Publication

The estimation of Generalized Extreme Value models from choice-based samples

Michel Bierlaire
2006
Reports

Abstract

We consider an estimation procedure for discrete choice models in general and Generalized Extreme Value (GEV) models in particular. It is based on a pseudo-likelihood function, generalizing the Conditional Maximum Likelihood (CML) estimator by Manski and McFadden (1981) and theWeighted Exogenous Sample Maximum Likelihood (WESML) estimator by Manski and Lerman (1977). We show that the property of Multinomial Logit (MNL) models, that consistent estimates of all parameters but the constants can be obtained from an Exogenous Sample Maximum Likelihood (ESML) estimation, does not hold in general for GEV models. We identify a specific class of GEV models with this desired property, and propose a new estimator for the more general case. This new estimator estimates the selection bias directly from the data. We illustrate the new estimator on pseudo-synthetic and real data.

Official source

https://infoscience.epfl.ch/entities/publication/ceaec43c-f7ec-4824-85ef-ccfa1a565bbf

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Michel Bierlaire
2006
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/ceaec43c-f7ec-4824-85ef-ccfa1a565bbf

About this result

Related concepts (32)

Estimator

In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished. For example, the sample mean is a commonly used estimator of the population mean. There are point and interval estimators. The point estimators yield single-valued results. This is in contrast to an interval estimator, where the result would be a range of plausible values.

Discrete choice

In economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in which the quantity of each good consumed is assumed to be a continuous variable. In the continuous case, calculus methods (e.g. first-order conditions) can be used to determine the optimum amount chosen, and demand can be modeled empirically using regression analysis.

Consistent estimator

In statistics, a consistent estimator or asymptotically consistent estimator is an estimator—a rule for computing estimates of a parameter θ0—having the property that as the number of data points used increases indefinitely, the resulting sequence of estimates converges in probability to θ0. This means that the distributions of the estimates become more and more concentrated near the true value of the parameter being estimated, so that the probability of the estimator being arbitrarily close to θ0 converges to one.

Related publications (32)

The estimation of Generalized Extreme Value models from choice-based samples

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