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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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.

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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