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Tests for Over-identifying Restrictions in Partially Identified Linear Structural Equations

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  • Giovanni Forchini

Abstract

Cragg and Donald (1996) have pointed out that the asymptotic size of tests for overidentifying restrictions can be much smaller than the asymptotic nominal size when the structural equation is partially identified. This may lead to misleading inference if the critical values are obtained from a chi-square distribution. To overcome this problem we derive the exact asymptotic distribution of the Byron test statistic. This allows the calculation of asymptotic critical values and p-values corrected for possible failure of identification.

Suggested Citation

  • 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.
    • Handle: RePEc:msh:ebswps:2006-20
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2006/wp20-06.pdf
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    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Phillips, P.C.B., 1989. "Partially Identified Econometric Models," Econometric Theory, Cambridge University Press, vol. 5(2), pages 181-240, August.
    3. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    4. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    5. 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.
    6. Hillier, Grant, 2006. "Yet More On The Exact Properties Of Iv Estimators," Econometric Theory, Cambridge University Press, vol. 22(5), pages 913-931, October.
    7. Robin, Jean-Marc & Smith, Richard J., 2000. "Tests Of Rank," Econometric Theory, Cambridge University Press, vol. 16(2), pages 151-175, April.
    8. BYRON, Ray P., 1974. "Testing structural specification using the unrestricted reduced form," LIDAM Reprints CORE 192, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Sargan, J D, 1983. "Identification and Lack of Identification," Econometrica, Econometric Society, vol. 51(6), pages 1605-1633, November.
    10. Hillier, Grant H, 1990. "On the Normalization of Structural Equations: Properties of Direct Estimators," Econometrica, Econometric Society, vol. 58(5), pages 1181-1194, September.
    11. Byron, R P, 1974. "Testing Structural Specification Using the Unrestricted Reduced Form," Econometrica, Econometric Society, vol. 42(5), pages 869-883, September.
    12. Choi, In & Phillips, Peter C. B., 1992. "Asymptotic and finite sample distribution theory for IV estimators and tests in partially identified structural equations," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 113-150.
    13. Jean-Marc Robin & Richard Smith, 2000. "Tests of rank," Post-Print hal-03587662, HAL.
    14. Hausman, Jerry A., 1983. "Specification and estimation of simultaneous equation models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 7, pages 391-448, Elsevier.
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    More about this item

    Keywords

    Invariant Tests; Over-identifying restrictions; Partially identified structural equation;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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