be unbounded. However, the second-order moment is finite if the normalization sets the sample error variance of the linear combination equal to 1. |
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| Keywords: | Limited Information Maximum Likelihood Bounded moments Normalization |
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