IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i11p2763-2775.html
   My bibliography  Save this article

Semiparametric indirect utility and consumer demand

Author

Listed:
  • 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.

Suggested Citation

  • Pendakur, Krishna & Scholz, Michael & Sperlich, Stefan, 2010. "Semiparametric indirect utility and consumer demand," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2763-2775, November.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2763-2775
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00148-9
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Richard Blundell & Xiaohong Chen & Dennis Kristensen, 2003. "Nonparametric IV estimation of shape-invariant Engel curves," CeMMAP working papers CWP15/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. 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.
    4. Jorgenson, Dale W & Lau, Lawrence J & Stoker, Thomas M, 1980. "Welfare Comparison under Exact Aggregation," American Economic Review, American Economic Association, vol. 70(2), pages 268-272, May.
    5. Pendakur, Krishna, 2002. "Taking prices seriously in the measurement of inequality," Journal of Public Economics, Elsevier, vol. 86(1), pages 47-69, October.
    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, Decembrie.
    8. Arthur Lewbel & Krishna Pendakur, 2009. "Tricks with Hicks: The EASI Demand System," American Economic Review, American Economic Association, vol. 99(3), pages 827-863, June.
    9. 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.
    10. 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.
    11. Richard Blundell & Alan Duncan & Krishna Pendakur, 1998. "Semiparametric estimation and consumer demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(5), pages 435-461.
    12. Deaton, Angus S & Muellbauer, John, 1980. "An Almost Ideal Demand System," American Economic Review, American Economic Association, vol. 70(3), pages 312-326, June.
    13. Slottje, Daniel, 2008. "Estimating demand systems and measuring consumer preferences," Journal of Econometrics, Elsevier, vol. 147(2), pages 207-209, December.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stefan Sperlich & Raoul Theler, 2015. "Modeling heterogeneity: a praise for varying-coefficient models in causal analysis," Computational Statistics, Springer, vol. 30(3), pages 693-718, September.
    2. Stefan Sperlich & Jose-Ramon Uriarte, 2019. "The economics of minority language use: theory and empirical evidence for a language game model," Papers 1908.11604, arXiv.org.
    3. Fabrizio Balli, 2012. "Are Traditional Equivalence Scales Still Useful? A Review and A Possible Answer," Department of Economics University of Siena 656, Department of Economics, University of Siena.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Christian Dudel & Jan Marvin Garbuszus & Julian Schmied, 2021. "Assessing differences in household needs: a comparison of approaches for the estimation of equivalence scales using German expenditure data," Empirical Economics, Springer, vol. 60(4), pages 1629-1659, April.
    3. Xiaodong Gong & Laurie Brown, 2017. "The Impacts of the Presence of Disabled Members on Intra-household Allocation in Older Australian Households," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 50(4), pages 398-411, December.
    4. Arthur Lewbel & Krishna Pendakur, 2009. "Tricks with Hicks: The EASI Demand System," American Economic Review, American Economic Association, vol. 99(3), pages 827-863, June.
    5. Paul Blacklow & Aaron Nicholas & Ranjan Ray, 2010. "Demographic Demand Systems With Application To Equivalence Scales Estimation And Inequality Analysis: The Australian Evidence," Australian Economic Papers, Wiley Blackwell, vol. 49(3), pages 161-179, September.
    6. Nicholas Oulton, 2012. "How To Measure Living Standards And Productivity," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 58(3), pages 424-456, September.
    7. Gong, X. & van Soest, A.H.O. & Zhang, P., 2000. "Sexual Bias and Household Consumption : A Semiparametic Analysis of Engel curves in Rural China," Other publications TiSEM 896cf4d1-37e5-490b-9e05-4, Tilburg University, School of Economics and Management.
    8. Schulte, Isabella & Heindl, Peter, 2017. "Price and income elasticities of residential energy demand in Germany," Energy Policy, Elsevier, vol. 102(C), pages 512-528.
    9. Simona Bigerna & Carlo Andrea Bollino & Maria Chiara D’Errico, 2020. "A general expenditure system for estimation of consumer demand functions," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(3), pages 1071-1088, October.
    10. 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.
    11. Li, Lianyou & Song, Ze & Ma, Chao, 2015. "Engel curves and price elasticity in urban Chinese Households," Economic Modelling, Elsevier, vol. 44(C), pages 236-242.
    12. Krishna Pendakur, 2018. "Welfare analysis when people are different," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(2), pages 321-360, May.
    13. Prize Committee, Nobel, 2015. "Consumption, Poverty, and Welfare," Nobel Prize in Economics documents 2015-2, Nobel Prize Committee.
    14. De Agostini, Paola, 2014. "The effect of food prices and household income on the British diet," ISER Working Paper Series 2014-10, Institute for Social and Economic Research.
    15. Ingvild Almås & Anders Kjelsrud & Rohini Somanathan, 2019. "A Behavior‐Based Approach to the Estimation of Poverty in India," Scandinavian Journal of Economics, Wiley Blackwell, vol. 121(1), pages 182-224, January.
    16. Barnett, William A. & Serletis, Apostolos, 2008. "Consumer preferences and demand systems," Journal of Econometrics, Elsevier, vol. 147(2), pages 210-224, December.
    17. Noriko Amano, 2018. "Nutrition Inequality: The Role of Prices, Income, and Preferences," 2018 Meeting Papers 453, Society for Economic Dynamics.
    18. Korir, Lilian & Rizov, Marian & Ruto, Eric, 2020. "Food security in Kenya: Insights from a household food demand model," Economic Modelling, Elsevier, vol. 92(C), pages 99-108.
    19. Irz, Xavier & Mazzocchi, Mario & Réquillart, Vincent & Soler, Louis-Georges, 2015. "Research in Food Economics: past trends and new challenges," Revue d'Etudes en Agriculture et Environnement, Editions NecPlus, vol. 96(01), pages 187-237, March.
    20. Chavas, Jean-Paul, 2013. "On Demand Analysis and Dynamics: A Benefit Function Approach," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149683, Agricultural and Applied Economics Association.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2763-2775. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.