IDEAS home Printed from https://ideas.repec.org/p/msh/ebswps/2014-28.html
   My bibliography  Save this paper

Bayesian Estimation for Partially Linear Models with an Application to Household Gasoline Consumption

Author

Listed:
  • Haotian Chen
  • Xibin Zhang

Abstract

A partially linear model is often estimated in a two-stage procedure, which involves estimating the nonlinear component conditional on initially estimated linear coefficients. We propose a sampling procedure that aims to simultaneously estimate the linear coefficients and bandwidths involved in the Nadaraya-Watson estimator of the nonlinear component. The performance of this sampling procedure is demonstrated through Monte Carlo simulation studies. The proposed sampling algorithm is applied to partially linear models of gasoline consumption based on the US household survey data. In contrary to implausible price effect reported in the literature, we find negative price effect on household gasoline consumption.

Suggested Citation

  • Haotian Chen & Xibin Zhang, 2014. "Bayesian Estimation for Partially Linear Models with an Application to Household Gasoline Consumption," Monash Econometrics and Business Statistics Working Papers 28/14, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2014-28
    as

    Download full text from publisher

    File URL: http://business.monash.edu/__data/assets/pdf_file/0004/327145/wp28-14.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wadud, Zia & Noland, Robert B. & Graham, Daniel J., 2010. "A semiparametric model of household gasoline demand," Energy Economics, Elsevier, vol. 32(1), pages 93-101, January.
    2. Dahl, Carol A, 1979. "Consumer Adjustment to a Gasoline Tax," The Review of Economics and Statistics, MIT Press, vol. 61(3), pages 427-432, August.
    3. Richard Blundell & Joel L. Horowitz & Matthias Parey, 2012. "Measuring the price responsiveness of gasoline demand: Economic shape restrictions and nonparametric demand estimation," Quantitative Economics, Econometric Society, vol. 3(1), pages 29-51, March.
    4. Kenneth A. Small & Kurt Van Dender, 2007. "Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound Effect," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 25-52.
    5. Linton, Oliver, 1995. "Second Order Approximation in the Partially Linear Regression Model," Econometrica, Econometric Society, vol. 63(5), pages 1079-1112, September.
    6. Jonathan E. Hughes & Christopher R. Knittel & Daniel Sperling, 2008. "Evidence of a Shift in the Short-Run Price Elasticity of Gasoline Demand," The Energy Journal, International Association for Energy Economics, vol. 29(1), pages 113-134.
    7. Dongfeng Chang & Apostolos Serletis, 2014. "The Demand For Gasoline: Evidence From Household Survey Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(2), pages 291-313, March.
    8. Angus Deaton & Christina Paxson, 1998. "Economies of Scale, Household Size, and the Demand for Food," Journal of Political Economy, University of Chicago Press, vol. 106(5), pages 897-930, October.
    9. Golubev, Georgi & Härdle, Wolfgang, 2000. "On adaptive estimation in partial linear models," SFB 373 Discussion Papers 2000,21, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    10. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    11. Xibin Zhang & Maxwell L. King, 2013. "Gaussian kernel GARCH models," Monash Econometrics and Business Statistics Working Papers 19/13, Monash University, Department of Econometrics and Business Statistics.
    12. Hausman, Jerry A & Newey, Whitney K, 1995. "Nonparametric Estimation of Exact Consumers Surplus and Deadweight Loss," Econometrica, Econometric Society, vol. 63(6), pages 1445-1476, November.
    13. Su, Qing, 2011. "The effect of population density, road network density, and congestion on household gasoline consumption in U.S. urban areas," Energy Economics, Elsevier, vol. 33(3), pages 445-452, May.
    14. Adonis Yatchew & Joungyeo Angela No, 2001. "Household Gasoline Demand in Canada," Econometrica, Econometric Society, vol. 69(6), pages 1697-1709, November.
    15. Liang, Hua, 2006. "Estimation in partially linear models and numerical comparisons," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 675-687, February.
    16. Li, Qi, 2000. "Efficient Estimation of Additive Partially Linear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 1073-1092, November.
    17. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    18. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    19. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
    20. Rice, John, 1986. "Convergence rates for partially splined models," Statistics & Probability Letters, Elsevier, vol. 4(4), pages 203-208, June.
    21. Sebastiano Manzan & Dawit Zerom, 2010. "A Semiparametric Analysis of Gasoline Demand in the United States Reexamining The Impact of Price," Econometric Reviews, Taylor & Francis Journals, vol. 29(4), pages 439-468.
    22. Dahl, Carol & Sterner, Thomas, 1991. "Analysing gasoline demand elasticities: a survey," Energy Economics, Elsevier, vol. 13(3), pages 203-210, July.
    23. Kayser, Hilke A., 2000. "Gasoline demand and car choice: estimating gasoline demand using household information," Energy Economics, Elsevier, vol. 22(3), pages 331-348, June.
    24. Stoker, Thomas M., 1996. "Smoothing bias in the measurement of marginal effects," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 49-84.
    25. Richard Schmalensee & Thomas M. Stoker, 1999. "Household Gasoline Demand in the United States," Econometrica, Econometric Society, vol. 67(3), pages 645-662, May.
    Full references (including those not matched with items on IDEAS)

    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. Chen, Haotian & Smyth, Russell & Zhang, Xibin, 2017. "A Bayesian sampling approach to measuring the price responsiveness of gasoline demand using a constrained partially linear model," Energy Economics, Elsevier, vol. 67(C), pages 346-354.
    2. Liu, Weiwei, 2014. "Modeling gasoline demand in the United States: A flexible semiparametric approach," Energy Economics, Elsevier, vol. 45(C), pages 244-253.
    3. Gillingham, Kenneth & Jenn, Alan & Azevedo, Inês M.L., 2015. "Heterogeneity in the response to gasoline prices: Evidence from Pennsylvania and implications for the rebound effect," Energy Economics, Elsevier, vol. 52(S1), pages 41-52.
    4. Liu, Weiwei, 2015. "Gasoline taxes or efficiency standards? A heterogeneous household demand analysis," Energy Policy, Elsevier, vol. 80(C), pages 54-64.
    5. Kim, Kun Ho & Chao, Shih-Kang & Härdle, Wolfgang Karl, 2020. "Simultaneous Inference of the Partially Linear Model with a Multivariate Unknown Function," IRTG 1792 Discussion Papers 2020-008, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. Gillingham, Kenneth & Munk-Nielsen, Anders, 2019. "A tale of two tails: Commuting and the fuel price response in driving," Journal of Urban Economics, Elsevier, vol. 109(C), pages 27-40.
    7. Tilov, Ivan & Weber, Sylvain, 2023. "Heterogeneity in price elasticity of vehicle kilometers traveled: Evidence from micro-level panel data," Energy Economics, Elsevier, vol. 127(PA).
    8. Han Shang, 2014. "Bayesian bandwidth estimation for a semi-functional partial linear regression model with unknown error density," Computational Statistics, Springer, vol. 29(3), pages 829-848, June.
    9. Manzan, sebastiano & Zerom, Dawit, 2008. "A Semiparametric Analysis of Gasoline Demand in the US: Reexamining The Impact of Price," MPRA Paper 14386, University Library of Munich, Germany.
    10. Su, Qing, 2011. "The effect of population density, road network density, and congestion on household gasoline consumption in U.S. urban areas," Energy Economics, Elsevier, vol. 33(3), pages 445-452, May.
    11. Wang, Qihua & Härdle, Wolfgang & Linton, Oliver, 2002. "Semiparametric regression analysis under imputation for missing response data," SFB 373 Discussion Papers 2002,6, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    12. Sebastiano Manzan & Dawit Zerom, 2010. "A Semiparametric Analysis of Gasoline Demand in the United States Reexamining The Impact of Price," Econometric Reviews, Taylor & Francis Journals, vol. 29(4), pages 439-468.
    13. Liang, Hua, 2006. "Estimation in partially linear models and numerical comparisons," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 675-687, February.
    14. Kilian, Lutz & Zhou, Xiaoqing, 2024. "Heterogeneity in the pass-through from oil to gasoline prices: A new instrument for estimating the price elasticity of gasoline demand," Journal of Public Economics, Elsevier, vol. 232(C).
    15. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90059, University Library of Munich, Germany.
    16. Silvia Tiezzi & Stefano F. Verde, 2017. "The signaling effect of gasoline taxes and its distributional implications," RSCAS Working Papers 2017/06, European University Institute.
    17. Richard Blundell & Joel L. Horowitz & Matthias Parey, 2009. "Measuring the price responsiveness of gasoline demand," CeMMAP working papers CWP11/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Mohammad Vesal & Amir Hossein Tavakoli & Mohammad H. Rahmati, 2022. "What do one hundred million transactions tell us about demand elasticity of gasoline?," Empirical Economics, Springer, vol. 62(6), pages 2693-2711, June.
    19. Mohcine Bakhat & José M. Labeaga & Xavier Labandeira & Xiral Lñpez, 2013. "Economic Crisis and Elasticities of Car Fuels: Evidence for Spain," Working Papers fa15-2013, Economics for Energy.
    20. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.

    More about this item

    Keywords

    backfitting least squares; bandwidth; household income; price elasticity; profile least squares; random-walk Metropolis;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

    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:msh:ebswps:2014-28. 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: Professor Xibin Zhang (email available below). General contact details of provider: https://edirc.repec.org/data/dxmonau.html .

    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.