{"content":[{"text":"PRELIMINARY – do not cite without permission","bbox":{"left":0.490196,"top":0.047446,"width":0.39213800000000004,"height":0.015136999999999998}},{"text":"TESTING A MODEL OF CONSUMER VEHICLE PURCHASES","bbox":{"left":0.236862,"top":0.191082,"width":0.5261750000000001,"height":0.015137000000000012}},{"text":"Gloria Helfand* \n David Greene \n Changzheng Liu \n Ari Kahan \n Michael Shelby \n Marie Donahue \n Jacqueline Doremus","bbox":{"left":0.420195,"top":0.256234,"width":0.159377,"height":0.11968200000000001}},{"text":"December 2013","bbox":{"left":0.437646,"top":0.413052,"width":0.12449200000000005,"height":0.015137000000000012}},{"text":"*Corresponding author: \n Office of Transportation and Air Quality \n U.S. Environmental Protection Agency \n 2000 Traverwood Drive \n Ann Arbor, MI 48105","bbox":{"left":0.117646,"top":0.4479,"width":0.319785,"height":0.08483399999999991}},{"text":"ABSTRACT","bbox":{"left":0.448823,"top":0.552597,"width":0.10235,"height":0.015136999999999956}},{"text":"Consumer vehicle choice models have been estimated and used for a wide variety of policy \n simulations. Infrequently, though, have predicted responses from these models been tested \n against actual outcomes. This paper tests a model developed for the U.S. Environmental \n Protection Agency that is intended to estimate the impacts of changes in vehicle prices and fuel \n economy. It is a nested logit with a representative consumer and 5 layers: the buy/no buy \n decision, passenger versus cargo versus ultra-prestige vehicle, vehicle classes, subdivision of \n those classes into standard and prestige vehicles, and then individual vehicles. It is calibrated to \n vehicle purchases in model year (MY) 2008. Vehicle changes between MY and 2010 are then \n used to make predictions, and those predictions are compared to actual outcomes in MY 2010. \n The research suggests that the model may predict better when its inputs are aggregated than \n when they are as disaggregated as possible, though further work is needed to assess the model’s \n predictive abilities.","bbox":{"left":0.117646,"top":0.6179,"width":0.751534,"height":0.23543999999999998}},{"text":"1","bbox":{"left":0.872548,"top":0.921688,"width":0.009785000000000044,"height":0.015136999999999956}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}