Skip to main content
Log in

Recent approaches to estimating Engel curves

  • Articles
  • Published:
Journal of Economics Aims and scope Submit manuscript

Abstract

Classical approaches of estimating cross-section Engel curves are based on parametric models. However, misspecification of a parametric model implies that information of structural nature might be masked. An alternative avoiding problems related to predetermined functional relations is the nonparametric approach. This paper surveys recent advances of nonparametric statistics in their relevance to estimating cross-section Engel curves.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bierens, H.J., and Pott-Butler, H.A. (1990): “Specification of Household Engel Curves by Nonparametric Regression.”Econometric Reviews 9: 123–184.

    Google Scholar 

  • Blundell, R., Pashardes, P., and Weber, G. (1993): “What Can We Learn About Consumer Demand Patterns from Micro Data?”American Economic Review 83: 570–597.

    Google Scholar 

  • Brockmann, M., Gasser, T., and Herrmann, E. (1993): “Locally Adaptive Bandwidth Choice for Kernel Regression Estimators.”Journal of the American Statistical Association 88: 1302–1309.

    Google Scholar 

  • Deaton, A.S. (1981):Three Essays on a Sri Lankan Household Survey (Living Standards Measurement Study, WD no. 11). Washington: The World Bank.

    Google Scholar 

  • — (1989): “Rice Prices and Income Distribution in Thailand: a Non Parametric Analysis.”Economic Journal 99: 1–37.

    Google Scholar 

  • Deaton, A.S. and Muellbauer, J. (1980): “An Almost Ideal Demand System.”American Economic Review 70: 312–336.

    Google Scholar 

  • Engel, E. (1857): “Die Produktions- und Consumptionsverhältnisse des Königreichs Sachsen.” [Reprinted inBulletin de Institut International de Statistique 9: 1–54 (1895).]

  • Enquête Budget de Famille (1979, 1984–85, 1989). Paris: Institut National de la Statistique et des Etudes Economiques, Division Condition de Vie des Menages.

  • Eubank, R. (1988):Spline Smoothing and Nonparametric Regression. New York: Dekker.

    Google Scholar 

  • Fan, J. (1993): “Local Linear Regression Smoothers and Their Mini-Max Efficiencies.”Annals of Statistics 21: 196–216.

    Google Scholar 

  • Fan, J., Gasser, T., Gijbels, I., Brockmann, M., and Engel, J. (1996): “On Nonparametric Estimation via Local Polynomial Regression.”Annals of Statistical Mathematics (to appear).

  • Friedman, J.H., and Silverman, B.W. (1989): “Flexible Parsimonious Smoothing and Additive Modelling.”Technometrics 3: 3–21.

    Google Scholar 

  • Gasser, T., and Müller, H.G. (1979): “Kernel Estimation of Regression Functions.” InSmoothing Techniques for Curve Estimation (Lecture Notes in Mathematics, vol. 757), edited by T. Gasser and M. Rosenblatt. Berlin: Springer.

    Google Scholar 

  • Gasser, T., Müller, H. G., and Mammitzsch, V. (1985): “Kernels for Nonparametric Curve Estimation.”Royal Statistical Society Journal Series B 47: 238–252.

    Google Scholar 

  • Gasser, T., Sroka, L., and Jennen-Steinmetz, C. (1986): “Residual Variance and Residual Pattern in Nonlinear Regression.”Biometrika 73: 625–633.

    Google Scholar 

  • Gasser, T., Kneip, A., and Köhler, W. (1991): “A Flexible and Fast Method for Automatic Smoothing.”Journal of the American Statistical Association 86: 643–652.

    Google Scholar 

  • Green, P.J., and Silverman, B.W. (1994):Nonparametric Regression and Generalized Linear Models: a Roughness Penalty Approach. London: Chapman and Hall.

    Google Scholar 

  • Härdle, W. (1990):Applied Nonparametric Regression. Cambridge: Cambridge University Press.

    Google Scholar 

  • Härdle, W., and Jerison, M. (1988): “The Evolution of Engel Curves over Time.” Discussion Paper no. A-178, Sonderforschungsbereich 303, University of Bonn, Bonn.

    Google Scholar 

  • — (1991): “Cross-section Engel Curves over Time.”Recherches Economiques de Louvain 57: 1525–1549.

    Google Scholar 

  • Härdle, W., and Kneip, A. (1992): “Testing a Regression Model when We Have a Smooth Alternative in Mind.” Discussion Paper no. A-389, Sonderforschungsbereich 303, University of Bonn, Bonn.

    Google Scholar 

  • Hastie, T., and Loader, C. (1993): “Local Regression: Automatic Kernel Carpentry.”Statistical Science 8: 120–143.

    Google Scholar 

  • Hildenbrand, W. (1994):Market Demand: Theory and Empirical Evidence. Princeton: Princeton University Press.

    Google Scholar 

  • Howe, H., Pollak, R.A., and Wales, T.J. (1979): “Theory and Time Series Estimation of the Quadratic Expenditure System.”Econometrica 47: 1231–1247.

    Google Scholar 

  • Kneip, A. (1993):Heterogeneity of Demand Behavior and the Space of Engel Curves. Habilitationsschrift, University of Bonn, Bonn.

    Google Scholar 

  • — (1994): “Nonparametric Estimation of Common Regressors for Similar Curve Data.”Annals of Statistics 22: 1386–1428.

    Google Scholar 

  • Kneip, A., and Engel, J. (1996): “A Remedy for Effective Regression Estimation Under Random Design.”Statistics (to appear).

  • Leser, C.E.V. (1983): “Form of Engel Functions.”Econometrica 31: 694–703.

    Google Scholar 

  • Lewbel, A. (1993): “The Rank of Demand Systems: Theory and Nonparametric Estimation.”Econometrica 59: 711–730.

    Google Scholar 

  • Müller, H.G. (1988):Nonparametric Regression Analysis of Longitudinal Data (Lecture Notes in Statistics, vol. 46). Berlin: Springer.

    Google Scholar 

  • Nadaraya, E.A. (1964): “On Estimating Regression.”Theory of Probability and Its Applications 9: 141–142.

    Google Scholar 

  • Pollak, R.A., and Wales, T.J. (1978): “Estimation of Complete Demand Systems from Household Budget Data.”American Economic Review 68: 348–359.

    Google Scholar 

  • Prais, S.J., and Houthakker, H.S. (1955):The Analysis of Family Budgets. Cambridge: Cambridge University Press.

    Google Scholar 

  • Reinsch, C.H. (1967): “Smoothing by Spline Functions.”Numerische Mathematik 16: 177–183.

    Google Scholar 

  • Ruppert, D., Sheather, S.J., and Wand, M.P. (1995): “An Effective Bandwidth Selector for Local Least Squares Regression.”Journal of the American Statistical Association 90: 1257–1270.

    Google Scholar 

  • Seifert, B., Brockmann, M., Engel, J., and Gasser, T. (1994): “Fast Algorithms for Nonparametric Curve Estimation.”Journal of Computational and Graphical Statistics 3: 192–213.

    Google Scholar 

  • Silverman, B. (1984): “Spline Smoothing: the Equivalent Variable Kernel Method.”Annals of Statistics 12: 898–916.

    Google Scholar 

  • Stoker, T.M. (1993): “Empirical Approaches to the Problem of Aggregation over Individuals.”Journal of Economic Literature 31: 1827–1874.

    Google Scholar 

  • Wand, M.P., and Jones, C. (1995): Kernel Smoothing. London: Chapman and Hall.

    Google Scholar 

  • Watson, G.S. (1964): “Smooth Regression Analysis.”Sankhy_a Series A 26: 359–372.

    Google Scholar 

  • Working, H. (1943): “Statistical Laws of Family Expenditure.”Journal of the American Statistical Association 38: 43–56.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Engel, J., Kneip, A. Recent approaches to estimating Engel curves. Zeitschr. f. Nationalökonomie 63, 187–212 (1996). https://doi.org/10.1007/BF01258672

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01258672

Keywords

JEL classification

Navigation