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On the Joint and Marginal Densities of Instrumental Variable Estimators in a General Structural Equation

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  • Hillier, Grant H.

Abstract

Starting from the conditional density of the instrumental variable (IV) estimator given the right-hand-side endogenous variables, we provide an alternative derivation of Phillips' result on the joint density of the IV estimator for the endogenous coefficients, and derive an expression for the marginal density of a linear combination of these coefficients. In addition, we extend Phillips' approximation to the joint density to 0(T−2,) and show how this result can be used to improve the approximation to the marginal density. Explicit formulae are given for the special case of no simultaneity, and the case of an equation with just three endogenous variables. The classical assumptions of independent normal reduced-form errors are employed throughout.

Suggested Citation

  • Hillier, Grant H., 1985. "On the Joint and Marginal Densities of Instrumental Variable Estimators in a General Structural Equation," Econometric Theory, Cambridge University Press, vol. 1(1), pages 53-72, April.
    • Handle: RePEc:cup:etheor:v:1:y:1985:i:01:p:53-72_01
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    Cited by:

    1. Forchini, Giovanni, 2010. "The Asymptotic Distribution Of The Liml Estimator In A Partially Identified Structural Equation," Econometric Theory, Cambridge University Press, vol. 26(3), pages 917-930, June.
    2. Hillier, Grant & Kan, Raymond & Wang, Xiaolu, 2009. "Computationally Efficient Recursions For Top-Order Invariant Polynomials With Applications," Econometric Theory, Cambridge University Press, vol. 25(1), pages 211-242, February.
    3. Phillips, Peter C. B., 1988. "Conditional and unconditional statistical independence," Journal of Econometrics, Elsevier, vol. 38(3), pages 341-348, July.
    4. Giovanni Forchini, 2006. "Tests for Over-identifying Restrictions in Partially Identified Linear Structural Equations," Monash Econometrics and Business Statistics Working Papers 20/06, Monash University, Department of Econometrics and Business Statistics.
    5. Grant Hillier & Giovanni Forchini, 2004. "Ill-posed Problems and Instruments' Weakness," Econometric Society 2004 Australasian Meetings 357, Econometric Society.
    6. Joel L. Horowitz, 1996. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Econometrics 9602009, University Library of Munich, Germany, revised 05 Mar 1996.
    7. Grant H. Hillier, 1987. "Joint Distribution Theory for Some Statistics Based on LIML and TSLS," Cowles Foundation Discussion Papers 840, Cowles Foundation for Research in Economics, Yale University.
    8. Pierre-Daniel G. Sarte, 1997. "On the identification of structural vector autoregressions," Economic Quarterly, Federal Reserve Bank of Richmond, issue Sum, pages 45-68.

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