IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/0090.html
   My bibliography  Save this paper

The Maximum Likelihood Stage Least Squares Estimator in the Nonlinear Simultaneous Equations Model

Author

Listed:
  • Takeshi Amemiya

Abstract

The consistency and the asymptotic normality of the maximum likelihood estimator in the general nonlinear simultaneous equation model are proved. It is shown that the proof depends on the assumption of normality unlike in the linear simultaneous equation model. It is proved that the maximum likelihood estimator is asymptotically more efficient than the nonlinear three-stage least squares estimator if the specification is correct, However, the latter has the advantage of being consistent even when the normality assumption is removed. Hausrnan' s instrumental-variable-interpretation of the maximum likelihood estimator is extended to the general nonlinear simultaneous equation model.

Suggested Citation

  • Takeshi Amemiya, 1975. "The Maximum Likelihood Stage Least Squares Estimator in the Nonlinear Simultaneous Equations Model," NBER Working Papers 0090, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:0090
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w0090.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Amemiya, Takeshi, 1974. "The nonlinear two-stage least-squares estimator," Journal of Econometrics, Elsevier, vol. 2(2), pages 105-110, July.
    2. Hausman, Jerry A, 1975. "An Instrumental Variable Approach to Full Information Estimators for Linear and Certain Nonlinear Econometric Models," Econometrica, Econometric Society, vol. 43(4), pages 727-738, July.
    3. Dale W. Jorgenson & Jean-Jacques Laffont, 1974. "Efficient Estimation of Nonlinear Simultaneous Equations with Additive Disturbances," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 615-640, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. J. Campos, 1986. "Instrumental Variables Estimation of Dynamic Simultaneous Systems with ARMA Errors," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(1), pages 125-138.
    2. Hall, Anthony David & Pagan, Adrian Rodney, 1981. "The LIML and Related Estimators of an Equation with Moving Average Disturbances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 22(3), pages 719-730, October.
    3. Amemiya, Takeshi, 1983. "Non-linear regression models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 6, pages 333-389, Elsevier.
    4. Calzolari, Giorgio, 1992. "Stima delle equazioni simultanee non-lineari: una rassegna [Estimation of nonlinear simultaneous equations: a survey]," MPRA Paper 24123, University Library of Munich, Germany, revised 1992.
    5. Tsai, Grace Yueh-Hsiang, 1989. "A dynamic model of the U.S. cotton market with rational expectations," ISU General Staff Papers 1989010108000012168, Iowa State University, Department of Economics.
    6. Werner Antweiler & Daniel Trefler, 2002. "Increasing Returns and All That: A View from Trade," American Economic Review, American Economic Association, vol. 92(1), pages 93-119, March.
    7. Perekhozhuk, Oleksandr, 2007. "Marktstruktur und Preisbildung auf dem ukrainischen Markt für Rohmilch," Studies on the Agricultural and Food Sector in Transition Economies, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), volume 41, number 92322.
    8. Awad, Taleb Mohammad, 1987. "International monetary and exchange rate policies and world agricultural markets: the case of soybeans and soybean products," ISU General Staff Papers 198701010800009611, Iowa State University, Department of Economics.
    9. Patrick Fève & François Langot, 1995. "La méthode des moments généralisés et ses extensions : théorie et applications en macro-économie," Économie et Prévision, Programme National Persée, vol. 119(3), pages 139-170.
    10. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    11. Poterba, James M. & Summers, Lawrence H., 1983. "Dividend taxes, corporate investment, and `Q'," Journal of Public Economics, Elsevier, vol. 22(2), pages 135-167, November.
    12. Boskin, Michael J. & Hurd, Michael D., 1978. "The effect of social security on early retirement," Journal of Public Economics, Elsevier, vol. 10(3), pages 361-377, December.
    13. Hanqiao Zhang, 2024. "Exit Spillovers of Foreign-invested Enterprises in Shenzhen's Electronics Manufacturing Industry," Papers 2404.18009, arXiv.org.
    14. Antle, John M., 1981. "Implications Of Sequential Decision Making For Specification And Estimation Of Production Models," Working Papers 225694, University of California, Davis, Department of Agricultural and Resource Economics.
    15. Kadilli, Anjeza & Krishnakumar, Jaya, 2022. "Smooth Transition Simultaneous Equation Models," Journal of Economic Dynamics and Control, Elsevier, vol. 145(C).
    16. Hassan, Rashid M. & D'Silva, Brian & Hallam, A., 1989. "Normative Supply Response Analysis under Production Uncertainty: Irrigated Multicrop Farming Sector of Sudan," 1989 Occasional Paper Series No. 5 197677, International Association of Agricultural Economists.
    17. Büttner, Thiess & Prey, Hedwig, 1997. "Does active labour market policy affect structural unemployment? An empirical investigation for West German regions, 1986 to 1993," Discussion Papers 42, University of Konstanz, Center for International Labor Economics (CILE).
    18. Ben R. Craig & Joseph G. Haubrich, 2013. "Gross Loan Flows," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(2-3), pages 401-421, March.
    19. S. Darolles & Y. Fan & J. P. Florens & E. Renault, 2011. "Nonparametric Instrumental Regression," Econometrica, Econometric Society, vol. 79(5), pages 1541-1565, September.
    20. Laure Latruffe & Boris E. Bravo-Ureta & Alain Carpentier & Yann Desjeux & Víctor H. Moreira, 2017. "Subsidies and Technical Efficiency in Agriculture: Evidence from European Dairy Farms," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(3), pages 783-799.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:0090. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.