A practitioner's guide to Bayesian estimation of discrete choice dynamic programming models

Quantitative Marketing and Economics

Volumn 10, Issue 2, 2012, Pages 151-196

  Ching, Andrew T ^a   Imai, Susumu ^b   Ishihara, Masakazu ^c   Jain, Neelam ^d  

^a UNIVERSITY OF TORONTO   (Canada)

^b QUEEN S UNIVERSITY   (Canada)

^c NEW YORK UNIVERSITY   (United States)

^d CITY UNIVERSITY   (United Kingdom)

Author keywords

Bayesian estimation; Discrete choice models; Dynamic programming; Rewards programs

Indexed keywords

EID: 84860883471     PISSN: 15707156     EISSN: 1573711X     Source Type: Journal    
DOI: 10.1007/s11129-012-9119-6     Document Type: Article

References (51)

1. Ackerberg, D. A. (2003). Advertising, learning, and consumer choice in experience good markets: An empirical examination. International Economic Review, 44(3), 1007-1040. (Pubitemid 37001801)
2. Ackerberg, D. A. (2009). A new use of importance sampling to reduce computational burden in simulation estimation. Quantitative Marketing and Economics, 7(4), 343-376.
3. Aguirregabiria, V., & Mira, P. (2002). Swapping the nested fixed point algorithm: A class of estimators for discrete Markov decision models. Econometrica, 70(4), 1519-1543. (Pubitemid 34799528)
4. Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88, 669-679.
5. Allenby, G. M. (1994). An introduction to hierarchical Bayesian modeling. Tutorial Notes, Advanced Research Techniques Forum, American Marketing Association.
6. Allenby, G. M., & Lenk, P. J. (1994). Modeling household purchase behavior with logistic normal regression. Journal of the American Statistical Association, 89, 1218-1231.
7. Allenby, G. M., & Rossi, P. E. (2006). Hierarchical Bayes models: A practitioner's guide. In R. Grover, & M. Vriens (Eds.), The handbook of marketing research. Newbury Park: Sage Publications.
8. Berry, S. T., Levinshohn, J., & Pakes, A. (1995). Automobile prices in market equilibrium. Econometrica, 63(4), 841-890.
9. Brown, M., & Flinn, C. J. (2011). Family law effects on divorce, fertility and child investment. Working paper, Department of Economics, New York University.
10. Černý, V. (1985). Thermodynamical approach to the travelling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45(1), 41-51. (Pubitemid 15462741)
11. Ching, A. T. (2010). A dynamic oligopoly structural model for the prescription drug market after patent expiration. International Economic Review, 51(4), 1175-1207.
12. Ching, A. T., Imai, S., Ishihara, M., & Jain, N. (2009). A dynamic model of consumer learning and forgetting. Work-in-progress, Rotman School of Management, University of Toronto.
13. Ching, A. T., Imai, S., Ishihara, M., & Jain, N. (2012). Identification of dynamic models of rewards program. Working paper, Rotman School of Management, University of Toronto.
14. Crawford, G. S., & Shum,M. (2005). Uncertainty and learning in pharmaceutical demand. Econometrica, 73(4), 1137-1174.
15. Diermeier, D., Keane, M. P., & Merlo, A. M. (2005). A political economy model of congressional careers. American Economic Review, 95, 347-373. (Pubitemid 40801727)
16. Erdem, T., Imai, S., & Keane, M. P. (2003). Brand and quality choice dynamics under price uncertainty. Quantitative Marketing and Economics, 1(1), 5-64.
17. Erdem, T., & Keane, M. P. (1996). Decision making under uncertainty: Capturing dynamic brand choice processes in turbulent consumer goods markets. Marketing Science, 15(1), 1-20.
18. Geweke, J., Houser, D., & Keane, M. P. (2001). Simulation based inference for dynamic multinomial choice models. In B. H. Baltagi (Ed.), A companion to theoretical econometrics (pp. 466-493). London: Blackwell.
19. Geweke, J. F., & Keane, M. P. (2000). Bayesian inference for dynamic discrete choice models without the need for dynamic programming. In R. Mariano, T. Schuermann, & M. J. Weeks (Eds.), Simulation based inference and econometrics: Methods and applications. Cambridge: Cambridge University Press.
20. Gönül, F., & Srinivasan, K. (1996). Estimating the impact of consumer expectations of coupons on purchase behavior: A dynamic structural model. Marketing Science, 15(3), 262-279.
21. Hartmann, W. R. (2006). Intertemporal effects of consumption and their implications for demand elasticity estimates. Quantitative Marketing and Economics, 4(4), 325-349. (Pubitemid 44768833)
22. Hendel, I., & Nevo, A. (2006). Measuring the implications of sales and consumer inventory behavior. Econometrica, 74(6), 1637-1673. (Pubitemid 44673209)
23. Hitsch, G. (2006). An empirical model of optimal dynamic product launch and exit under demand uncertainty. Marketing Science, 25(1), 25-50. (Pubitemid 43333331)
24. Hotz, J. V., & Miller, R. (1993). Conditional choice probabilities and the estimation of dynamic models. Review of Economic Studies, 60(3), 497-529.
25. Imai, S., Jain, N., & Ching, A. (2009a). Bayesian estimation of dynamic discrete choice models. Econometrica, 77(6), 1865-1899.
26. Imai, S., Jain, N., & Ching, A. (2009b). Supplement to 'Bayesian estimation of dynamic discrete choice models'. Econometrica (Supplementary Material), 77. http://www.econometricsociety.org/ecta/Supmat/5658-proofs.pdf.
27. Imai, S., & Krishna, K. (2004). Employment, deterrence and crime in a dynamic model. International Economic Review, 45(3), 845-872. (Pubitemid 39223997)
28. Ishihara, M. (2011). Dynamic demand for new and used durable goods without physical depreciation. Ph.D. dissertation, Rotman School of Management, University of Toronto.
29. Keane, M. P., & Wolpin, K. I. (1994). The solution and estimation of discrete choice dynamic programming models by simulation and interpolation: Monte Carlo evidence. Review of Economics and Statistics, 76(4), 648-672.
30. Keane, M. P., & Wolpin, K. I. (1997). The career decisions of young men. Journal of Political Economy, 105, 473-521.
31. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671-680.
32. Lancaster, T. (1997). Exact structural inference in optimal job search models. Journal of Business and Economic Statistics, 15(2), 165-179.
33. McCulloch, R., & Rossi, P. E. (1994).An exact likelihood analysis of themultinomial probitmodel. Journal of Econometrics, 64, 207-240.
34. Norets,A. (2009). Inference in dynamic discrete choice models with serially correlated unobserved state variables. Econometrica, 77(5), 1665-1682.
35. Norets, A. (2010). Continuity and differentiability of expected value functions in dynamic discrete choice models. Quantitative Economics, 1(2), 305-322.
36. Osborne, M. (2011). Consumer learning, switching costs, and heterogeneity: A structural examination. Quantitative Marketing and Economics, 9(1), 25-70.
37. Pantano, J. (2008). Essays in applied microeconomics. Ph.D. dissertation, UCLA.
38. Roos, J.M. T., Mela, C. F., & Shachar, R. (2011). Hyper-media search and consumption. Working paper, Fuqua School of Business, Duke University.
39. Rossi, P. E., & Allenby, G. M. (1999). Marketing models of consumer heterogeneity. Journal of Econometrics, 89, 57-78. (Pubitemid 128416645)
40. Rossi, P. E., Allenby,G.M., & McCulloch, R. (2005). Bayesian statistics and marketing. Chichester: Wiley.
41. Rossi, P. E., McCulloch, R., & Allenby, G. M. (1996). The value of purchase history data in target marketing. Marketing Science, 15, 321-340.
42. Rust, J. (1987). Optimal replacement of gmc bus engines: An empirical model of Harold Zurcher. Econometrica, 55(5), 999-1033.
43. Rust, J. (1988). Maximum likelihood estimation of discrete control processes. SIAM Journal on Control and Optimization, 26(5), 1006-1024.
44. Rust, J. (1997). Using randomization to break the curse of dimensionality. Econometrica, 65(3), 487-516. (Pubitemid 127466157)
45. Santos, M. S., & Rust, J. (2004). Convergence properties of policy iteration. SIAM Journal on Control and Optimization, 42(6), 2094-2115.
46. Silverman, B.W. (1986). Density estimation for statistics and data analysis. London: Chapman and Hall.
47. Song, I., & Chintagunta, P. K. (2003). A micromodel of new product adoption with heterogeneous and forward looking consumers: Application to the digital camera category. Quantitative Marketing and Economics, 1(4), 371-407.
48. Sun, B. (2005). Promotion effect on endogenous consumption. Marketing Science, 24(3), 430-443. (Pubitemid 41409335)
49. Train, K. E. (2003). Discrete choice methods with simulation. Cambridge: Cambridge University Press. Available at http://elsa.berkeley.edu/books/choice2. html.
50. Walsh, B. (2004). Markov Chain Monte Carlo and Gibbs sampling. Lecture Notes for EEB 581, University of Arizona. http://nitro.biosci.arizona.edu/ courses/EEB581-2004/handouts/Gibbs.pdf.
51. Yang, B., & Ching, A. (2010). Dynamics of consumer adoption of financial innovation: The case of ATM cards. Working paper, Rotman School of Management, University of Toronto. Available at SSRN: http://ssrn.com/abstract= 1434722.