Mixed Logit Model Performance and Distributional Assumptions: Preferences and GM foods

Using mixed logit models to analyse choice data is common but requires ex ante specification of the functional forms of preference distributions. We make the case for greater use of bounded functional forms and propose the use of the Marginal Likelihood, calculated using Bayesian techniques, as a single measure of model performance across non nested mixed logit specifications. Using this measure leads to very different rankings of model specifications compared to alternative rule of thumb measures. The approach is illustrated using data from a choice experiment regarding GM food types which provides insights regarding the recent WTO dispute between the EU and the US, Canada and Argentina and whether labelling and trade regimes should be based on the production process or product composition.

Consistent estimation of the fixed effects ordered logit model

The paper considers panel data methods for estimating ordered logit models with individual-specific correlated unobserved heterogeneity. We show that a popular approach is inconsistent, whereas some consistent and efficient estimators are available, including minimum distance and generalized method-of-moment estimators. A Monte Carlo study reveals the good properties of an alternative estimator that has not been considered in econometric applications before, is simple to implement and almost as efficient. An illustrative application based on data from the German Socio-Economic Panel confirms the large negative effect of unemployment on life satisfaction that has been found in the previous literature.

Identification and estimation of thresholds in the fixed effects ordered logit model

This paper proposes a new estimator for the fixed effects ordered logit model. In contrast to existing methods, the new procedure allows estimating the thresholds. The empirical relevance and simplicity of implementation is illustrated in an application on the effect of unemployment on life satisfaction.

A Bayesian approach to estimating the regression coefficients of a multinomial logit model

A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ordinal scale response categories is presented. A Monte Carlo method is used to construct the posterior distribution of the link function. The link function is treated as an arbitrary scalar function. Then the Gauss-Markov theorem is used to determine a function of the link which produces a random vector of coefficients. The posterior distribution of the random vector of coefficients is used to estimate the regression coefficients. The method described is referred to as a Bayesian generalized least square (BGLS) analysis. Two cases involving multinominal logit models are described. Case I involves a cumulative logit model and Case II involves a proportional-odds model. All inferences about the coefficients for both cases are described in terms of the posterior distribution of the regression coefficients. The results from the BGLS method are compared to maximum likelihood estimates of the regression coefficients. The BGLS method avoids the nonlinear problems encountered when estimating the regression coefficients of a generalized linear model. The method is not complex or computationally intensive. The BGLS method offers several advantages over Bayesian approaches. ^

A mixed logit model for predicting exit choice during building evacuations.

Knowledge on human behaviour in emergency is crucial to increase the safety of buildings and transportation systems. Decision making during evacuations implies different choices, of which one of the most important concerns the escape route. The choice of a route may involve local decisions between alternative exits from an enclosed environment. This work investigates the influence of environmental (presence of smoke, emergency lighting and distance of exit) and social factors (interaction with evacuees close to the exits and with those near the decision-maker) on local exit choice. This goal is pursued using an online stated preference survey carried out making use of non-immersive virtual reality. A sample of 1,503 participants is obtained and a Mixed Logit Model is calibrated using these data. The model shows that presence of smoke, emergency lighting, distance of exit, number of evacuees near the exits and the decision-maker, and flow of evacuees through the exits significantly affect local exit choice. Moreover, the model points out that decision making is affected by a high degree of behavioural uncertainty. Our findings support the improvement of evacuation models and the accuracy of their results, which can assist in designing and managing building and transportation systems. The main contribution of this work is to enrich the understanding of how local exit choices are made and how behavioural uncertainty affects these choices.

Health care provider choice in the case of patient-initiated contacts. An extended version of discrete choice of model demand

This paper analyzes the nature of health care provider choice inthe case of patient-initiated contacts, with special reference toa National Health Service setting, where monetary prices are zeroand general practitioners act as gatekeepers to publicly financedspecialized care. We focus our attention on the factors that mayexplain the continuously increasing use of hospital emergencyvisits as opposed to other provider alternatives. An extendedversion of a discrete choice model of demand for patient-initiatedcontacts is presented, allowing for individual and town residencesize differences in perceived quality (preferences) betweenalternative providers and including travel and waiting time asnon-monetary costs. Results of a nested multinomial logit model ofprovider choice are presented. Individual choice betweenalternatives considers, in a repeated nested structure, self-care,primary care, hospital and clinic emergency services. Welfareimplications and income effects are analyzed by computingcompensating variations, and by simulating the effects of userfees by levels of income. Results indicate that compensatingvariation per visit is higher than the direct marginal cost ofemergency visits, and consequently, emergency visits do not appearas an inefficient alternative even for non-urgent conditions.

Revenue management under general discrete choice model of consumer behavior

Customer choice behavior, such as 'buy-up' and 'buy-down', is an importantphe-nomenon in a wide range of industries. Yet there are few models ormethodologies available to exploit this phenomenon within yield managementsystems. We make some progress on filling this void. Specifically, wedevelop a model of yield management in which the buyers' behavior ismodeled explicitly using a multi-nomial logit model of demand. Thecontrol problem is to decide which subset of fare classes to offer ateach point in time. The set of open fare classes then affects the purchaseprobabilities for each class. We formulate a dynamic program todetermine the optimal control policy and show that it reduces to a dynamicnested allocation policy. Thus, the optimal choice-based policy caneasily be implemented in reservation systems that use nested allocationcontrols. We also develop an estimation procedure for our model based onthe expectation-maximization (EM) method that jointly estimates arrivalrates and choice model parameters when no-purchase outcomes areunobservable. Numerical results show that this combined optimization-estimation approach may significantly improve revenue performancerelative to traditional leg-based models that do not account for choicebehavior.

A finite-population revenue management model and a risk-ratio procedure for the joint estimation of population size and parameters

Many dynamic revenue management models divide the sale period into a finite number of periods T and assume, invoking a fine-enough grid of time, that each period sees at most one booking request. These Poisson-type assumptions restrict the variability of the demand in the model, but researchers and practitioners were willing to overlook this for the benefit of tractability of the models. In this paper, we criticize this model from another angle. Estimating the discrete finite-period model poses problems of indeterminacy and non-robustness: Arbitrarily fixing T leads to arbitrary control values and on the other hand estimating T from data adds an additional layer of indeterminacy. To counter this, we first propose an alternate finite-population model that avoids this problem of fixing T and allows a wider range of demand distributions, while retaining the useful marginal-value properties of the finite-period model. The finite-population model still requires jointly estimating market size and the parameters of the customer purchase model without observing no-purchases. Estimation of market-size when no-purchases are unobservable has rarely been attempted in the marketing or revenue management literature. Indeed, we point out that it is akin to the classical statistical problem of estimating the parameters of a binomial distribution with unknown population size and success probability, and hence likely to be challenging. However, when the purchase probabilities are given by a functional form such as a multinomial-logit model, we propose an estimation heuristic that exploits the specification of the functional form, the variety of the offer sets in a typical RM setting, and qualitative knowledge of arrival rates. Finally we perform simulations to show that the estimator is very promising in obtaining unbiased estimates of population size and the model parameters.

Using a Bayesian model for bankruptcy prediction : a comparative approach

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors' concerns with respect to assessing failure risk.

Revisiting a destination: A tourist’s choice model : A case study of Sweden

This paper documents the design and results of a study on tourists’ decision-making about destinations in Sweden. For this purpose, secondary data, available from surveys were used to identify which type of individual has the highest probability to revisit a destination and what are influencing factors to do so. A binary logit model is applied. The results show that very important influencing factors are the length of stay as well as the origin of the individual. These results could be useful for a marketing organization as well as for policy, to develop strategies to attract the most profitable tourism segment. Therefore, it can also be a great support for sustainable tourism development, where the main focus does not has priority on increasing number of tourists.

Determining the season of death from the family composition of insects infesting carrion

Determining the season of death by means of the composition of the families of insects infesting carrion is rarely attempted in forensic studies and has never been statistically modelled. For this reason, a baseline-category logit model is proposed for predicting the season of death as a function of whether the area where the carcass was exposed is sunlit or shaded and of the relative abundance of particular families of carrion insects (Calliphoridae, Fanniidae, Sarcophagidae, and Formicidae). The field study was conducted using rodent carcasses (20-252 g) in an urban forest in southeastern Brazil. Four carcasses (2 in a sunlit and 2 in a shaded area) were placed simultaneously at the study site, twice during each season from August 2003 through June 2004. The feasibility of the model, measured in terms of overall accuracy, is 64 +/- 14%. It is likely the proposed model will assist forensic teams in predicting the season of death in tropical ecosystems, without the need of identifying the species of specimens or the remains of carrion insects.

The host-tourist interaction in a world heritage site: the case of Guimarães

Guimarães, in the northwest of Portugal, is a city of strong symbolic and cultural significance and its nomination by UNESCO as world heritage, in 2001, enlarged its tourism potential. In this paper we present a few results of a survey that envisaged capturing the Guimarães residents’ perceptions of tourism impacts and their attitudes towards tourists. Specifically, one analyzes the type of relationship that exists between some socio-demographic groups and the perceived tourism impacts, as well as their socio-characteristics and the existing level of interaction between residents and tourists. The survey was implemented between January and March 2010 to a convenience sample of 540 inhabitants of the municipality of Guimarães resulting in 400 questionnaires with complete data. For this, we made use of various statistical techniques. Using a factorial analysis, we can conclude that the three factors used explain 52.3% of the variance contained in the original variables obtained from the survey. By another side, using a logit model in the analysis and taking as the dependent variable the frequent or very frequent contact with tourists, we found that only the variables referred to perceived positive impacts of tourism, education and the place of residence in urban areas have shown to be statistically significant. We are aware of the multiple ways the issue of residents’ perceptions and attitudes towards tourism can be approached and of the difficulties to get useful policy-oriented insights. This paper is a step in that trail.

Estudo empírico sobre insolvências no setor de alojamento e restauração

Mestrado em Contabilidade e Análise Financeira