Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/324172 
Authors: 
Year of Publication: 
2025
Series/Report no.: 
Working Paper Series in Economics No. 433
Publisher: 
Leuphana Universität Lüneburg, Institut für Volkswirtschaftslehre, Lüneburg
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.
Subjects: 
Two-way trading firms
firm level data
BEEPS data
kernel regularized least squares (KRLS)
JEL: 
F14
Document Type: 
Working Paper

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