Firm characteristics of two-way traders: Evidence from Probit vs. Kernel-Regularized Least Squares regressions

Joachim Wagner ()
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Joachim Wagner: Leuphana Universität Lüneburg, Institut für Volkswirtschaftslehre

No 433, Working Paper Series in Economics from University of Lüneburg, Institute of Economics

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)
JEL-codes: F14 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2025-05
New Economics Papers: this item is included in nep-big and nep-iaf
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