Goodness-of-fit tests for stochastic frontier models based on the characteristic function

TL;DR

This paper introduces a new goodness-of-fit test for stochastic frontier models based on the characteristic function of the composed error, offering consistency and computational efficiency.

Contribution

The paper develops a novel characteristic function-based test for the distribution of errors in stochastic frontier models, improving on classical methods.

Findings

The test is consistent across various scenarios.

Resampling versions outperform classical goodness-of-fit tests.

The method is computationally convenient.

Abstract

We consider goodness-of-fit tests for the distribution of the composed error in Stochastic Frontier Models. The proposed test statistic utilizes the characteristic function of the composed error term, and is formulated as a weighted integral of properly standardized data. The new test statistic is shown to be consistent and computationally convenient. Simulation results are presented whereby resampling versions of the new tests are compared to classical goodness-of-fit methods.