Estimation for generalized linear cointegration regression models through composite quantile regression approach

This paper introduces a meaningful approach employing composite quantile regression (CQR) to estimate generalized linear cointegration regression models. We elucidate the fundamental structure of the proposed model by presenting its underlying expressions and derive the asymptotic distribution of the estimates of model parameters. Through extensive simulations, our findings demonstrate the superior robustness and precision of the CQR method compared to ordinary least squares (OLS) and quantile regression (QR) approaches. The application of the model to economic and financial variables highlights its significant academic and practical value.