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.

The central prediction of the current paper is that manufacturer price advertising may be a less effective tool for influencing demand than retailer price advertising. We manipulate the source of a price advertisement in an experiment run on a sample of pickup truck owners. Manufacturer price advertising leads to lower indicators of potential demand than dealer price advertising, even among consumers who are experienced with the brand. An econometric analysis of pickup truck sales, price, and advertising data shows that this effect is large enough to detect in market data. Manufacturer and dealer price advertising both increase the demand intercept and the responsiveness of demand to price, but the effects of dealer price advertising are larger. Although dealer price advertising is more effective than manufacturer price advertising, manufacturer price advertising may still be useful to reduce channel conflict. This paper was accepted by Pradeep Chintagunta, marketing.