@article{Estimation:1857,
      recid = {1857},
      author = {Chung, Jae Hyen},
      title = {Estimation of Sequential Search Models},
      publisher = {The University of Chicago},
      school = {Ph.D.},
      address = {2019-06},
      pages = {93},
      abstract = {We propose a new likelihood-based estimation method for  the sequential search model. By allowing search costs to be  heterogeneous across consumers and products, we can  directly compute the joint probability of the search  sequence and the purchase decision when consumers are  searching for the idiosyncratic preference shocks in their  utility functions. Under this procedure, one recursively  makes random draws for each dimension that requires  numerical integration to simulate the probabilities  associated with the purchase decision and the search  sequence under the sequential search algorithm. We then  present details from an extensive simulation study that  compares the proposed approach with existing estimation  methods recently used for sequential search model  estimation, viz., the kernel-smoothed frequency simulator  (KSFS) and the crude frequency simulator (CFS). In the  empirical application, we apply the proposed method to the  Expedia dataset from Kaggle which has previously been  analyzed using the KSFS estimator and the assumption of  homogeneous search costs. We demonstrate that the proposed  method has a better predictive performance associated with  differences in the estimated effects of various drivers of  clicks and purchases, and highlight the importance of the  heterogeneous search costs assumption even when KSFS is  used to estimate the sequential search model. Lastly, from  a managerial perspective, we show that sorting products by  their expected utilities can enhance consumer welfare and  increase the number of transactions.},
      url = {http://knowledge.uchicago.edu/record/1857},
      doi = {https://doi.org/10.6082/uchicago.1857},
}