IDEAS home Printed from https://ideas.repec.org/a/cup/jagaec/v45y2013i01p79-94_00.html
   My bibliography  Save this article

A Semiparametric Approach to Analyzing Differentiated Agricultural Products

Author

Listed:
  • Bekkerman, Anton
  • Brester, Gary W.
  • McDonald, Tyrel J.

Abstract

When consumers have heterogeneous perceptions about product quality, traditional parametric methods may not provide accurate marginal valuation estimates of a product's characteristics. A quantile regression framework can be used to estimate valuations of product characteristics when quality perceptions are not homogeneous. Semiparametric quantile regressions provide identification and quantification of heterogeneous marginal valuation effects across a conditional price distribution. Using purchase price data from a bull auction, we show that there are nonconstant marginal valuations of bull carcass and growth traits. Improved understanding of product characteristic valuations across differentiated market segments can help producers develop more cost-effective management strategies.

Suggested Citation

  • Bekkerman, Anton & Brester, Gary W. & McDonald, Tyrel J., 2013. "A Semiparametric Approach to Analyzing Differentiated Agricultural Products," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 45(1), pages 79-94, February.
    • Handle: RePEc:cup:jagaec:v:45:y:2013:i:01:p:79-94_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S1074070800004594/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Gary W. Brester, 2002. "Meeting Consumer Demands with Genetics and Market Coordination: The Case of the Leachman Cattle Company," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 24(1), pages 251-265.
    2. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275, January.
    3. Jones, Rodney & Turner, Tyler & Dhuyvetter, Kevin C. & Marsh, Thomas L., 2008. "Estimating the Economic Value of Specific Characteristics Associated with Angus Bulls Sold at Auction," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(1), pages 315-333, April.
    4. Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
    5. repec:cup:jagaec:v:40:y:2008:i:1:p:315-333_17 is not listed on IDEAS
    6. Gary W. Brester, 2002. "Meeting Consumer Demands with Genetics and Market Coordination: The Case of the Leachman Cattle Company," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 24(1), pages 251-265.
    7. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    8. Omar Arias & Walter Sosa-Escudero & Kevin F. Hallock, 2001. "Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data," Empirical Economics, Springer, vol. 26(1), pages 7-40.
    9. Parcell, Joseph L. & Schroeder, T.C., 2007. "Hedonic Retail Beef and Pork Product Prices," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 39(1), pages 29-46, April.
    10. Dhuyvetter, Kevin C. & Schroeder, Ted C. & Simms, Danny D. & Bolze, Ronald P., Jr. & Geske, Jeremy, 1996. "Determinants Of Purebred Beef Bull Price Differentials," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 21(2), pages 1-15, December.
    11. Allan M. Walburger, 2002. "Estimating the Implicit Prices of Beef Cattle Attributes: A Case from Alberta," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 50(2), pages 135-149, July.
    12. Michelle L. Barnes & Anthony W. Hughes, 2002. "A quantile regression analysis of the cross section of stock market returns," Working Papers 02-2, Federal Reserve Bank of Boston.
    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. Boyer, Christopher & Griffith, Andrew P. & Thompson, Jada M. & Rhinehart, Justin & Burdine, Kenneth H., 2022. "Bred Heifer Price Determinants in the Southeast," Journal of Applied Farm Economics, Purdue University, vol. 3(2), May.
    2. B. Wade Brorsen & Notie H. Lansford, 2013. "Sales Tax Collections in Nonmetropolitan Communities," Public Finance Review, , vol. 41(4), pages 489-503, July.
    3. Tang, M. & Thompson, N.M. & Boyer, C.N. & Widmar, N.J.O. & Lusk, J.L., 2023. "Implicit Market Segmentation and Valuation of Angus Bull Attributes," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 48(2), May.

    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. Khalifa, Maha & Othman, Hakim Ben & Hussainey, Khaled, 2018. "The effect of ex ante and ex post conservatism on the cost of equity capital: A quantile regression approach for MENA countries," Research in International Business and Finance, Elsevier, vol. 44(C), pages 239-255.
    2. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    3. Chowdhury, Biplob & Jeyasreedharan, Nagaratnam & Dungey, Mardi, 2018. "Quantile relationships between standard, diffusion and jump betas across Japanese banks," Journal of Asian Economics, Elsevier, vol. 59(C), pages 29-47.
    4. Wiji Arulampalam & Alison Booth & Mark Bryan, 2010. "Are there asymmetries in the effects of training on the conditional male wage distribution?," Journal of Population Economics, Springer;European Society for Population Economics, vol. 23(1), pages 251-272, January.
    5. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(5), pages 123-128, August.
    6. Joachim Wagner, 2014. "Exports, foreign direct investments and productivity: are services firms different?," The Service Industries Journal, Taylor & Francis Journals, vol. 34(1), pages 24-37, January.
    7. Brunello, Giorgio & Fabbri, Daniele & Fort, Margherita, 2009. "Years of Schooling, Human Capital and the Body Mass Index of European Females," IZA Discussion Papers 4667, Institute of Labor Economics (IZA).
    8. David E Allen & Abhay K Singh & Robert J Powell & Michael McAleer & James Taylor & Lyn Thomas, 2012. "The Volatility-Return Relationship:Insights from Linear and Non-Linear Quantile Regressions," KIER Working Papers 831, Kyoto University, Institute of Economic Research.
    9. RAMDANI, Dendi & VAN WITTELOOSTUIJN, Arjen, 2009. "Board independence, CEO duality and firm performance: A quantile regression analysis for Indonesia, Malaysia, South Korea and Thailand," ACED Working Papers 2009003, University of Antwerp, Faculty of Business and Economics.
    10. Billger, Sherrilyn M., 2007. "Principals as Agents? Investigating Accountability in the Compensation and Performance of School Principals," IZA Discussion Papers 2662, Institute of Labor Economics (IZA).
    11. Matthias Duschl & Antje Schimke & Thomas Brenner & Dennis Luxen, 2011. "Firm Growth and the Spatial Impact of Geolocated External Factors – Empirical Evidence for German Manufacturing Firms," Working Papers on Innovation and Space 2011-03, Philipps University Marburg, Department of Geography.
    12. Jens J. Krüger, 2008. "Productivity dynamics and structural change in the US manufacturing sector," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 17(4), pages 875-902, August.
    13. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Fernández-Val, Iván, 2019. "Conditional quantile processes based on series or many regressors," Journal of Econometrics, Elsevier, vol. 213(1), pages 4-29.
    14. Le Wang, 2013. "How Does Education Affect the Earnings Distribution in Urban China?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(3), pages 435-454, June.
    15. repec:cte:werepe:we1237 is not listed on IDEAS
    16. Sheng-Tung Chen & Hsiao-I. Kuo & Chi-Chung Chen, 2012. "Estimating the extreme behaviors of students performance using quantile regression -- evidences from Taiwan," Education Economics, Taylor & Francis Journals, vol. 20(1), pages 93-113, December.
    17. Juliana Guimarães & Breno Sampaio, 2008. "Mind the Gap: Evidences from Gender Differences in Scores in Brazil," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807211527140, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    18. Tae-Hwan Kim, & Christophe Muller, 2012. "Bias Transmission and Variance Reduction in Two-Stage Quantile Regression," AMSE Working Papers 1221, Aix-Marseille School of Economics, France.
    19. Meligkotsidou, Loukia & Vrontos, Ioannis D. & Vrontos, Spyridon D., 2009. "Quantile regression analysis of hedge fund strategies," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 264-279, March.
    20. Broberg, Thomas & Kazukauskas, Andrius, 2014. "Inefficiencies in residential use of energy - A critical overview of literature and energy efficiency policies in EU and Sweden," CERE Working Papers 2014:7, CERE - the Center for Environmental and Resource Economics.
    21. Crambes, Christophe & Gannoun, Ali & Henchiri, Yousri, 2013. "Support vector machine quantile regression approach for functional data: Simulation and application studies," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 50-68.

    More about this item

    JEL classification:

    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D49 - Microeconomics - - Market Structure, Pricing, and Design - - - Other

    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:cup:jagaec:v:45:y:2013:i:01:p:79-94_00. 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/aae .

    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.