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NOTE ON FINITE APPROXIMATIONS OF THE ASYMPTOTICALLY IDEAL MODEL

Published online by Cambridge University Press:  01 September 2008

Apostolos Serletis and
Asghar Shahmoradi
Apostolos Serletis*
Affiliation:
University of Calgary
Asghar Shahmoradi
Affiliation:
University of Tehran
*
Address correspondence to: Apostolos Serletis, Department of Economics, University of Calgary, Calgary, Alberta, T2N 1N4, Canada; e-mail: Serletis@ucalgary.ca.
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Abstract

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This note builds on recent work by Serletis and Shahmoradi [Macroeconomic Dynamics 9 (2005), 542–559] and estimates the AIM model at different degrees of approximation, using the same optimization procedures as in Gallant and Golub [Journal of Econometrics. 26 (1984), 295–321]. We estimate the models subject to regularity and provide a comparison between the different versions. We argue that the AIM(3) model estimated subject to global curvature currently provides the best specification for research in semiparametric modeling of consumer demand systems.

Keywords

Flexible Functional FormsAsymptotically Ideal ModelGlobal Curvature Restrictions

Information

Type
Note
Information
Macroeconomic Dynamics , Volume 12 , Issue 4 , September 2008, pp. 579 - 590
Copyright
Copyright © Cambridge University Press 2008

References

REFERENCES

Barnett, William A. (2002) Tastes and technology: Curvature is not sufficient for regularity. Journal of Econometrics 108, 199–202.Google Scholar
Barnett, William A., Geweke, John, and Wolfe, Michael (1991) Semi-nonparametric Bayesian estimation of the asymptotically ideal model. Journal of Econometrics 49, 5–50.CrossRefGoogle Scholar
Barnett, William A., Geweke, John, and Yue, Piyu (1991) Semi-nonparametric Bayesian estimation of the asymptotically ideal model: The AIM demand system. In Barnett, W. A., Powell, J., and Tauchen, G. (eds.), Nonparametric and Semiparametric Methods, pp. 127–173. Cambridge: Cambridge University Press.Google Scholar
Barnett, William A. and Jonas, A. (1983) The Muntz–Szatz demand system: An application of a globally well-behaved series expansion. Economics Letters 11, 337–342.Google Scholar
Barnett, William A. and Pasupathy, Meenakshi (2003) Regularity of the generalized quadratic production model: A counterexample. Econometric Reviews 22, 135–154.Google Scholar
Barnett, William A. and Yue, P. (1988) Semi-parametric estimation of the asymptotically ideal model: The AIM demand system. In Rhodes, G. and Fomby, T. B. (eds), Advances in Econometrics, Vol. VII, pp. 229–251. Greenwich, CT: JAI Press.Google Scholar
Burguete, Jose F., Gallant, A. Ronald, and Souza, Geraldo (1982) On unification of the asymptotic theory of nonlinear econometric models. Econometric Reviews 1, 151–190.CrossRefGoogle Scholar
Diewert, W. Erwin (1971) An application of the shephard duality theorem: A generalized Leontief production function. Journal of Political Economy 79, 481–507.Google Scholar
Eastwood, Brian J. and Gallant, A. Ronald (1991) Adaptive rules for seminonparametric estimators that achieve asymptotic normality. Econometric Theory 7, 307–340.Google Scholar
Gallant, A. Ronald (1981) On the bias in flexible functcional forms and an essentially unbiased form: The Fourier flexible form. Journal of Econometrics 15, 211–245.Google Scholar
Gallant, A. Ronald and Golub, Gene H. (1984) Imposing curvature restrictions on flexible functional forms. Journal of Econometrics 26, 295–321.Google Scholar
Huber, Peter J. (1981) Robust Statistics. New York: Wiley.Google Scholar
Serletis, Apostolos and Shahmoradi, Asghar (2005) Semi-nonparametric estimates of the demand for money in the United States. Macroeconomic Dynamics 9, 542–559.Google Scholar
Zellner, A. (1962) An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association 57, 348–368.Google Scholar