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Estimation of empirical models for margins of exports with unknown nonlinear functional forms: A Kernel-Regularized Least Squares (KRLS) approach

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  • Wagner, Joachim

Abstract

Empirical models for intensive or extensive margins of trade that relate measures of exports to firm characteristics are usually estimated by variants of (generalized) linear models. Usually, the firm characteristics that explain these export margins enter the empirical model in linear form, sometimes augmented by quadratic terms or higher order polynomials, or interaction terms, to take care or test for non-linear relationships. If these non-linear relationships do matter and if they are ignored in the specification of the empirical model this leads to biased results. Researchers, however, can never be sure that all possible non-linear relationships are taken care of in their chosen specifications. This note uses for the first time the Kernel-Regularized Least Squares (KRLS) estimator to deal with this issue in empirical models for margins of exports. KRLS is a machine learning method that learns the functional form from the data. Empirical examples show that it is easy to apply and works well. Therefore, it is considered as a useful addition to the box of tools of empirical trade economists.

Suggested Citation

  • Wagner, Joachim, 2024. "Estimation of empirical models for margins of exports with unknown nonlinear functional forms: A Kernel-Regularized Least Squares (KRLS) approach," KCG Working Papers 32, Kiel Centre for Globalization (KCG).
  • Handle: RePEc:zbw:kcgwps:282005
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    References listed on IDEAS

    as
    1. Wagner, Joachim, 2023. "Big data analytics and exports: Evidence for manufacturing firms from 27 EU countries," KCG Working Papers 28, Kiel Centre for Globalization (KCG).
    2. Hainmueller, Jens & Hazlett, Chad, 2014. "Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach," Political Analysis, Cambridge University Press, vol. 22(2), pages 143-168, April.
    3. Jean-Joseph Minviel & Faten Ben Bouheni, 2022. "The impact of research and development (R&D) on economic growth: new evidence from kernel-based regularized least squares," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 23(5), pages 583-604, July.
    4. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    5. Ferwerda, Jeremy & Hainmueller, Jens & Hazlett, Chad J., 2017. "Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls)," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i03).
    6. Joachim Wagner, 2023. "Big Data Analytics and Exports - Evidence for Manufacturing Firms from 27 EU Countries," Working Paper Series in Economics 421, University of Lüneburg, Institute of Economics.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Margins of exports; empirical models; non-linear relationships; kernel-regularized least squares; krls;
    All these keywords.

    JEL classification:

    • F14 - International Economics - - Trade - - - Empirical Studies of Trade

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