Publication

Correcting for endogeneity using the multiple indicator solution

Michel Bierlaire
2016
Reports

Abstract

In this paper we extend the Multiple Indicator Solution (MIS),used to correct for endogeneity, so that it can also be used when there are interactions between observed and unobserved factors in the specification of the utility function. We show the theoretical derivation and illustrate it with a case study. Policy indicators, such as time elasticity and value of time are derived, and the results are compared with a logit model and with an Integrated Choice and Latent Variable (ICLV) model. The contribution is twofold: this is the first application of the MIS methodology with revealed preference data, and the MIS has been adapted to account for interactions between observed and unobserved attributes.

Official source

https://infoscience.epfl.ch/entities/publication/dd4171fe-d3fb-44b2-84a8-66d23ca38d39

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Michel Bierlaire
2016
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/dd4171fe-d3fb-44b2-84a8-66d23ca38d39

About this result

Related concepts (32)

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.

Logistic regression

In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination).

Logit

In statistics, the logit (ˈloʊdʒɪt ) function is the quantile function associated with the standard logistic distribution. It has many uses in data analysis and machine learning, especially in data transformations. Mathematically, the logit is the inverse of the standard logistic function , so the logit is defined as Because of this, the logit is also called the log-odds since it is equal to the logarithm of the odds where p is a probability. Thus, the logit is a type of function that maps probability values from to real numbers in , akin to the probit function.

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Correcting for endogeneity using the multiple indicator solution

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