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Flexible Estimation of Demand Systems: A Copula Approach*

* This paper is a replication of an original study

Author

Listed:
  • Mateo Velásquez‐Giraldo
  • Gustavo Canavire‐Bacarreza
  • Kim P. Huynh
  • David T. Jacho‐Chavez

Abstract

In this paper we study the own‐price elasticity for gasoline in demand systems involving three expenditure categories in the transportation sector in Canada: gasoline, local transportation, and intercity transportation for Canadian households from 1997 to 2009. In particular, we conduct a replication of Chang and Serletis, (The demand for gasoline: Evidence from household survey data, Journal of Applied Econometrics, 2014, 29, 291–343) hereafter CS, who—using TSP version 5.1—estimated Deaton and Muellbauer, 's Almost Ideal Demand System (AIDS) (American Economic Review, 1980, 70, 312–326), Banks et al., 's Quadratic AIDS (Review of Economics and Statistics, 1997, 79, 527–539), and Barnett, 's Minflex Laurent (ML) (Journal of Business and Economic Statistics, 1983, 1, 7–23) models to demand systems consisting of these three goods, analyzing and enforcing theoretical economic regularity—that is, the compliance of estimates with positivity, monotonicity, and curvature. Using the R statistical language instead, we found that our estimates are similar to those of CS using data for single‐member households and married couples without children, but differ for households with one child. (All replicated estimation tables in CS, as well as our full implementation, are available as supplementary material in the online version of this paper.) However, using a more flexible copula model, a total of 168 possible specifications for each type of household and their resulting gasoline own‐price elasticities are also estimated. We find that allowing for skewness in the marginal distributions of local transportation budget shares greatly improves the Bayesian information criterion (BIC) of our models.

Suggested Citation

  • Mateo Velásquez‐Giraldo & Gustavo Canavire‐Bacarreza & Kim P. Huynh & David T. Jacho‐Chavez, 2018. "Flexible Estimation of Demand Systems: A Copula Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 1109-1116, November.
    • Handle: RePEc:wly:japmet:v:33:y:2018:i:7:p:1109-1116
    DOI: 10.1002/jae.2651
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    References listed on IDEAS

    as
    1. Barten, A. P., 1969. "Maximum likelihood estimation of a complete system of demand equations," European Economic Review, Elsevier, vol. 1(1), pages 7-73.
    2. Trivedi, Pravin K. & Zimmer, David M., 2007. "Copula Modeling: An Introduction for Practitioners," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(1), pages 1-111, April.
    3. Ryan, David L & Wales, Terence J, 1998. "A Simple Method for Imposing Local Curvature in Some Flexible Consumer-Demand Systems," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 331-338, July.
    4. McCullough, B D, 1999. "Econometric Software Reliability: EViews, LIMDEP, SHAZAM and TSP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(2), pages 191-202, March-Apr.
    5. 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.
    6. Ho, Anson T.Y. & Huynh, Kim P. & Jacho-Chávez, David T., 2019. "Using nonparametric copulas to measure crude oil price co-movements," Energy Economics, Elsevier, vol. 82(C), pages 211-223.
    7. William A. Barnett, 2000. "New Indices of Money Supply and the Flexible Laurent Demand System," Contributions to Economic Analysis, in: The Theory of Monetary Aggregation, pages 325-359, Emerald Group Publishing Limited.
    8. BARTEN, Anton P., 1969. "Maximum likelihood estimation of a complete system of demand equations," LIDAM Reprints CORE 34, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. 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.
    10. David M. Lilien, 2000. "Econometric software reliability and nonlinear estimation in EViews: comment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 107-110.
    11. Anson T. Y. Ho & Kim P. Huynh & David T. Jacho‐Chávez, 2016. "Flexible Estimation of Copulas: An Application to the US Housing Crisis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 603-610, April.
    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.
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    Cited by:

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    2. Montoya-Blandón, Santiago & Jacho-Chávez, David T., 2020. "Semiparametric quasi maximum likelihood estimation of the fractional response model," Economics Letters, Elsevier, vol. 186(C).
    3. Garratt, Anthony & Henckel, Timo & Vahey, Shaun P., 2023. "Empirically-transformed linear opinion pools," International Journal of Forecasting, Elsevier, vol. 39(2), pages 736-753.

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    Replication

    This item is a replication of:
  • 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.

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