Dynamic Quality Ladder Model Predictions in Nonrandom Holdout Samples
用非随机保留样本方法评估动态质量阶梯模型在政策变化后的预测能力，发现该模型在受政策冲击较大的汽车类别中表现优于基准VAR模型，有助于验证结构模型在政策变化后的预测有效性。
In light of recent calls for further validation of structural models, this paper evaluates the popular dynamic quality ladder (DQL) model using a nonrandom holdout approach. The model is used to predict data following a regime shift—that is, a change in the environment that produced the estimation data. The prediction performance is evaluated relative to a benchmark vector autoregression (VAR) model across three automotive categories and multiple prediction horizons. Whereas the VAR model performs better in all scenarios in the compact car category, the DQL model tends to perform better on multiple-year horizons in both the midsize car and full-size pickup categories. A supplementary data analysis suggests that DQL model performance in the nonrandom holdout prediction task is better in categories that are more affected by the regime shift, helping to validate the usefulness of the dynamic structural model for making predictions after policy changes. This paper was accepted by Matthew Shum, marketing.