Abstract
A structural econometric model typically has the form y = f(x, u, θ), where y is a set of observed dependent variables; x a set of observed explanatory variables; u represents some unobserved variables, often called ‘error’ terms or stochastic terms; and θ is a parameter vector which is to be estimated. The main focus of econometric modelling is on specification of the function f. But estimation and inference also require assumptions about the statistical properties of u. Very little may be known about those variables, so one should be cautious of estimators that rely on a specific distribution function for them.
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Bibliography
Amemiya, T. 1983. Nonlinear regression models. In Handbook of Econometrics, Vol. 1, ed. Z. Griliches and M.D. Intriligator, Amsterdam: North-Holland.
Buckley, J. and James, I. 1979. Linear regression with censored data. Biometrika 66, 429–36.
Cosslett, S.R. 1983. Distribution-free maximum likelihood estimator of the binary choice model. Econometrica 51, 765–82.
Gallant, A.R. and Nychka, D.W. 1987. Semi-nonparametric maximum likelihood estimation. Econometrica 55.
Heckman, J. and Singer, B. 1984. A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 52, 271–320.
Kaplan, E.L. and Meier, P. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53, 457–81.
Lynden-Bell, D. 1971. A method of allowing for known observational selection in small samples aplied to 3CR quasars. Monthly Notices of the Royal Astronomical Society 155, 95–118.
Maddala, G.S. 1983. Limited Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.
Manski, C.F. 1974. Maximum score estimation of the stochastic utility model of choice. Journal of Econometrics 3, 205–28.
Manski, C.F. 1984. Adaptive estimation of non-linear regression models. Econometric Reviews 3, 145–94.
Manski, C.F. 1985. Semiparametric analysis of discrete response: asymptotic properties of the maximum score estimator. Journal of Econometrics 27, 303–33.
Miller, R. and Halpern, J. 1982. Regression with censored data. Biometrika 69, 521–31.
Phillips, P.C.B. 1983. ERA’s: a new approach to small sample theory. Econometrica 51, 1505–25.
Powell, J.L. 1984. Least absolute deviations estimation for the censored regression model. Journal of Econometrics 25, 303–25.
Powell, J.L. 1986. Symmetrically trimmed least squares estimation for tobit models. Econometrica 54, 1435–60.
Stoker, T.M. 1986. Consistent estimation of scaled coefficients. Econometrica 54, 1461–81.
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© 1990 Palgrave Macmillan, a division of Macmillan Publishers Limited
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Cosslett, S.R. (1990). Semiparametric Estimation. In: Eatwell, J., Milgate, M., Newman, P. (eds) Time Series and Statistics. The New Palgrave. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-20865-4_32
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DOI: https://doi.org/10.1007/978-1-349-20865-4_32
Publisher Name: Palgrave Macmillan, London
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