Hybrid Choice Models: The Identification Problem

Choice modelling is an increasingly important technique for forecasting and valuation, with applications in fields such as transportation, health and environmental economics. For this reason it has attracted attention from leading academics and practitioners and methods have advanced substantially in recent years. This Handbook, composed of contributions from senior figures in the field, summarises the essential analytical techniques and discusses the key current research issues. It will be of interest to academics, students and practitioners in a wide range of areas.

[1]  Joan L. Walker,et al.  Identification of parameters in normal error component logit-mixture (NECLM) models , 2007 .

[2]  Cinzia Cirillo,et al.  Reducing simulation bias in mixed logit model estimation , 2008 .

[3]  Joan L. Walker.  Extended discrete choice models : integrated framework, flexible error structures, and latent variables , 2001 .

[4]  Khandker Nurul Habib,et al.  Incorporating the explicit role of psychological factors on mode choice: a hybrid mode choice model by using data from an innovative psychometric survey , 2011 .

[5]  David A. Hensher,et al.  The Mixed Logit Model: the State of Practice and Warnings for the Unwary , 2001 .

[6]  Jinhuan Zhao.  Preference accommodating and preference shaping : incorporating traveler preferences into transportation planning , 2009 .

[7]  M. Sobel,et al.  Identification Problems in the Social Sciences. , 1996 .

[8]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[9]  D. Bolduc.  A practical technique to estimate multinomial probit models in transportation , 1999 .

[10]  Till Dannewald,et al.  Incorporating Latent Variables into Discrete Choice Models — A Simultaneous Estimation Approach Using SEM Software , 2008 .

[11]  Leslie A. Hayduk.  Structural equation modeling with LISREL: essentials and advances , 1987 .

[12]  Franklin M. Fisher,et al.  The identification problem in econometrics , 1967 .

[13]  D. McFadden,et al.  MIXED MNL MODELS FOR DISCRETE RESPONSE , 2000 .

[14]  Akshay Vij,et al.  The power and value of green in promoting sustainable transport behavior , 2011 .

[15]  Kenneth A. Bollen,et al.  Structural Equations with Latent Variables , 1989 .

[16]  Joan L. Walker,et al.  Integration of Choice and Latent Variable Models , 1999 .

[17]  B. Muthén,et al.  How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power , 2002 .

[18]  Robert M. O'Brien,et al.  Identification of Simple Measurement Models with Multiple Latent Variables and Correlated Errors , 1994 .

[19]  D. A. Kenny,et al.  Correlation and causality , 1979 .

[20]  Ricardo A. Daziano,et al.  Covariance, identification, and finite-sample performance of the MSL and Bayes estimators of a logit model with latent attributes , 2013 .

[21]  Lesley Chiou,et al.  Masking Identification of Discrete Choice Models Under Simulation Methods , 2007 .

[22]  Juan Antonio Carrasco,et al.  Review and assessment of the nested logit model , 2002 .

[23]  Edward E. Rigdon,et al.  A Necessary and Sufficient Identification Rule for Structural Models Estimated in Practice. , 1995, Multivariate behavioral research.

[24]  F. E. Grubbs.  Procedures for Detecting Outlying Observations in Samples , 1969 .

[25]  Outlying Sensitivities in Discrete Choice Data: Causes, Consequences and Remedies , 2009 .

[26]  Juan de Dios Ortúzar,et al.  Practical and empirical identifiability of hybrid discrete choice models , 2012 .

[27]  T. Reilly,et al.  A Necessary and Sufficient Condition for Identification of Confirmatory Factor Analysis Models of Factor Complexity One , 1995 .

[28]  Joan L. Walker,et al.  Generalized random utility model , 2002, Math. Soc. Sci..