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On the Use of Artificial Regressions in Certain Microeconometric Models


  • Orme, Chris


Conditional moment tests check to see whether or not population moment equalities, implied by the null model specification, hold approximately in the sample. Asymptotically valid conditional statistics can easily be calculated from the output of a so-called outer product of the gradient (OPG) artificial regression. However, several studies have now found that this OPG variant exhibits extremely poor finite sample behavior and that significant improvements can be made by employing the efficient variant. In the light of such evidence, this paper develops new artificial regressions that can be used to calculate the efficient variant of the test statistic. These artificial regressions can also serve several other purposes, including the construction of Hausmantype tests of parameter estimator consistency.

Suggested Citation

  • Orme, Chris, 1995. "On the Use of Artificial Regressions in Certain Microeconometric Models," Econometric Theory, Cambridge University Press, vol. 11(02), pages 290-305, February.
  • Handle: RePEc:cup:etheor:v:11:y:1995:i:02:p:290-305_00

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    References listed on IDEAS

    1. Hannan, E. J., 1981. "Estimating the dimension of a linear system," Journal of Multivariate Analysis, Elsevier, vol. 11(4), pages 459-473, December.
    2. Phillips, P C B, 1991. "To Criticize the Critics: An Objective Bayesian Analysis of Stochastic Trends," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 333-364, Oct.-Dec..
    3. Park, Joon Y. & Phillips, Peter C.B., 1989. "Statistical Inference in Regressions with Integrated Processes: Part 2," Econometric Theory, Cambridge University Press, vol. 5(01), pages 95-131, April.
    4. Park, Joon Y. & Phillips, Peter C.B., 1988. "Statistical Inference in Regressions with Integrated Processes: Part 1," Econometric Theory, Cambridge University Press, vol. 4(03), pages 468-497, December.
    5. Phillips, Peter C. B., 1995. "Bayesian model selection and prediction with empirical applications," Journal of Econometrics, Elsevier, vol. 69(1), pages 289-331, September.
    6. Peter C.B. Phillips & Werner Ploberger, 1991. "Time Series Modelling with a Bayesian Frame of Reference: 1. Concepts and Illustrations," Cowles Foundation Discussion Papers 980, Cowles Foundation for Research in Economics, Yale University.
    7. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
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    Cited by:

    1. Andrea Morone & Piergiuseppe Morone, 2012. "Are small groups Expected Utility?," Working Papers 2012/08, Economics Department, Universitat Jaume I, Castellón (Spain).
    2. Davidson, R. & MacKinnon & J.G., 1999. "Artificial Regressions," G.R.E.Q.A.M. 99a04, Universite Aix-Marseille III.
    3. Hoetker, Glenn, 2004. "Confounded Coefficients: Accurately Comparing Logit and Probit Coefficients across Groups," Working Papers 03-0100, University of Illinois at Urbana-Champaign, College of Business.
    4. Skeels, Christopher L. & Vella, Francis, 1999. "A Monte Carlo investigation of the sampling behavior of conditional moment tests in Tobit and Probit models," Journal of Econometrics, Elsevier, vol. 92(2), pages 275-294, October.
    5. Jakusch, Sven Thorsten & Meyer, Steffen & Hackethal, Andreas, 2016. "Taming models of prospect theory in the Wild? Estimation of Vlcek and Hens (2011)," SAFE Working Paper Series 146, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    6. Jakusch, Sven Thorsten, 2016. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    7. A. Morone & P. Morone, 2014. "Estimating individual and group preference functionals using experimental data," Theory and Decision, Springer, vol. 77(3), pages 403-422, October.

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