In ordered multi-classification analysis,mixed data observation is be-coming more and more common.In order to solve the problem of ordered multi-classification under a large frequency ratio,we combine the mixed data sampling(MIDAS)with ordered Logit(OLogit)model to construct an MIDAS-OLogit model.The MIDAS-OLogit model uses high-frequency explanatory variables to predict the ordered multi-classification results of the low-frequency variable,which expands the application range of OLogit model and can adapt to the ordered multi-classification analysis of mixed data under large frequency ratio.In order to verify its effective-ness,Monte Carlo numerical simulation is carried out,and the results show that the prediction performance of MIDAS-OLogit model is better than that of competitive models.In addition,we use the MIDAS-OLogit model to credit ratings the corporate bonds issued by Chinese listed companies from 2008 to 2021,and the results further verify its superior performance of classified forecasting and real-time forecasting.
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