scholarly journals The Random Coefficients Logit Model Is Identified

2009 ◽  
Author(s):  
Patrick Bajari ◽  
Jeremy Fox ◽  
Kyoo il Kim ◽  
Stephen Ryan
Keyword(s):  
Logit Model ◽  
Random Coefficients ◽  
Random Coefficients Logit Model

scholarly journals The random coefficients logit model is identified

2012 ◽  
Vol 166 (2) ◽  
pp. 204-212 ◽  
Author(s):  
Jeremy T. Fox ◽  
Kyoo il Kim ◽  
Stephen P. Ryan ◽  
Patrick Bajari
Keyword(s):  
Logit Model ◽  
Random Coefficients ◽  
Random Coefficients Logit Model

scholarly journals U.S. Consumers' Valuation of Quality Attributes in Beef Products

2011 ◽  
Vol 43 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Babatunde O. Abidoye ◽  
Harun Bulut ◽  
John D. Lawrence ◽  
Brian Mennecke ◽  
Anthony M. Townsend
Keyword(s):  
Willingness To Pay ◽  
Logit Model ◽  
Quality Attributes ◽  
Random Coefficients ◽  
Beef Products ◽  
Random Coefficients Logit Model

A sample of U.S. consumers were surveyed in a choice based experiment in the Fall of 2005 and Spring 2006 to elicit Consumers' preferences for quality attributes in beef products. Based on the resulting data, a random coefficients logit model is estimated, and Consumers' willingness to pay for these quality attributes in beef products is obtained. The results indicate that consumers have strong valuation for traceability, grass-fed, and U.S. origin attributes in a standard rib-eye steak and are willing to pay a premium for these attributes.

The Multidimensional Random Coefficients Multinomial Logit Model

1997 ◽  
Vol 21 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Raymond J. Adams ◽  
Mark Wilson ◽  
Wen-chung Wang
Keyword(s):  
Logit Model ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Random Coefficients

Accounting for Voter Heterogeneity within and across Districts with a Factor-Analytic Voter-Choice Model

2007 ◽  
Vol 15 (1) ◽  
pp. 67-84 ◽  
Author(s):  
Wagner A. Kamakura ◽  
José Afonso Mazzon
Keyword(s):  
Logit Model ◽  
Choice Model ◽  
Random Coefficients ◽  
Nested Logit ◽  
Second Stage ◽  
Voter Preferences ◽  
Political Preferences ◽  
Factor Analytic ◽  
Proposed Model ◽  
Voter Choice

In this study, we propose a model of individual voter behavior that can be applied to aggregate data at the district (or precinct) levels while accounting for differences in political preferences across districts and across voters within each district. Our model produces a mapping of the competing candidates and electoral districts on a latent “issues” space that describes how political preferences in each district deviate from the average voter and how each candidate caters to average voter preferences within each district. We formulate our model as a random-coefficients nested logit model in which the voter first evaluates the candidates to decide whether or not to cast his or her vote, and then chooses the candidate who provides him or her with the highest value. Because we allow the random coefficient to vary not only across districts but also across unobservable voters within each district, the model avoids the Independence of Irrelevant Alternatives Assumption both across districts and within each district, thereby accounting for the cannibalization of votes among similar candidates within and across voting districts. We illustrate our proposed model by calibrating it to the actual voting data from the first stage of a two-stage state governor election in the Brazilian state of Santa Catarina, and then using the estimates to predict the final outcome of the second stage.

Eliminating the Outside Good Bias in Logit Models of Demand with Aggregate Data

2014 ◽  
Vol 12 (1) ◽  
pp. 1-36 ◽  
Author(s):  
Dongling Huang ◽  
Christian Rojas
Keyword(s):  
Logit Model ◽  
Fixed Effects ◽  
Structural Parameters ◽  
Aggregate Data ◽  
Random Coefficients ◽  
Market Potential ◽  
Demand Model ◽  
Three Solutions ◽  
Outside Option ◽  
Monte Carlo Experiments

AbstractThe logit model is the most popular tool in estimating demand for differentiated products. In this model, the outside good plays a crucial role because it allows consumers to stop buying the differentiated good altogether if all brands simultaneously become less attractive (e.g. if a simultaneous price increase occurs). But practitioners lack data on the outside good when only aggregate data are available. The currently accepted procedure is to assume a “market potential” that implicitly defines the size of the outside good (i.e. the number of consumers who considered the product but did not purchase); in practice, this means that an endogenous quantity is approximated by a reasonable guess thereby introducing the possibility of an additional source of error and, most importantly, bias. We provide two contributions in this paper. First, we show that structural parameters can be substantially biased when the assumed market potential does not approximate the outside option correctly. Second, we show how to use panel data techniques to produce unbiased structural estimates by treating the market potential as an unobservable in both the simple and the random coefficients logit demand model. We explore three possible solutions: (a) controlling for the unobservable with market fixed effects, (b) specifying the unobservable to be a linear function of product characteristics, and (c) using a “demeaned regression” approach. Solution (a) is feasible (and preferable) when the number of goods is large relative to the number of markets, whereas (b) and (c) are attractive when the number of markets is too large (as in most applications in Marketing). Importantly, we find that all three solutions are nearly as effective in removing the bias. We demonstrate our two contributions in the simple and random coefficients versions of the logit model via Monte Carlo experiments and with data from the automobile and breakfast cereals markets.

scholarly journals Understanding Visitors’ Responses to Intelligent Transportation System in a Tourist City with a Mixed Ranked Logit Model

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Yang Liu ◽  
Jing Shi ◽  
Meiying Jian
Keyword(s):  
Real Time ◽  
Logit Model ◽  
Intelligent Transportation System ◽  
Stated Preference ◽  
Wait Time ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Random Coefficients ◽  
Information Uncertainty ◽  
Real Time Information

One important function of Intelligent Transportation System (ITS) applied in tourist cities is to improve visitors’ mobility by releasing real-time transportation information and then shifting tourists from individual vehicles to intelligent public transit. The objective of this research is to quantify visitors’ psychological and behavioral responses to tourism-related ITS. Designed with a Mixed Ranked Logit Model (MRLM) with random coefficients that was capable of evaluating potential effects from information uncertainty and other relevant factors on tourists’ transport choices, an on-site and a subsequent web-based stated preference survey were conducted in a representative tourist city (Chengde, China). Simulated maximum-likelihood procedure was used to estimate random coefficients. Results indicate that tourists generally perceive longer travel time and longer wait time if real-time information is not available. ITS information is able to reduce tourists’ perceived uncertainty and stimulating transport modal shifts. This novel MRLM contributes a new derivation model to logit model family and for the first time proposes an applicable methodology to assess useful features of ITS for tourists.

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