Version 1
: Received: 2 August 2024 / Approved: 3 August 2024 / Online: 5 August 2024 (12:05:28 CEST)
How to cite:
Wagner, J. Estimation of Empirical Models for Margins of Exports with Unknown Non-linear Functional Forms: A Kernel-Regularized Least Squares (KRLS) Approach. Preprints2024, 2024080246. https://doi.org/10.20944/preprints202408.0246.v1
Wagner, J. Estimation of Empirical Models for Margins of Exports with Unknown Non-linear Functional Forms: A Kernel-Regularized Least Squares (KRLS) Approach. Preprints 2024, 2024080246. https://doi.org/10.20944/preprints202408.0246.v1
Wagner, J. Estimation of Empirical Models for Margins of Exports with Unknown Non-linear Functional Forms: A Kernel-Regularized Least Squares (KRLS) Approach. Preprints2024, 2024080246. https://doi.org/10.20944/preprints202408.0246.v1
APA Style
Wagner, J. (2024). Estimation of Empirical Models for Margins of Exports with Unknown Non-linear Functional Forms: A Kernel-Regularized Least Squares (KRLS) Approach. Preprints. https://doi.org/10.20944/preprints202408.0246.v1
Chicago/Turabian Style
Wagner, J. 2024 "Estimation of Empirical Models for Margins of Exports with Unknown Non-linear Functional Forms: A Kernel-Regularized Least Squares (KRLS) Approach" Preprints. https://doi.org/10.20944/preprints202408.0246.v1
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.
Keywords
margins of exports; empirical models; non-linear relationships; kernel-regularized least squares; krls
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
