Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches

Integrated choice and latent variable (ICLV) model incorporates latent factors into standard discrete choice model with aim to provide greater explanatory power. Using simulated datasets, this study makes a comparison among three estimation approaches corresponding to the sequential approach and two simultaneous approaches including the maximum simulated likelihood with GHK estimator and maximum approximate composite marginal likelihood (MACML) approach, to evaluate their abilities to recover the underlying parameters of multinomial probit-kernel ICLV model. The results show that both simultaneous approaches outperform the sequential approach in terms of estimates accuracy and efficiency irrespective of the sample sizes, and the MACML approach is the most preferable due to its best performance on recovering true values of parameters with relatively small standard errors, especially when the sample size is large enough.

[1]  D. Bolduc,et al.  Sequential and Simultaneous Estimation of Hybrid Discrete Choice Models , 2010 .

[2]  Sebastián Raveau,et al.  Inclusion of latent variables in Mixed Logit models: Modelling and forecasting , 2010 .

[3]  V. Hajivassiliou,et al.  Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models , 1993 .

[4]  Kalidas Ashok,et al.  Extending Discrete Choice Models to Incorporate Attitudinal and Other Latent Variables , 2002 .

[5]  K. Train.  Discrete Choice Methods with Simulation , 2003 .

[6]  J. Geweke,et al.  Alternative computational approaches to inference in the multinomial probit model , 1994 .

[7]  Ricardo A. Daziano,et al.  A Bayesian approach to Hybrid Choice models , 2010 .

[8]  Maria Johansson,et al.  The effects of attitudes and personality traits on mode choice , 2006 .

[9]  Denis Bolduc,et al.  Development of Integrated Choice and Latent Variable (ICLV) Models for the Residential Relocation Decision in Island Areas , 2009 .

[10]  Joan L. Walker,et al.  Extended Framework for Modeling Choice Behavior , 1999 .

[11]  C. Bhat.  Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model , 2001 .

[12]  John Geweke,et al.  Efficient Simulation from the Multivariate Normal and Student-t Distributions Subject to Linear Constraints and the Evaluation of Constraint Probabilities , 1991 .

[13]  Fiona L. Gibson,et al.  Biased estimates in discrete choice models: the appropriate inclusion of psychometric data into the valuation of recycled wastewater , 2009 .

[14]  Kevin M. Murphy,et al.  Estimation and Inference in Two-Step Econometric Models , 1985 .

[15]  Chandra R. Bhat,et al.  A simulation evaluation of the maximum approximate composite marginal likelihood (MACML) estimator for mixed multinomial probit models , 2011 .

[16]  D. McFadden,et al.  The method of simulated scores for the estimation of LDV models , 1998 .

[17]  Elisabetta Cherchi,et al.  Empirical Identification in the Mixed Logit Model: Analysing the Effect of Data Richness , 2008 .

[18]  Chandra R. Bhat,et al.  A New Estimation Approach to Integrate Latent Psychological Constructs in Choice Modeling , 2014 .

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

[20]  Chandra R. Bhat,et al.  The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models , 2011 .

[21]  Denis Bolduc,et al.  On estimation of Hybrid Choice Models , 2009 .

[22]  Joan L. Walker,et al.  Hybrid Choice Models: Progress and Challenges , 2002 .

[23]  Michael Keane,et al.  A Computationally Practical Simulation Estimator for Panel Data , 1994 .

[24]  Joan L. Walker,et al.  Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice , 2014 .

[25]  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 .

[26]  Moshe Ben-Akiva,et al.  Hybrid Choice Models with Logit Kernel: Applicability to Large Scale Models1 , 2005 .

[27]  Francisco J. Bahamonde-Birke,et al.  Is Sequential Estimation a Suitable Second Best for Estimation of Hybrid Choice Models? , 2014 .

[28]  H. Williams,et al.  Behavioural theories of dispersion and the mis-specification of travel demand models☆ , 1982 .

[29]  William H. K. Lam,et al.  Incorporating passenger perceived service quality in airport ground access mode choice model , 2010 .

[30]  D. McFadden.  The Choice Theory Approach to Market Research , 1986 .

[31]  Michael P. Keane,et al.  Four essays in empirical macro and labor economics , 1990 .

[32]  Stephen P. Jenkins,et al.  Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation , 2006, SSRN Electronic Journal.

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

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

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

[36]  Moshe Ben-Akiva,et al.  Hybrid Choice Models with Logit Kernel , 2005 .