IDEAS home Printed from https://ideas.repec.org/p/bbk/bbkefp/0406.html
   My bibliography  Save this paper

Invertibility of Nonparametric Stochastic Demand Functions

Author

Listed:
  • Walter Beckert

    (Department of Economics, Mathematics & Statistics, Birkbeck)

  • Richard Blundell

Abstract

This paper considers structural nonparametric random utility models for continuous choice variables. It provides sufficient conditions on the structural model to yield reduced-form systems of nonparametric stochastic demand functions that constitute a global homeomorphism between demands and random utility components. Such homeomorphic relationships are essential for global identification of the structural model, the existence of well-specified probability models for choice variables and for the analysis of revealed stochastic preference.

Suggested Citation

  • Walter Beckert & Richard Blundell, 2004. "Invertibility of Nonparametric Stochastic Demand Functions," Birkbeck Working Papers in Economics and Finance 0406, Birkbeck, Department of Economics, Mathematics & Statistics.
  • Handle: RePEc:bbk:bbkefp:0406
    as

    Download full text from publisher

    File URL: https://eprints.bbk.ac.uk/id/eprint/27108
    File Function: First version, 2004
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gourieroux, C & Laffont, J J & Monfort, A, 1980. "Coherency Conditions in Simultaneous Linear Equation Models with Endogenous Switching Regimes," Econometrica, Econometric Society, vol. 48(3), pages 675-695, April.
    2. Daniel McFadden, 2005. "Revealed stochastic preference: a synthesis," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 26(2), pages 245-264, August.
    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. McAleer, Michael & Medeiros, Marcelo C. & Slottje, Daniel, 2008. "A neural network demand system with heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 147(2), pages 359-371, December.
    2. Knobel, Alexander (Кнобель, Александр) & Chentsov, Alexander (Ченцов, Александр), 2018. "The Impact of Exchange Rates and Their Volatility on Russia's Foreign Trade, Taking into Account its Membership in EAEU [Влияние Обменных Курсов И Их Волатильности На Внешнюю Торговлю России С Учет," Working Papers 061824, Russian Presidential Academy of National Economy and Public Administration.
    3. Mette Lunde Christensen, 2002. "Heterogeneity in consumer demands and the income effect: evidence from panel data," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 C4-1, International Conferences on Panel Data.
    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. Dharmasena, Senarath & Capps, Oral, Jr., 2014. "U.S. Demand for Wellness and Functional Beverages and Implications on Nutritional Intake: An Application of EASI Demand System Capturing Diverse Preference Heterogeniety," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169811, Agricultural and Applied Economics Association.
    6. Arthur Lewbel, 2006. "Modeling Heterogeneity," Boston College Working Papers in Economics 650, Boston College Department of Economics.

    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. Arthur Lewbel, 2006. "Modeling Heterogeneity," Boston College Working Papers in Economics 650, Boston College Department of Economics.
    2. Yuichi Kitamura & Jörg Stoye, 2013. "Nonparametric analysis of random utility models: testing," CeMMAP working papers 36/13, Institute for Fiscal Studies.
    3. Marc Henry & Ismael Mourifié, 2013. "Euclidean Revealed Preferences: Testing The Spatial Voting Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 650-666, June.
    4. S. Bogan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2021. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 96-120, July.
    5. Changkuk Im & John Rehbeck, 2021. "Non-rationalizable Individuals, Stochastic Rationalizability, and Sampling," Papers 2102.03436, arXiv.org, revised Oct 2021.
    6. Soren Blomquist & Anil Kumar & Che-Yuan Liang & Whitney K. Newey, 2022. "Nonlinear Budget Set Regressions for the Random Utility Model," Working Papers 2219, Federal Reserve Bank of Dallas.
    7. Cherchye, Laurens & Demuynck, Thomas & De Rock, Bram, 2018. "Transitivity of preferences: when does it matter?," Theoretical Economics, Econometric Society, vol. 13(3), September.
    8. Hans G. Bloemen & Arie Kapteyn, 2008. "The estimation of utility-consistent labor supply models by means of simulated scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(4), pages 395-422.
    9. Millimet, Daniel L. & Tchernis, Rusty, 2008. "Estimating high-dimensional demand systems in the presence of many binding non-negativity constraints," Journal of Econometrics, Elsevier, vol. 147(2), pages 384-395, December.
    10. repec:zbw:rwirep:0070 is not listed on IDEAS
    11. Philip A. Haile & Ali Hortaçsu & Grigory Kosenok, 2008. "On the Empirical Content of Quantal Response Equilibrium," American Economic Review, American Economic Association, vol. 98(1), pages 180-200, March.
    12. Kadilli, Anjeza & Krishnakumar, Jaya, 2022. "Smooth Transition Simultaneous Equation Models," Journal of Economic Dynamics and Control, Elsevier, vol. 145(C).
    13. Fortin, Bernard & Lacroix, Guy & Villeval, Marie-Claire, 2007. "Tax evasion and social interactions," Journal of Public Economics, Elsevier, vol. 91(11-12), pages 2089-2112, December.
    14. Ascari, Guido & Mavroeidis, Sophocles, 2022. "The unbearable lightness of equilibria in a low interest rate environment," Journal of Monetary Economics, Elsevier, vol. 127(C), pages 1-17.
    15. Yuichi Kitamura & Jörg Stoye, 2018. "Nonparametric Analysis of Random Utility Models," Econometrica, Econometric Society, vol. 86(6), pages 1883-1909, November.
    16. Sickles, Robin C & Taubman, Paul, 1986. "An Analysis of the Health and Retirement Status of the Elderly," Econometrica, Econometric Society, vol. 54(6), pages 1339-1356, November.
    17. Ho, Kate & Rosen, Adam M., 2015. "Partial Identification in Applied Research: Benefits and Challenges," CEPR Discussion Papers 10883, C.E.P.R. Discussion Papers.
    18. Yuichi Kitamura & Jorg Stoye, 2019. "Nonparametric Counterfactuals in Random Utility Models," Papers 1902.08350, arXiv.org, revised May 2019.
    19. Arthur Lewbel & Krishna Pendakur, 2017. "Unobserved Preference Heterogeneity in Demand Using Generalized Random Coefficients," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 1100-1148.
    20. Francesca Molinari, 2020. "Microeconometrics with Partial Identi?cation," CeMMAP working papers CWP15/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. V A Hajivassiliou & Frédérique Savignac & Frédérique Savignac, 2019. "Novel Approaches to Coherency Conditions in Dynamic LDV Models: Quantifying Financing Constraints and a Firm's Decision and Ability to Innovate," STICERD - Econometrics Paper Series 606, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

    More about this item

    Keywords

    nonparametric random utility model; stochastic demand; global homeomorphism; coherency;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D1 - Microeconomics - - Household Behavior

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:bbk:bbkefp:0406. 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: the person in charge (email available below). General contact details of provider: https://www.bbk.ac.uk/departments/ems/ .

    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.