What type of study is this?

August 17, 2025

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

Key Points

Results indicate that using KRLS may reveal significant non-linear relationships that standard models overlook, affecting trade margins.
Analysis highlights a key difference where KRLS and probit methods yield varying interpretations of firm characteristics in trade contexts.
This assessment employs kernel-regularized least squares methodology to address potential biases in linear empirical models.
Findings suggest that traditional approaches could misrepresent data, emphasizing the need for advanced modeling techniques.

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.

Mark Helpful

Bookmark

Relay