Improved Inference for Moving Average Disturbances in Nonlinear Regression Models

Abstract EN

This paper proposes an improved likelihood-based method to test for first-order moving average inthe disturbances of nonlinear regression models.

The proposed method has a third-order distributionalaccuracy which makes it particularly attractive for inference in small sample sizes models.

Compared tothe commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samplesand asymptotic properties of the maximum likelihood estimation, the proposed method has remarkableaccuracy.

Monte Carlo simulations are provided to show how the proposed method outperforms the existingones.

Two empirical examples including a power regression model of aggregate consumption and aGompertz growth model of mobile cellular usage in the US are presented to illustrate the implementationand usefulness of the proposed method in practice.