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Cross Section Engel Curves over Time

Published online by Cambridge University Press:  17 August 2016

Wolfgang Härdle
Affiliation:
CORE
Michael Jerison
Affiliation:
Department of Economics, SUNY
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Summary

Methods for nonparametric estimation and comparison of cross section Engel curves are presented and applied to U.K. expenditure data. Real Engel curves (with quantity demanded and real total expenditure on the axes) vary over time, but their shapes are generally quite stable. Mean normalized Engel curves are defined and are found not to vary greatly over time. Consequences of such invariance for the testing of microeconomic demand models are investigated.

Résumé

Résumé

Cet article présente des méthodes d'estimation non-paramétrique et de comparaison en coupe de courbes d'Engel et les applique à des données de dépenses au Royaume-Uni. Les courbes d'Engel réelles (avec quantité demandée et dépense totale réelle le long des axes) varient dans le temps mais leurs formes sont généralement stables. Les courbes d'Engel moyennes normalisées sont ensuite définies. Il est montré qu'elles varient peu dans le temps et les conséquences de cette invariance quant à l'estimation de modèles micro-économiques de demande sont étudiées.

Keywords

Type
Research Article
Copyright
Copyright © Université catholique de Louvain, Institut de recherches économiques et sociales 1991 

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Footnotes

*

This paper was written at the University of Bonn. We would like to express our gratitude to the Deutsche Forschungsgemeinschaft, SFB 303 for financial support. We are also grateful for numerous discussions with Kurt Hildenbrand, Werner Hildenbrand, Heinz-Peter Schmitz and Tom Stoker. We thank Angus Deaton, Arthur Lewbel and three anonymous referees for comments on earlier versions of the paper/The data were made available by the ESRC Data Archive at the University of Essex.

References

REFERENCES

Afriat, S.N. (1967), The construction of a utility function from expenditure data, International Economic Review 8, 6777..Google Scholar
Antonelli, S.N. (1886), Sulla teoria mathematica della economia politica, English translation in: Preferences, Utility and Demand (Chipman, J.S. et al. Eds.), p. 333360, Harcourt Brace Jovanovich, New York 1971..Google Scholar
Barnett, W. and Lee, Y.W. (1985), The global properties of the minflex Laurent, generalized Leontief, and transiog flexible functional forms, Econometrica 53, 14211437.Google Scholar
Bickel, R. J. and Rosenblatt, M. (1973), On some global measures of the deviations of density function estimates, Annals of Statistics, 1, 10711091.Google Scholar
Bierens, H. J. (1987), Kernel estimators of regression functions, in Adva nces in Econometrics (Bewley, T. F., Ed.), Cambridge University Press, New York.Google Scholar
Bierens, H. J. and Pott-Buter, H. A. (1990), Specification of household En gel curves by Nonparametric regression, Econometric Reviews 9, 123184.Google Scholar
Blundell, R., Pashardes, P. and Weber, G. (1988), What do we learn about consumer demand patterns from micro-data?, Institute for Fiscal Studies, Paper 88/10.Google Scholar
Deaton, A. and Muellbauer, J. (1980), An almost ideal demand system, American Economic Review 70, 312326.Google Scholar
Gazette, Employment (1982), Department of Employment, Her Majesty's Stationery Office, London,.Google Scholar
Family Expenditure Survey, Annual Base Tapes (19681983), Department of Em ployment, Statistics Division, Her Majesty's Stationery Office, London.Google Scholar
Gallant, R. (1981), On the bias in flexible functional forms, and an essentially unbiased form: the Fourier functional form, Journal of Econometrics 15, 211245.Google Scholar
Gorman, W.M. (1953), Community preference fields, Econometrica, 21, 6380.Google Scholar
Gorman, W.M. (1981), Some Engel curves, in:, Essays in the Theory and Measurement of Consumer Behaviour (Deaton, A., ed.), Cambridge University Press, Cambridge.Google Scholar
Gózalo, P.L. (1989), Nonparametric analysis of cross-section demand functi ons, Dept. of Economics, Brown University.Google Scholar
Härdle, W. (1990), Applied Nonparametric Regression, Econometric Society Monograph Series, Cambridge University Press, Cambridge.Google Scholar
Härdle, W. and Marron, J.S. (1985), Optimal Bandwidth Selection in Nonparametric Regression Function Estimation, Annals of Statistics, 13, 14651481.Google Scholar
Hausman, J.A., Newey, W.K. and Powell, J.L. (1988), Nonlinear errors in variables: estimation of some Engel curves,.Google Scholar
Hildenbrand, W. (1985), A problem in demand aggregation; per capita demand as a function of per capita expenditure, Discussion paper A-12, SFB 303, University of Bonn.Google Scholar
Hildenbrand, K. and Hildenbrand, W. (1986), On the mean income effect: a data analysis of the U.K. family expenditure survey, in: Contributions to Mathematical Economics (Hildenbrand, W., Mas-Cclell, A., eds.) North Holland.Google Scholar
Jerison, M. (1984), Aggregation and Pairwise Aggregation of Demand when the distribution of income is fixed, J. Economic Theory, 33, 131.Google Scholar
Jerison, M. (1992a), Cross section invariance and microeconomic demand models,.Google Scholar
Jerison, M. (1992b), Functional forms for consumer preference aggregation,.Google Scholar
Jorgenson, D.W., L.J., Lau. and Stoker, T. (1982), The transcendental logarithmic model of aggregate consumer behaviour, in Advances in Econometrics. Basmann, R. and Rhodes, G., eds., JAI Press, Greenwich, CT Google Scholar
Keen, M. (1986), Zero expenditures and the estimation of Engel curves, J ournal of Applied Econometrics, 1, 277286.Google Scholar
Kneip, A. (1991), Identifying low dimensional regression models: a self-modeling aproach, Universität Bonn.Google Scholar
Lau, L.J. (1982), A note on the fundamental theorem of exact aggregation, Economics Letters, 9, 119126.Google Scholar
Leser, C.E. (1963), Forms of Engel functions, Econometrica, 31, 694703.Google Scholar
Lewbel, A. (1988), The rank of demand systems: theory and nonparametric estimation, Econometrica, 59, 711730.Google Scholar
Liero, H. (1982), On the maximal deviation of the kernel regression function estimate, Math. Operationsforsch, Statist., Ser. Statistics, 13, 171182.Google Scholar
Nadaraya, E. A. (1964), On Estimating Regression., Theory Prob. Appl. 10, 186190.Google Scholar
Nataf, A. (1953), Sur des questions d'aggregation en econometrie, Publ. Inst Statist. Univ. Paris, 2, 561.Google Scholar
Neter, J. and Wasserman, W. (1974), Applied Linear Statistical Models, Irwin-Dorsey Ltd., Georgetown, Ontario.Google Scholar
Pollak, R.A. and Wales, T.J (1978), Estimation of complete demand systems from household budget data: the linear and quadratic expenditure systems,, American Economic Review 68, 348359.Google Scholar
Prais, S.J. and Houthakker, H.S. (1955), The Analysis of Family Budgets, Cambridge Univ. Press, Cambridge.Google Scholar
Pudney, S. (1987), On the estimation of Engel curves, London School of Economics discussion paper.Google Scholar
Stoker, T.M. (1986a), Aggregation, efficiency and cross-section regression, Econometrica 54, 171192.Google Scholar
Stoker, T.M (1986 b), Simple tests of distributional effects on macroeconomic equations,, Journal of Political Economy 94, 763795.Google Scholar
Varían, H.R. (1982), The nonparametric approach to demand analysis, Econometrica 50, 945–97..Google Scholar
Varían, H.R. (1983), Nonparametric tests of models of consumer bahavior, Review of Economic Studies, 50, 99110.Google Scholar
Watson, G.S. (1964), Smooth regression analysis, Sankhyā, Series A, 26, 359372.Google Scholar
Working, H. (1943), Statistical laws of family expenditure, Journal of the American Statistical Association 38, 4356.Google Scholar