An unrestricted MIDAS ordered logit model with applications to credit ratings

Abstract

AbstractThe ordered logit (OLogit) model is a regression model for an ordinal dependent variable. For a conventional time series OLogit model, both the dependent variable and the independent variables are required to be observed at the same frequency. However, this requirement is violated under the circumstance of mixed frequency data. To this end, we introduce the unrestricted MIDAS (U‐MIDAS) method into the OLogit model and develop a novel U‐MIDAS‐OLogit model, in which high‐frequency covariates are used to predict a low‐frequency outcome with ordinal categories. The U‐MIDAS‐OLogit model enlarges the application of OLogit and enables to produce timely forecast. To verify its effectiveness, we conduct extensive Monte Carlo simulations. The numerical results show that the U‐MIDAS‐OLogit model is superior to several typical OLogit models in terms of prediction performance. We then apply the U‐MIDAS‐OLogit model to predict credit ratings of listed companies in China and the US, respectively. The empirical results also confirm its promising in practical applications.