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

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: KCG Working Paper ; No. 32

Classification and Topics

• Classification: Wirtschaft ; Empirical Studies of Trade
• Subject: Margins of exports ; empirical models ; non-linear relationships ; kernel-regularized least squares ; krls

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Wagner, Joachim
• Event: Veröffentlichung
• (who): Kiel Centre for Globalization (KCG)
• (where): Kiel
• (when): 2024

Further information

• Handle: http://hdl.handle.net/10419/282005
• Last update: 10.03.2025, 11:44 AM CET