Critical assessment of five methods to correct for endogeneity in discrete-choice models

Guevara, C. Ángelo

doi:10.1016/j.tra.2015.10.005

Cited by 62 publications

(64 citation statements)

References 32 publications

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“…In the base model, the parameter of the housing price is positive and significant, which is inconsistent (Guevara 2015). In model 2, the proximity to metro, tramway and railway stations has a positive effect, which confirms the positive effect of the presence of PT stations, while the proximity to motorways has a negative one.…”

Section: Analysis Of the Model Parametersmentioning

confidence: 87%

Does the accessibility measure influence the results of residential location choice modelling?

et al. 2018

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Accessibility is essential in Land-Use Transport Interaction (LUTI) framework. For residential location choice in particular, it has always been important at theoretical level. At empirical level, the place of accessibility is doubt in some case studies, considering other location characteristics more important, like the social environment and the neighbourhood amenities. However, this result can be caused by the measurement of accessibility. In view of the wealth of approaches, this paper examines whether different accessibility measures can lead to divergent results. Using a residential location choice model for the Lyon urban area, we test various accessibility indicators and we compare the results. Accessibility is an indispensable variable, whatever is the measure. Without it, the model gives inconsistent results. Complex accessibility measures give better results but simple measures are also relevant for residential location choices modelling. The choice depends heavily on the objectives of the application especially if the model is to be used for simulation.

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Section: Analysis Of the Model Parametersmentioning

confidence: 87%

Does the accessibility measure influence the results of residential location choice modelling?

et al. 2018

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“…Endogeneity results in inconsistent estimators of the parameters in several standard econometric models, including linear models (Wooldridge, ) and discrete‐choice models (Guevara, ). The method of IVs provides a general solution to the problem of endogenous explanatory variables (Wooldridge, ).Finding suitable excluded instruments is in general quite challenging, and particularly challenging in this analysis.…”

Section: The Potential Endogeneity and Multicollinearity Problemsmentioning

confidence: 99%

The relationship between training and innovation activities in enterprises

Børing

2017

Int J Training Development

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We examine the relationship between enterprises’ use of employee training (or education) as a method to stimulate new ideas or creativity among their staff and enterprises’ innovation activities. A data set of 5204 Norwegian enterprises is used. Based on correlation coefficients, we find a positive relationship between enterprises’ use of employee training and their innovation activities. This relationship is not found significant for innovative enterprises that developed the innovations mainly by themselves or together with external partners if we control for enterprises’ use of brainstorming sessions and/or work teams to stimulate new ideas or creativity among their staff. Training can, however, be argued to be indirectly related to innovation strategies, as it is combined with other human resource practices.

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“…travel costs and travel times), then one will not be able to make valid causal inferences. To a certain extent, this fact has been acknowledged by academics who work in the field of travel demand modeling (Petrin and Train, 2010;Mabit and Fosgerau, 2010;Pinjari et al, 2011;Guevara, 2015), but such knowledge is not routinely reflected in travel demand research, and it is largely ignored by travel demand modeling practitioners.…”

Section: The Current State Of the Unionmentioning

confidence: 99%

Causal inference in travel demand modeling (and the lack thereof)

Brathwaite

Walker

2018

Journal of Choice Modelling

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This paper is about the general disconnect that we see, both in practice and in literature, between the disciplines of travel demand modeling and causal inference. In this paper, we assert that travel demand modeling should be one of the many fields that focuses on the production of valid causal inferences, and we hypothesize about reasons for the current disconnect between the two bodies of research. Furthermore, we explore the potential benefits of uniting these two disciplines. We consider what travel demand modeling can gain from greater incorporation of techniques and perspectives from the causal inference literatures, and we briefly discuss what the causal inference literature might gain from the work of travel demand modelers. In this paper, we do not attempt to "solve" issues related to the drawing of causal inferences from travel demand models. Instead, we hope to spark a larger discussion both within and between the travel demand modeling and causal inference literatures. In particular, we hope to incite discussion about the necessity of drawing causal inferences in travel demand applications and the methods by which one might credibly do so.

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Critical assessment of five methods to correct for endogeneity in discrete-choice models

Cited by 62 publications

References 32 publications

Does the accessibility measure influence the results of residential location choice modelling?

Does the accessibility measure influence the results of residential location choice modelling?

The relationship between training and innovation activities in enterprises

Causal inference in travel demand modeling (and the lack thereof)

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