Advanced search
Publication Cover
Applied Economics Volume 49, 2017 - Issue 58
Submit an article Journal homepage
310
Views
2
CrossRef citations to date
0
Altmetric
Original Articles

A fast estimation procedure for discrete choice random coefficients demand model

Dong-Hyuk KimSchool of Economics, University of Queensland, Brisbane, Australia
,
Yong SongDepartment of Economics, University of Melbourne, Melbourne, AustraliaCorrespondence[email protected]
&
Huaxin XuSchool of Physical and Mathematical Sciences, University of Technology Sydney, Sydney, Australia
Pages 5849-5855 | Published online: 10 Jul 2017

References

  • Aldrich, E. M., J. Fernandez-Villaverde, A. R. Gallant, and J. F. Rubio-Ramirez. 2011. “Tapping the Supercomputer under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors.” Journal of Economic Dynamic & Control 35: 386–393.
  • Beam, A. L., S. K. Ghosh, and J. Doyle. 2014. “Fast Hamiltonian Monte Carlo Using GPU Computing.” Working Paper.
  • Berry, S. 1994. “Estimating Discrete-Choice Models of Product Differentiation.” The Rand Journal of Economics 25 (2): 242–262.
  • Berry, S., J. Levinsohn, and A. Pakes. 1995. “Automobile Prices in Market Equilibrium.” Econometrica 63 (4): 841–890.
  • Casarin, R., S. Grassi, F. Ravazzolo, and H. K. van Dijk. 2015. “Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox.” Journal of Statistical Software 68: 3.
  • Chang, L.-W., J. A. Stratton, H.-S. Kim, and W.-M. W. Hwu. 2012. “A Scalable, Numerically Stable, High-Performance Tridiagonal Solver Using GPUs.” International Conference for High Performance Computing, Networking Storage and Analysis.
  • Dube, J.-P., J. T. Fox, and C.-L. Su. 2012. “Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation.” Econometrica 80 (5): 2231–2267.
  • Iskhakov, F., J. Lee, J. Rust, E. Schjerning, and K. Seo. 2016. “Comment on ‘Constrained Optimization Approaches to Estimation of Structural Models’.” Econometrica 84 (1): 365–370.
  • Knittel, C. R., and K. Metaxoglou. 2014. “Estimation of Random-Coefficient Demand Models: Two Empiricists’ Perspective.” Review of Economics and Statistics 96 (1): 34–59.
  • Lee, J., and K. Seo. 2014. “Comment on Improving the Numerical Performance of BLP Static and Dynamic Discrete Choice Random Coefficients Demand Estimation.” Working Paper.
  • Lee, J., and K. Seo. 2015. “A Computationally Fast Estimator for Random Coefficients Logit Demand Models Using Aggregate Data.” Rand Journal of Economics 46 (1): 86–102.
  • Suchard, M. A., Q. Wang, C. Chan, J. Frelinger, A. Cron, and M. West. 2010. “Understanding GPU Programming for Statistical Computation: Studies in Massively Parallel Massive Mixtures.” Journal of Computational and Graphical Statistics 19 (2): 419–438.
  • White, G., and M. D. Porter. 2014. “GPU Accelerated MCMC for Modeling Terrorist Activity.” Computational Statistics & Data Analysis 71: 643–651.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.