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The three chapters in this dissertation study and apply econometric models to answer questions in transportation economics. Chapter 1 and 2 analyze the Berry, Levinsohn and Pakes (BLP) discrete choice model for combined micro- and macro-level data. Chapter 1 considers the concerns of choice set aggregation and estimating consistent standard errors within the BLP Model. These concerns are studied within the context of a vehicle choice application with interest in estimating household valuation of fuel efficiency. Chapter 2 studies the numerical properties of the maximum likelihood approach to estimating this BLP model. Chapter 3 applies a Poisson-Log Normal panel data model to study the effect of red light cameras on collision counts in Los Angeles. The camera program suffered from weaknesses in enforcement that dampened the effectiveness of the program over time. The model considered here controls for this dampening effect.Chapter 1 finds that choice set aggregation affects the point estimates obtained from the BLP model and causes standard errors to be too small. The use of inconsistent sequential standard errors also underestimates the magnitude of standard errors. These findings may partly explain the disparity across existing estimates from choice models on the value households place on vehicle fuel efficiency.Chapter 2 finds that the maximum likelihood estimation approach is able to find the global minimum regardless of choice of starting values, optimization routine used and tightness of convergence criteria. These findings highlight the benefits of estimating the BLP model on combined micro- and macro-level datasets using the maximum likelihood approach compared to using the nested fixed point approach and only macro level data where numerical stability is difficult to obtain.Chapter 3 finds that controlling for the dampening effect from poor enforcement, the Los Angeles Automated Red Light Camera program decreased red light running related collisions but increased right-angle and injury collisions, as well as collisions overall. |
