Empirical Economics

, Volume 22, Issue 3, pp 409–429 | Cite as

Household characteristics and consumption behaviour: A nonparametric approach

  • Miguel A. Delgado
  • Daniel Miles
Article

Abstract

In this paper we apply nonparametric methods in order to discuss some empirical aspects of household consumption behaviour. First, we study the differences in the consumption behaviour between household types. We find that, except for food, there are no clear significant differences. Secondly, we derive the functional form for the food Engel curve, using specification tests consistent in the direction of nonparametric alternatives. Finally, we use this specification to discuss the misleading conclusions that could be reached from a mechanic interpretation of the rejection of Hausman's test, when applied to test the exogeneity of expenditure. The data is obtained from the Spanish Expenditure Survey 1980–81 and 1990–91.

Key Words

Engel curves Household characteristics Nonparametric Estimation Consistent Specification Tests Expenditure Endogeneity 

JEL Classification System-Numbers

C14 C21 C52 D12 

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References

  1. [1]
    Alonso-Colmenares M, Lara A, Arévalo R, Ruiz Castillo J (1994) La encuesta de presupuestos familiares de 1980–1981. Documento de Trabajo 94-12(05), Universidad Carlos III de MadridGoogle Scholar
  2. [2]
    Baccouche R, Laisney F (1989) Simulation of VAT reforms for France using cross-section data. In: Florens JP, Ivaldi M, Laffont JJ, Laisney F (eds) Microeconometric: Surveys and applications, Basil Blackwell. OxfordGoogle Scholar
  3. [3]
    Banks J, Blundell R Lewbel A (1994) Quadratic Engel curves, indirect tax reform and welfare measurement. University College London, Discussion PaperGoogle Scholar
  4. [4]
    Bierens H (1991) A consistent conditional moment test of functional form. Econometrica 58:1443–58Google Scholar
  5. [5]
    Bierens H (1982) Consistent model specification tests. Journal of Econometrics 20:105–34Google Scholar
  6. [6]
    Bierens H Ploberger W (1996) Asymptotic theory of integrated conditional moment test against global and local alternatives. WP, Centre for Economic Research.Google Scholar
  7. [7]
    Blundell R (1988) Consumer behaviour: Theory and empirical evidence — A survey. The Economic Journal 98:16–65Google Scholar
  8. [8]
    Blundell R Pashardes P, Weber G (1993) What do we learn about consumer demand patterns from micro-data?. American Economic Review 83,3:570–97Google Scholar
  9. [9]
    Cardelús M, Arévalo R, Ruiz Castillo J (1995) La encuesta de presupuestos familiares de 1990–91. Documento de Trabajo 95-07(05), Universidad Carlos III de MadridGoogle Scholar
  10. [10]
    Davidson R, MacKinnon J (1993) Estimation and inference in econometrics. Oxford University PressGoogle Scholar
  11. [11]
    Deaton A (1986) Demand analysis. In: Griliches Z, Intrilligator MD (eds.) Handbook of econometrics, Elsevier Science PublishersGoogle Scholar
  12. [12]
    Deaton A, Muellbauer J (1980) Economics and consumer behaviour. Cambridge University Press, CambridgeGoogle Scholar
  13. [13]
    Deaton A, Ruiz-Castillo J, Thomas D (1989) The influence of household composition on household expenditure patterns: Theory and Spanish evidence. Journal of Political Economy 97,1:179–200Google Scholar
  14. [14]
    Ellison G, Ellison SF (1992) A nonparametric residual-based specification test: Asymptotic, finite sample, and computational properties. manuscriptGoogle Scholar
  15. [15]
    Gorman WM (1981) Some Engel curves. In: Deaton A (ed.) The theory and measurement of consumer behaviour, Cambridge University Press, CambridgeGoogle Scholar
  16. [16]
    Härdle W (1990) Applied nonparametric regression. Cambridge University Press, CambridgeGoogle Scholar
  17. [17]
    Härdle W, Hildenbrand W, Jerison M (1991) Empirical evidence on the law of demand. Econometrica 59:1525–49Google Scholar
  18. [18]
    Härdle W, Jerison M (1991) Cross section Engel curves over time. Recherches Economiques de Louvain 57(4)Google Scholar
  19. [19]
    Härdle W, Mammen E (1993) Comparing nonparametric versus parametric regression fits. The Annals of Statistics 21(4):1926–47Google Scholar
  20. [20]
    Härdle W, Proenca I (1993) A bootstrap test for single index models. Discussion Paper 93-25, CORE-Institut de Statistique, Universite Catholique de LouvainGoogle Scholar
  21. [21]
    Härdle W, Stoker TM (1989) Investigating smooth multiple regression by the method of average derivatives. Journal of the American Statistical Association 84:986–995Google Scholar
  22. [22]
    Hausman JA (1986) Specification and estimation of simultaneous equation models. In: Griliches Z, Intriligator M (eds) Handbook of econometrics, North-HollandGoogle Scholar
  23. [23]
    Horowitz JL, Härdle W (1994) Testing a parametric model against a semiparametric alternative. Econometric Theory 10:821–48Google Scholar
  24. [24]
    Lewbel A (1991) The rank of demand systems: Theory and nonparametric estimation. Econometrica 59:711–30Google Scholar
  25. [25]
    Miles D, Mora J (1997) On the performance of specification tests based on nonparametric estimates. IVIE WP, forthcomingGoogle Scholar
  26. [26]
    Mroz T (1987) The sensitivity of an empirical model of married women's hours of work to economic and statistical assumptions. Econometrica 55,4:765–99Google Scholar
  27. [27]
    Nichele V, Robin JM (1995) Simulation of indirect tax reforms using pooled micro and macro French data. Journal of Public Economics 56:225–244Google Scholar
  28. [28]
    Proença I (1993) On the performance of the H−H test Discussion Paper 93-05, CORE-Institut de Statistique, Universite Catholique de LouvainGoogle Scholar
  29. [29]
    Spencer DE, Berk K (1981) A limited information specification tets. Econometrica 49,4:1079–1085Google Scholar
  30. [30]
    Stoker TM (1991) Lectures on semiparametric econometrics. CORE Lecture Series, CORE Foundation, Universite Catholique de LouvainGoogle Scholar
  31. [31]
    Stute W (1996) Nonparametric model checks for regression. The Annals of Statistics, forthcomingGoogle Scholar
  32. [32]
    Stute W, González-Manteiga W, Presedo M (1996) Bootstrap approximations in model checks for regression. Journal of the American Statistical Association, forthcomingGoogle Scholar
  33. [33]
    Wand MP, Jones MC (1995) Kernel smoothing. Chapman & HallGoogle Scholar
  34. [34]
    Wooldridge JM (1996) Estimating systems of equations with different instruments for different equations. 74,2:387–406Google Scholar

Copyright information

© Physica-Verlag 1997

Authors and Affiliations

  • Miguel A. Delgado
    • 1
  • Daniel Miles
    • 1
  1. 1.Universidad Carlos III de MadridSpain

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