Publication

Correcting for endogeneity using the EMIS method: a case study with revealed preference data

Michel Bierlaire
2015
Conference Papers

Abstract

Endogeneity is an important issue that often arises in discrete choice models leading to biased estimates of the parameters. We propose the extended multiple indicator solution (EMIS) methodology to correct for it and exemplify it with a case study using revealed preference data about mode choice in Switzerland. The same data is used as one of the first applications of the multiple indicator solution (MIS) method. These two methodologies - EMIS and MIS - are then compared to an integrated choice and latent variable model (ICLV), and a model without any correction. In order to compare the different methodologies between them, the value of time and the time elasticity in public transportation estimates are computed and reported for each of the methods.

Official source

https://infoscience.epfl.ch/entities/publication/f9d95eb6-f58a-4482-85f6-2015eb80d829

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Michel Bierlaire
2015
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/f9d95eb6-f58a-4482-85f6-2015eb80d829

About this result

Related concepts (32)

Data

In common usage and statistics, data (USˈdætə; UKˈdeɪtə) is a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures.

Mode choice

Mode choice analysis is the third step in the conventional four-step transportation forecasting model of transportation planning, following trip distribution and preceding route assignment. From origin-destination table inputs provided by trip distribution, mode choice analysis allows the modeler to determine probabilities that travelers will use a certain mode of transport. These probabilities are called the modal share, and can be used to produce an estimate of the amount of trips taken using each feasible mode.

Big data

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.

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Correcting for endogeneity using the EMIS method: a case study with revealed preference data

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