Identification of parameters in normal error component logit-mixture (NECLM) models
Although the basic structure of logit-mixture models is well understood, important identification and normalization issues often get overlooked. This paper addresses issues related to the identification of parameters in logit-mixture models containing normally distributed error components associated with alternatives or nests of alternatives (normal error component logit mixture, or NECLM, models). NECLM models include special cases such as unrestricted, fixed covariance matrices; alternative-specific variances; nesting and cross-nesting structures; and some applications to panel data. A general framework is presented for determining which parameters are identified as well as what normalization to impose when specifying NECLM models. It is generally necessary to specify and estimate NECLM models at the levels, or structural, form. This precludes working with utility differences, which would otherwise greatly simplify the identification and normalization process. Our results show that identification is not always intuitive; for example, normalization issues present in logit-mixture models are not present in analogous probit models. To identify and properly normalize the NECLM, we introduce the 'equality condition', an addition to the standard order and rank conditions. The identifying conditions are worked through for a number of special cases, and our findings are demonstrated with empirical examples using both synthetic and real data. Copyright © 2007 John Wiley & Sons, Ltd.
Volume (Year): 22 (2007)
Issue (Month): 6 ()
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- Bolduc, D. & Ben-Akiva, M., 1991. "A Multinational Probit Formulation for Large Choice Sets," Papers 9110, Laval - Recherche en Energie.
- McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457 Elsevier.
- Chiou, Lesley & Walker, Joan L., 2007. "Masking identification of discrete choice models under simulation methods," Journal of Econometrics, Elsevier, vol. 141(2), pages 683-703, December.
- McFadden, Daniel, 1989.
"A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration,"
Econometric Society, vol. 57(5), pages 995-1026, September.
- Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers 464, Massachusetts Institute of Technology (MIT), Department of Economics.
- Joseph A. Herriges & Daniel J. Phaneuf, 2002. "Inducing Patterns of Correlation and Substitution in Repeated Logit Models of Recreation Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(4), pages 1076-1090.
- Andrew A. Goett & Kathleen Hudson & Kenneth E. Train, 2000. "Customers' Choice Among Retail Energy Suppliers: The Willingness-to-Pay for Service Attributes," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 1-28.
- Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D., 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, January.
- Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
- Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, January.
- Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2, June.
- Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
- Céline Bonnet, 2001. "Assessing consumer response to Protected Designation of Origin labelling: a mixed multinomial logit approach," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 28(4), pages 433-450, December.
- Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
- Bolduc, Denis, 1999. "A practical technique to estimate multinomial probit models in transportation," Transportation Research Part B: Methodological, Elsevier, vol. 33(1), pages 63-79, February.
- Füsun Gönül & Kannan Srinivasan, 1993. "Modeling Multiple Sources of Heterogeneity in Multinomial Logit Models: Methodological and Managerial Issues," Marketing Science, INFORMS, vol. 12(3), pages 213-229.
- Taylor, Cameron L. & Adamowicz, Wiktor L. & Luckert, Martin K., 2003. "Preferences over the timing of forest resource use," Journal of Forest Economics, Elsevier, vol. 9(3), pages 223-240.
- Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 471-483, December.
- Bhat, Chandra R. & Gossen, Rachel, 2004. "A mixed multinomial logit model analysis of weekend recreational episode type choice," Transportation Research Part B: Methodological, Elsevier, vol. 38(9), pages 767-787, November.
- David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
- Stern, Steven, 1992. "A Method for Smoothing Simulated Moments of Discrete Probabilities in Multinomial Probit Models," Econometrica, Econometric Society, vol. 60(4), pages 943-952, July.
- Steckel, Joel H & Vanhonacker, Wilfried R, 1988. "A Heterogeneous Conditional Logit Model of Choice," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 391-398, July.
- Ben-Akiva, M. & Bolduc, D. & Bradley, M., 1993. "Estimation of Travel Choice Models with Randomly Distributed Values of Time," Papers 9303, Laval - Recherche en Energie.
- Bhat, Chandra R. & Castelar, Saul, 2002. "A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area," Transportation Research Part B: Methodological, Elsevier, vol. 36(7), pages 593-616, August.
- Therese Hindman Persson, 2002. "Welfare calculations in models of the demand for sanitation," Applied Economics, Taylor & Francis Journals, vol. 34(12), pages 1509-1518.
- Carlos Barros & Isabel Proença, 2005. "Mixed Logit Estimation Of Radical Islamic Terrorism In Europe And North America: A Comparative Study," Microeconomics 0508005, EconWPA.
- Bhat, Chandra R. & Guo, Jessica, 2004. "A mixed spatially correlated logit model: formulation and application to residential choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 38(2), pages 147-168, February.
- Glasgow, Garrett, 2001. "Mixed Logit Models for Multiparty Elections," Political Analysis, Cambridge University Press, vol. 9(02), pages 116-136, January.
- Bunch, David S., 1991. "Estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 1-12, February. Full references (including those not matched with items on IDEAS)
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