A Multinomial Probit Model with Latent Factors: Identification and Interpretation without a Measurement System
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A Multinomial Probit Model with Latent Factors : Identification and Interpretation without a Measurement System. / Piatek, Rémi; Gensowski, Miriam.
2017.Research output: Working paper › Research
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TY - UNPB
T1 - A Multinomial Probit Model with Latent Factors
T2 - Identification and Interpretation without a Measurement System
AU - Piatek, Rémi
AU - Gensowski, Miriam
PY - 2017/7
Y1 - 2017/7
N2 - We develop a parametrization of the multinomial probit model that yields greater insight into the underlying decision-making process, by decomposing the error terms of the utilities into latent factors and noise. The latent factors are identified without a measurement system, and they can be meaningfully linked to an economic model. We provide sufficient conditions that make this structure identified and interpretable. For inference, we design a Markov chain Monte Carlo sampler based on marginal data augmentation. A simulation exercise shows the good numerical performance of our sampler and reveals the practical importance of alternative identification restrictions. Our approach can generally be applied to any setting where researchers can specify an a priori structure on a few drivers of unobserved heterogeneity. One such example is the choice of combinations of two options, which we explore with real data on education and occupation pairs.
AB - We develop a parametrization of the multinomial probit model that yields greater insight into the underlying decision-making process, by decomposing the error terms of the utilities into latent factors and noise. The latent factors are identified without a measurement system, and they can be meaningfully linked to an economic model. We provide sufficient conditions that make this structure identified and interpretable. For inference, we design a Markov chain Monte Carlo sampler based on marginal data augmentation. A simulation exercise shows the good numerical performance of our sampler and reveals the practical importance of alternative identification restrictions. Our approach can generally be applied to any setting where researchers can specify an a priori structure on a few drivers of unobserved heterogeneity. One such example is the choice of combinations of two options, which we explore with real data on education and occupation pairs.
KW - Faculty of Social Sciences
KW - multinomial probit
KW - latent factors
KW - Bayesian analysis
KW - marginal data augmentation
KW - educational choice
KW - occupational choice
KW - C11
KW - C25
KW - C35
M3 - Working paper
T3 - IZA Discussion Paper
BT - A Multinomial Probit Model with Latent Factors
ER -
ID: 168865392