Firm Characteristics of Two-Way Traders: Evidence from Probit Vs. Kernel-Regularized Least Squares Regressions

Joachim Wagner
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Joachim Wagner: Kiel Centre for Globalization, Leuphana University Lueneburg, Lüneburg, Germany

Economic Analysis Letters, 2025, vol. 4, issue 3, 20-27

Abstract: Firm characteristics in empirical models for margins of international trade usually enter these models in linear form. If non-linearities do matter and are ignored this leads to biased results. Researchers, however, can never be sure that all possible non-linear relationships are taken care of. A solution is provided by Kernel Regularized Least Squares (KRLS) that uses a machine learning approach to learn the functional form from the data. While in earlier applications the big picture revealed by standard empirical models and KRLS was identical this note presents a case where results from a standard approach and KRLS do differ considerably.

Keywords: Two-way trading firms; firm level data; BEEPS data; kernel regularized least squares (KRLS) (search for similar items in EconPapers)
Date: 2025
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