Arbeitspapier
Forecasting adoption of ultra-low-emission vehicles using the GHK simulator and Bayes estimates of a multinomial probit model
In this paper we use Bayes estimates of a multinomial probit model with fully flexible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the probit Gibbs sampler, we use stated-preference data on vehicle choice from a Germany-wide survey of potential light-duty-vehicle buyers using computer-assisted personal interviewing. We show that Bayesian estimation of a multinomial probit model with a full covariance matrix is feasible for this medium-scale problem. Using the posterior distribution of the parameters of the vehicle choice model as well as the GHK simulator we derive the choice probabilities of the different alternatives. We first show that the Bayes point estimates of the market shares reproduce the observed values. Then, we define a base scenario of vehicle attributes that aims at representing an average of the current vehicle choice situation in Germany. Consumer response to qualitative changes in the base scenario is subsequently studied. In particular, we analyze the effect of increasing the network of service stations for charging electric vehicles as well as for refueling hydrogen. The result is the posterior distribution of the choice probabilities that represent adoption of the energy-effcient technologies.
- Sprache
-
Englisch
- Erschienen in
-
Series: ZEW Discussion Papers ; No. 12-017
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
Consumer Economics: Empirical Analysis
Alternative Energy Sources
- Thema
-
Discrete choice models
Bayesian econometrics
Low emission vehicles
Charging infrastructure
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Daziano, Ricardo A.
Achtnicht, Martin
- Ereignis
-
Veröffentlichung
- (wer)
-
Zentrum für Europäische Wirtschaftsforschung (ZEW)
- (wo)
-
Mannheim
- (wann)
-
2012
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Daziano, Ricardo A.
- Achtnicht, Martin
- Zentrum für Europäische Wirtschaftsforschung (ZEW)
Entstanden
- 2012
Ähnliche Objekte (12)
Forecasting adoption of ultra-low-emission vehicles using the GHK simulator and bayes estimates of a multinomial probit model
Simulated z-tests in multinomial probit models
Simulated z-Tests in Multinomial Probit Models
A Bayesian mixed logit-probit model for multinomial choice
The Multinomial Multiperiod Probit Model: Identification and Efficient Estimation
Dynamic invariant multinomial probit model: identification, pretesting and estimation
Simulated Classical Tests in the Multiperiod Multinomial Probit Model
Dynamic invariant multinomial probit model: identification, pretesting and estimation
Simulated Classical Tests in the Multiperiod Multinomial Probit Model
What can we learn about correlations from multinomial probit estimates?
Refining analytic approximation based estimation of mixed multinomial probit models by parameter selection
Refining analytic approximation based estimation of mixed multinomial probit models by parameter selection
Forecasting adoption of ultra-low-emission vehicles using the GHK simulator and bayes estimates of a multinomial probit model
Simulated z-tests in multinomial probit models
Simulated z-Tests in Multinomial Probit Models
A Bayesian mixed logit-probit model for multinomial choice
The Multinomial Multiperiod Probit Model: Identification and Efficient Estimation
Dynamic invariant multinomial probit model: identification, pretesting and estimation
Simulated Classical Tests in the Multiperiod Multinomial Probit Model
Dynamic invariant multinomial probit model: identification, pretesting and estimation
Simulated Classical Tests in the Multiperiod Multinomial Probit Model
What can we learn about correlations from multinomial probit estimates?
Refining analytic approximation based estimation of mixed multinomial probit models by parameter selection
Refining analytic approximation based estimation of mixed multinomial probit models by parameter selection
Forecasting adoption of ultra-low-emission vehicles using the GHK simulator and bayes estimates of a multinomial probit model
Simulated z-tests in multinomial probit models
Simulated z-Tests in Multinomial Probit Models
A Bayesian mixed logit-probit model for multinomial choice
The Multinomial Multiperiod Probit Model: Identification and Efficient Estimation
Dynamic invariant multinomial probit model: identification, pretesting and estimation
Simulated Classical Tests in the Multiperiod Multinomial Probit Model
Dynamic invariant multinomial probit model: identification, pretesting and estimation
Simulated Classical Tests in the Multiperiod Multinomial Probit Model
What can we learn about correlations from multinomial probit estimates?
Refining analytic approximation based estimation of mixed multinomial probit models by parameter selection