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Unconditional Quantile Estimation: An Application to the Gravity Framework

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  • Cairns, Alexander P.
  • Ker, Alan P.

Abstract

Since its inception, the gravity model has been the cornerstone of empirical trade analysis. It has been used to estimate the marginal e↵ects of various determinants of trade as well as to test hypothesized relationships, many of which have direct and significant policy implications. Conventional estimation methods derive the marginal e↵ects of the covariates at the mean; or rather, only estimate the average e↵ect of an explanatory variable on trade flows. This study applies unconditional quantile estimation (UQE), as developed by Firpo, Fortin, and Lemieux (2009), to a variant of the gravity model developed by Hallak (2006) in order to obtain income and distance elasticity estimates for imports of six di↵erentiated agrifood products. Findings suggest that both income and distance elasticity estimates do vary across quantiles, suggesting that conventional estimation methods may limit the depth of analysis. The estimated income elasticities vary across products as well, both in terms of the underlying trends across quantiles and in magnitude, further demonstrating the capacity of UQE to identify how the underlying trend across quantiles is conditional on the product under examination.

Suggested Citation

  • Cairns, Alexander P. & Ker, Alan P., 2013. "Unconditional Quantile Estimation: An Application to the Gravity Framework," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150399, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea13:150399
    DOI: 10.22004/ag.econ.150399
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    References listed on IDEAS

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    3. Sampath Jayasinghe & John C. Beghin & Giancarlo Moschini, 2017. "Determinants Of World Demand For U.S. Corn Seeds: The Role Of Trade Costs," World Scientific Book Chapters, in: John Christopher Beghin (ed.), Nontariff Measures and International Trade, chapter 17, pages 309-320, World Scientific Publishing Co. Pte. Ltd..
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    Cited by:

    1. Badi Baltagi & Peter Egger, 2016. "Estimation of structural gravity quantile regression models," Empirical Economics, Springer, vol. 50(1), pages 5-15, February.

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