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Semiparametric indirect utility and consumer demand

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Author Info

  • Pendakur, Krishna
  • Scholz, Michael
  • Sperlich, Stefan

Abstract

A semiparametric model of consumer demand is considered. In the model, the indirect utility function is specified as a partially linear, where utility is nonparametric in expenditure and parametric (with fixed- or varying-coefficients) in prices. Because the starting point is a model of indirect utility, rationality restrictions like homogeneity and Slutsky symmetry are easily imposed. The resulting model for expenditure shares (as functions of expenditures and prices) is locally given by a fraction whose numerator is partially linear, but whose denominator is nonconstant and given by the derivative of the numerator. The basic insight is that given a local polynomial model for the numerator, the denominator is given by a lower order local polynomial. The model can thus be estimated using modified versions of local polynomial modeling techniques. For inference, a new asymmetric version of the wild bootstrap is introduced. Monte Carlo evidence that the proposed technique's work is provided as well as an implementation of the model on Canadian consumer expenditure and price micro-data.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 54 (2010)
Issue (Month): 11 (November)
Pages: 2763-2775

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Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2763-2775

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Web page: http://www.elsevier.com/locate/csda

Related research

Keywords: Consumer demand Engel curves Semiparametric econometrics Wild bootstrap with asymmetric errors;

References

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  1. Slottje, Daniel, 2008. "Estimating demand systems and measuring consumer preferences," Journal of Econometrics, Elsevier, vol. 147(2), pages 207-209, December.
  2. Arthur Lewbel & Krishna Pendakur, 2009. "Tricks with Hicks: The EASI Demand System," American Economic Review, American Economic Association, vol. 99(3), pages 827-63, June.
  3. Pendakur, K., 1999. "Taking Prices Seriously in the Measurement of Inequality," Working Papers dp99-7, CRABE, Department of Economics, Simon Fraser University.
  4. Mazzocchi, Mario, 2006. "Time patterns in UK demand for alcohol and tobacco: an application of the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2191-2205, May.
  5. Camilo Sarmiento, 2005. "A Varying Coefficient Approach to Global Flexibility in Demand Analysis: A Semiparametric Approximation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(1), pages 38-47.
  6. Haag, Berthold R. & Hoderlein, Stefan & Pendakur, Krishna, 2009. "Testing and imposing Slutsky symmetry in nonparametric demand systems," Journal of Econometrics, Elsevier, vol. 153(1), pages 33-50, November.
  7. Mas-Colell, Andreu & Whinston, Michael D. & Green, Jerry R., 1995. "Microeconomic Theory," OUP Catalogue, Oxford University Press, number 9780195102680.
  8. Deschamps, Philippe J., 1988. "A note on the maximum likehood estimation of allocation systems," Computational Statistics & Data Analysis, Elsevier, vol. 6(2), pages 109-112, March.
  9. Deaton, Angus S & Muellbauer, John, 1980. "An Almost Ideal Demand System," American Economic Review, American Economic Association, vol. 70(3), pages 312-26, June.
  10. James Banks & Richard Blundell & Arthur Lewbel, 1997. "Quadratic Engel Curves And Consumer Demand," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 527-539, November.
  11. Krishna Pendakur & Stefan Sperlich, 2010. "Semiparametric estimation of consumer demand systems in real expenditure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(3), pages 420-457.
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