Discrete choice model

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Controlling for the effects of information in a public goods discrete choice model

Controlling for the effects of information in a public goods discrete choice model

Finally, it should be noted that the preference (taste) and scale parameters are not sepa- rately identifiable, as they are always observed as a in multiplicative form. Hess and Rose (2012) demonstrate that in the case when (1) all parameters are modelled as random and (2) all parameters are allowed to be correlated, introducing the random scale coefficient is equiv- alent to allowing for a more flexible distribution for the taste-scale mixture. In many cases, however, introducing a random scale coefficient is useful because it allows one to account for all (random or non-random) parameters for a particular individual becoming larger or smaller, relative to the utility function error term (whose variance is normalized to one). In this way, a single parameter allows us to observe how the deterministic part of respondent’s utility function varies relatively to the random component, from the perspective of the analyst. This approach provides a convenient way of observing and interpreting the level of heteroge- neous predictability (‘perceived randomness’) of agent’s choices by the econometrician. In our case, as described in the next section, we not only make the scale coefficient random, but also introduce information-set-specific covariates into its mean and variance, thus proposing a useful, reduced form method of empirical investigation of the effects of information and updating in a public goods discrete choice model. 13
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A Discrete Choice Model for Web Site Work Results

A Discrete Choice Model for Web Site Work Results

We build a sequential discrete choice model for web site outputs. Explanatory variables set includes a number of visited pages, seconds spent on the web site, and dummy variables for specific pages visited (a page with prices information, a portfolio page). Also we investigate an influence of search engines (Google, Yahoo, MSN), which refer a visitor to a corporate web site and keywords used for pay(per(click advertising campaigns.

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Incorporating needs-satisfaction in a discrete choice model of leisure activities

Incorporating needs-satisfaction in a discrete choice model of leisure activities

The notion of needs-satisfaction is intrinsically related to the concept of leisure travel activities. If the activity does not satisfy a particular need, individuals are unlikely to undertake it, including its associated journey. The inherently latent nature of needs and anticipated needs-satisfaction has been acknowledged throughout the literature on leisure activity participation (e.g. Tinsley and Kass, 1979). Typically, mathematical psychologists apply structural equation models to link indicators, in the form of subjective needs- satisfaction statements, to latent constructs and thereby identify the driving factors. In this paper, we develop a particular type of structural equation model, also known as a hybrid choice model, which apart from explaining heterogeneity in (latent) anticipated needs- satisfaction also enables researchers to study its impact on leisure activity-travel decisions. The inclusion of anticipated latent needs-satisfaction in a choice model is a step forward from what, to our knowledge, has been done in the leisure modelling literature so far (e.g. Jun et al. 2012). For example, Chen et al. (2013) only measure the relationship between leisure motivation and leisure satisfaction in a structural equation model without modelling the actual decision. Leversen et al. (2012) ask about activity participation amongst adolescents, but treat it as an exogenous explanatory variable of the latent construct life satisfaction. We are aware of some studies (e.g. King et al. 2006) that use participation intensity as a dependent variable in structural equation modelling, i.e. how often do you undertake activity x per week. This appears to be a method mainly applied in medical sciences with little connection to the type of behavioural models applied in (transport) economics and geography. As such, the behavioural scope of traditional utility-based tourism and leisure activity-travel choice models is extended. Using a stated choice-dataset involving hypothetical choices between leisure activities made by citizens of The Netherlands aged 60 and older, we contrast regret-minimisation based discrete choice models including and excluding the subjective measurements of need- satisfaction. Empirical results show that approximately 40% of the unobserved heterogeneity in the activity specific utility levels can be attributed to anticipated needs-satisfaction. Hence,
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Recreational pursuits on marginal farm land: a discrete choice model of Irish farm commonage recreation

Recreational pursuits on marginal farm land: a discrete choice model of Irish farm commonage recreation

In calculating the travel cost to the commonage area, the Automobile Association (AA) of Ireland’s calculations for motoring expenses were used. It is assumed here that recreationalists only take into account the operating expenses when deciding to make a trip to the commonage area or not. Considering that the standing charges will have to be paid regardless of whether a trip is made or not and the fact that the operating cost of the car is directly dependent on miles travelled, this is not an unrealistic assumption. For these reasons, the AA estimate for operating cost per mile of € 0.25 is taken as the recreationalists travel cost per mile in this study. Table 1 summarises some of the survey responses and highlights some of the variables included in the TCM analysis. The sample of 241 observations represents the total number of surveys that were used in the empirical analysis. Just 17 of the total 265 surveys were returned incomplete, lacking some portion of the data that was needed to be included in the final model specification.
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How many wheels will it be today? An exploratory research into variables useful for modeling bicycle mode
choice with a discrete choice model

How many wheels will it be today? An exploratory research into variables useful for modeling bicycle mode choice with a discrete choice model

In Dutch urban transportation models, currently used in municipal practice, mode choice and destination choice are usually modeled simultaneously, using a gravity model. The resistances in the skim matrices are calculated in the same way for each mode, and are based on either travel time, trip distance or a combination of both in the form of a generalized cost function. While these attributes are generally sufficient for the modeling of car traffic, they are insufficient for the modeling of slow modes as the bicycle (Krizek, Forsyth, & Baum, 2009): more variables appear to play an important role in the choice to use the bicycle. Inclusion of ‘softer’ variables as habit and attitudes, or socio- economic characteristics could improve urban transportation models. Gravity models are not capable of incorporating such variables: each extra variable would double the number of gravity functions needed. A different model type is needed, that can incorporate more and different variables, and that is what this research is to set the first steps towards.
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A generic discrete choice model of automobile purchase

A generic discrete choice model of automobile purchase

Since the seminal papers by Lave and Train [24] and Manski and Sherman [25], automobile demand and vehicle choice have been the subjects of multiple studies by transport re- searchers. Most studies (e. g., [3, 4, 8, 10, 21, 34]) are based on disaggregate discrete choice modelling of household be- haviour. But some are also based on aggregate sales data, whereby one estimates total demand or market shares held by various vehicle models (e.g., [1, 5, 14, 20, 22]). Common to most of these studies is that their data sets and methodology are too crude or too incomplete to allow for reliable predic- tions of the car fleet composition under varying fiscal and regulatory policy options. Some recent studies have, however, come a long way towards modelling the complex, joint deci- sion processes of vehicle choice and usage [6, 9, 18, 19, 28]. The introduction of novel fuel and propulsion technologies, such as battery, (plug-in) hybrid and fuel cell electric vehicles, and the need to combat the exhaust emission of local and global pollutants from the passenger car fleet have enhanced the political interest in the vehicle purchase choices made by private households and firms, and in how these choices can be influenced through fiscal and regulatory penalties and incen- tives. In Norway, a large number of incentives have been implemented over the last 10–12 years, most importantly a steeply CO 2 -graduated vehicle purchase tax. These incite a
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Patterns of consumption in a discrete choice model with asymmetric interactions

Patterns of consumption in a discrete choice model with asymmetric interactions

Discrete choice models have been analyzed using the techniques of statistical mechanics. In the case of sym- metric interactions, the equilibrium condition can be ex- pressed in terms of the Boltzman distribution. This is no more the case in models with asymmetric interactions. As a consequence, the long time behaviour of the system has to be calculated by solving the dynamical problem (which in most cases is not possible analytically) and cannot be evaluated by equilibrium ensemble averages. We use numerical simulations with Glauber dynamics (Glauber 1963) to explore, the dynamical properties of the model. We implement three different evolution algo- rithms with: parallel, sequential and random-sequential updating rules, depending on the order on which indi- vidual agents update their decision. We first focus on the deterministic limit and study the attractors of the model, which determine the steady state, long-time be- haviour of the consumption behaviour. Depending on the evolution algorithm as well as the degree of the asym- metry the attractors can be either fixed points or limit cycles. We then introduce noise in the system and study how this affects the dynamics of consumption. Eventu-
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Time and income constraints in discrete choice models with an application to mode choice

Time and income constraints in discrete choice models with an application to mode choice

In this paper, we will discuss a special case of demand models, namely discrete choice models. The paper contains a theoretical part and an empirical part. In the former, we discuss how income and time enter a discrete choice model in a way that is consistent with microeconomic theory. The main points are the discussions about the necessity to assume working time restrictions ex ante, the possibility to include time or time components in a direct utility function and the implications of this for value of time measurements, and the possibility to map the inherently ordinal conditional indirect utility function to a cardinal utility scale in different ways. Further, we discuss Taylor expansion of the conditional indirect utility function, interpretations of this and the possibility to choose expansion point. Two natural expansion points to consider are the gross available income and time and the expected residual income and time. The latter is special in that it is an endogenously determined point, depending on the estimated parameter values. We briefly discuss the econometrics connected with this, and show in the empirical part that it is possible to estimate this type of model.
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Discrete Choice and Rational Inattention: a General Equivalence Result

Discrete Choice and Rational Inattention: a General Equivalence Result

From an applied point of view, an important implication of Proposition 5 is that it allows us to formulate rational inattention models that have complex substitution patterns, beyond the multinomial logit case. In this example, we consider a GERI model with an information cost function derived from a nested logit discrete choice model. The nested logit choice probabilities are consistent with a discrete choice model in which the utility shocks ǫ are jointly distributed in the class of generalized extreme value distributions. Among applied researchers, the nested logit model is often preferred over the multinomial logit model because it allows some products to be closer substitutes than others, thus avoiding the “red bus/blue bus” criticism. 10 We partition the set of options i ∈ {1, . . . , N } into mutually exclusive nests, and let g i denote the nest containing option i. Let ζ g i ∈ (0, 1] be nest-specific
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Gastrointestinal events and association with initiation of treatment for osteoporosis

Gastrointestinal events and association with initiation of treatment for osteoporosis

interest was the presence of a post-index GI event; models were also adjusted for the presence of pre-index GI events (discrete choice model only), age group, pre-index medica- tion use (gastroprotective agents, NSAIDs, glucocorticoster- oids), pre-index CCI, and selected pre-index comorbidities (chronic inflammatory bowel disease, chronic inflammatory joint disease, celiac disease, diabetes, depression, chronic kidney failure, hypertension, GI mucositis and urination problems, hyperparathyroidism, vitamin D deficiency, and fatigue). The Cox regression model separately quanti- fied the effects of a post-index GI event for those with and without pre-index GI events.
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The role of financial factors in the mobility and location choices of General Practitioners in Australia

The role of financial factors in the mobility and location choices of General Practitioners in Australia

This paper examines the role of financial factors in re- locating established GPs into metropolitan neighbour- hoods with low-socioeconomic status. GPs’ decisions to locate largely in affluent areas can result in inefficiencies in the allocation of health resources [24]. The paper uses a discrete choice model and panel data on GPs’ actual observed changes in location from one year to the next. The model accounts for several non-pecuniary practice attributes and a range of personal characteristics. In- corporating the dynamic aspects of location choice leads to a more accurate and relevant assessment of the im- portance of financial factors than what is currently avail- able. Once all these aspects are accounted for, a policy simulation suggests that financial incentives are not very effective at inducing established GPs to relocate.
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The Intuition Behind Income Effects of Price Changes in Discrete Choice Models, and a Simple Method for Measuring the Compensating Variation

The Intuition Behind Income Effects of Price Changes in Discrete Choice Models, and a Simple Method for Measuring the Compensating Variation

4. Recognising the limitations of S&R’s consumer surplus measure, McFadden (1995) and Karlström and Morey (2001) devised methods for measuring the expected Hicksian compensating variation in the presence of non-linear income effects. However, both methods call for significant computational and/or analytical effort following estimation of the discrete choice model, and this perhaps explains why neither method has been widely adopted in practice. The present paper proposed a simple method for approxi- mating the expected Hicksian compensating variation. This entails the derivation of analytical bounds, where one bound is given by the expected Slutsky compensating variation in the event of zero substitution between goods, and the other bound is given by the corresponding measure in the event of maximum substitution between goods. Table 1 Models of destination
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Re-estimating UK Appraisal Values for Non-work Travel time Savings Using Random Coefficient Logit Model

Re-estimating UK Appraisal Values for Non-work Travel time Savings Using Random Coefficient Logit Model

The official appraisal values of travel time savings (VTTS) for non-work trips in UK were estimated by basic discrete choice model on stated choice data collected over 20 years ago. This choice model developed by Bates and Whalen (2001) was specified to address some long-standing issues in the field of VTTS valuation including the sign and size of VTTS while allowing continuous interactions between VTTS and journey covariates. With respect to the size issue, it was found that a “tapering” function, whereby time changes are increasingly discounted, could best explain the lower unit utility observed for small time savings (STS). While this set of non-work VTTS is still being used for transport appraisal in UK, the field of discrete choice modelling has evolved significantly brought by a leap of computing power and improved simulation techniques. Notably, advanced model such as mixed multinomial logit (MMNL) has been widely used to facilitate more realistic travel behavioural modelling by explaining random taste heterogeneity across respondents, which cannot be achieved in a deterministic manner. Also, techniques in specifying such model for VTTS valuation are well established by researchers nowadays. The key objective of this research was then to apply the MMNL model and re-estimate the current UK VTTS within a random coefficient logit framework. Alongside the theoretical discussions, this paper presents a synthesis of empirical evidence to support an updated appraisal value for non-work travel time savings in UK. Some key findings from this paper include a much higher mean value for the VTTS and the significantly reduced “perception effect” for the STS. In particular, this research found that MMNL model substantially reduces the “tapering” parameter of the discounting function for STS such that the “perception effect” of the VTTS becomes minimal. This finding suggests that travel benefits due to STS should be included for transport appraisal and it challenges some appraisal frameworks for countries like Germany where VTTS are discounted or even completely ignored for STS.
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Residential broadband subscription demand: an econometric analysis of Australian choice experiment data

Residential broadband subscription demand: an econometric analysis of Australian choice experiment data

The econometric model employed here has the advantage that it can be developed in a random utility choice problem context. The structure of the experiment requires a trade-o€ between installation and rental price is made in selecting an optimal outcome. Accordingly, installation and rental price, income and other household demographic characteristics are linked to subscription through a binomial discrete choice model. This paper presents preliminary empirical results for a simple network subscription model. Estimates are based on a small set of hypothetical choice experiments conducted by the authors. The results are at least illustrative and possibly of some interest per se.
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Networks and Rewards among Hollywood Artists: Evidence for a Social Structural Ordering of Creativity

Networks and Rewards among Hollywood Artists: Evidence for a Social Structural Ordering of Creativity

Table 3 Results for Discrete Choice Model Predicting Peers’ Choice Model 1 Model 2 Variables Movie Genre dummies Movie Rating dummies Movie Sequel dummy Team Reputation Individual Degree[r]

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Simulation Based Inference for Dynamic Multinomial Choice Models

Simulation Based Inference for Dynamic Multinomial Choice Models

The econometrician is interested in drawing inferences about θ , the vector of structural parameters. One econometric procedure to accomplish this (see Rust (1987) or Wolpin (1984)) requires using dynamic programming to solve system (1.1)-(1.3) at many trial parameter vectors. At each parameter vector, the solution to the system is used as input to evaluate a prespecified econometric objective function. The parameter space is systematically searched until a vector that “optimizes” the objective function is found. A potential drawback of this procedure is that, in general, solving system (1.1)-(1.3) with dynamic programming is extremely computationally burdensome. The reason is that the mathematical expectations that appear on the right-hand side of (1.1) are often impossible to compute analytically, and very time-consuming to approximate well numerically. Hence, as a practical matter, this estimation procedure is useful only under very special circumstances (for instance, when there is a small number of state-variables.) Consequently, a literature has arisen that suggests alternative approaches to inference in dynamic multinomial choice models.
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Threshold Extension of Gallium Arsenide/Aluminum Gallium Arsenide Terahertz Detectors and Switching in Heterostructures

Threshold Extension of Gallium Arsenide/Aluminum Gallium Arsenide Terahertz Detectors and Switching in Heterostructures

Despite the important role these workers can play in fostering economic growth and development in a region, relatively little is known regarding the individual and geographic factors that influence their location decisions. Understanding these factors is necessary in order for policymakers to identify what policy objectives can be taken to increase the inflow and retention of highly educated scientists and engineers. 1 This dissertation addresses this issue by examining the determinants of the residential location choices of new S&E Ph.D.s at the time of degree. A random utility model (RUM) of migration is employed to estimate how city amenities influence Ph.D.s’ utility and their choice of where to work and live. The RUM uses Ph.D.s’ observed (utility-maximizing) location choice at the primary metropolitan statistical area (MSA) level to infer how the amenities provided by an MSA affect that probability that it will be chosen.
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Consumer preferences for food allergen labeling

Consumer preferences for food allergen labeling

While it is never possible to know if respondents com- pletely understood the task or questions, the results do provide an assessment based on their face validity, e.g. those with a need for allergen avoidance had stronger preferences. Furthermore, we incorporated two fixed repeated choice questions in the final version of the sur- vey which showed that approximately 10% of respond- ents were considered inconsistent and were deemed to not have made meaningful choices. Data from these respondents were therefore excluded from analysis, contributing to the validity of the final results. Finally, our results are also in agreement with earlier qualitative Fig. 2 Relative importance of attributes by class. The preference
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A Microeconomic Theory–Based Latent Class Multiple Discrete–Continuous Choice Model of Time Use and Goods Consumption

A Microeconomic Theory–Based Latent Class Multiple Discrete–Continuous Choice Model of Time Use and Goods Consumption

of the segments following those probabilities. Then the values of time within each of the segments were computed. The values of time implied from these alternative models are presented in the last six rows but one of Table 3 and those implied from the proposed model are presented in the last row. It can be observed that all three alternative models overestimate the values of time allocated to both work and leisure. The first alternative model and the formulation of Jara-Díaz et al. (4) do not allow corner solutions and do not allow minimum consumption and minimum time allocation. In the formu- lation of Castro et al., a linear relationship is assumed between time assigned to activities and the expense associated with those activities by using money prices of time allocation to different activities (9). This method not only creates a transformation between money and time that is not necessarily always true but also precludes the inclu- sion of goods consumed (or expenditures for consuming goods) in the utility functions. Also, the formulation of Castro et al. does not consider minimum consumption. Therefore, one can conclude that either ignoring corner solutions and minimum consumption or ignor- ing goods consumption in time use models can lead to overestimation of the values of leisure and work times.
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Airport Choice in a Constraint World: Discrete Choice Models and Capacity Constraints

Airport Choice in a Constraint World: Discrete Choice Models and Capacity Constraints

But, at least over a short time horizon, air fares may not fully reflect the capacity situation at a given airport. Furthermore, some airport choice models (see e.g. Gelhausen 2007a, Innes and Doucet 1990, Moreno and Muller 2003, Ozoka and Ashford 1988, Windle and Dresner 1995) do not include ticket prices as an explanatory variable or employ proxy variables instead, either because exact differences in air ticket prices between different airports are less important for airport choice in an unconstrained airport environment, or they are not available to the researcher due to the survey design. Since air fares vary more across ticket categories at the same airport than a ticket category varies across different airports (Moreno and Muller 2003, p. 19), ticket price related information usually cannot be reconstructed fully by the researcher if it is not already included in the survey. Furthermore, for long-term aggregate airport choice forecasting purposes, it is difficult for the researcher to determine which tickets on which relations increase in price how much in the future, so that capacity constraints are met.
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