O papel da educação financeira na contratação de crédito em mercados informais

O artigo examina os determinantes para o uso de fontes informais de crédito com uma base de dados com 2.023 observações. São analisados os financiamentos contratados através de amigos/conhecidos, com ou sem a cobrança de juros, e via agiotas. Um logit multinomial é usado para comparar os efeitos da tomada de crédito na informalidade com as famílias que não contrataram nenhum tipo de crédito e com as que utilizaram crédito bancário. Os títulos de capitalização são usados como proxy para identificar aqueles com baixos níveis de educação financeira. Os resultados indicam que além da questão da restrição de crédito, o analfabetismo financeiro desempenha um papel significante na seleção desse tipo de fonte de financiamento.

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

I introduce the new mgof command to compute distributional tests for discrete (categorical, multinomial) variables. The command supports largesample tests for complex survey designs and exact tests for small samples as well as classic large-sample x2-approximation tests based on Pearson’s X2, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read, 1984, Journal of the Royal Statistical Society, Series B (Methodological) 46: 440–464). The complex survey correction is based on the approach by Rao and Scott (1981, Journal of the American Statistical Association 76: 221–230) and parallels the survey design correction used for independence tests in svy: tabulate. mgof computes the exact tests by using Monte Carlo methods or exhaustive enumeration. mgof also provides an exact one-sample Kolmogorov–Smirnov test for discrete data.

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.

Logistic regression models for ordinal response: A study of self-efficacy in colorectal cancer screening

The ordinal logistic regression models are used to analyze the dependant variable with multiple outcomes that can be ranked, but have been underutilized. In this study, we describe four logistic regression models for analyzing the ordinal response variable. ^ In this methodological study, the four regression models are proposed. The first model uses the multinomial logistic model. The second is adjacent-category logit model. The third is the proportional odds model and the fourth model is the continuation-ratio model. We illustrate and compare the fit of these models using data from the survey designed by the University of Texas, School of Public Health research project PCCaSO (Promoting Colon Cancer Screening in people 50 and Over), to study the patient’s confidence in the completion colorectal cancer screening (CRCS). ^ The purpose of this study is two fold: first, to provide a synthesized review of models for analyzing data with ordinal response, and second, to evaluate their usefulness in epidemiological research, with particular emphasis on model formulation, interpretation of model coefficients, and their implications. Four ordinal logistic models that are used in this study include (1) Multinomial logistic model, (2) Adjacent-category logistic model [9], (3) Continuation-ratio logistic model [10], (4) Proportional logistic model [11]. We recommend that the analyst performs (1) goodness-of-fit tests, (2) sensitivity analysis by fitting and comparing different models.^

Prospective run theory for multinomial outcomes

The focus of this study was to generalize the theory of runs to multinomial outcomes using the generating function approach. Detailed discussion is provided for determining the probability distributions for all runs of length i in a sequence of n trials for the binomial and trinomial cases. The generalization to multinomial case is also presented. Application to data for patients from a long term disability care facility is presented to illustrate the use of Run Theory in determining the probability of a dominant state of treatment associated with a patient during his/her hospitalization. ^

Survey on economic assessment of the improvement of the quality of environment: the case of the fight against floods in Cotonou (Benin Living Standard Survey)

This study aims to analyze households' attitude toward flood risk in Cotonou in the sense to identify whether they are willing or not to leave the flood-prone zones. Moreover, the attitudes toward the management of wastes and dirty water are analyzed. The data used in this study were obtained from two sources: the survey implemented during March 2011 on one hundred and fifty randomly selected households living in flood-prone areas of Cotonou, and Benin Living Standard Survey of 2006 (Part relative to Cotonou on 1,586 households). Moreover, climate data were used in this study. Multinomial probability model is used for the econometric analysis of the attitude toward flood risk. While the attitudes toward the management of wastes and dirty water are analyzed through a simple logit. The results show that 55.3% of households agreed to go elsewhere while 44.7% refused [we are better-off here (10.67%), due to the proximity of the activities (19.33), the best way is to build infrastructures that will protect against flood and family house (14.67%)]. The authorities have to rethink an alternative policy to what they have been doing such as building socio-economic houses outside Cotonou and propose to the households that are living the areas prone to inundation. Moreover, access to formal education has to be reinforced.

Las nuevas medidas agroenergéticas mexicanas: un modelo microeconométrico para la evaluación de su impacto sobre los ecosistemas naturales en los estados de Hidalgo, Querétaro y Tamaulipas (México)

La ley para la Promoción y Desarrollo de Biocombustibles aprobada en México en 2007 permite la producción de bioetanol y biodiesel. Esta producción puede entrar en conflicto con la producción de alimentos y con los ecosistemas naturales y en esta tesis se desarrolla un modelo microeconométrico que puede servir de base para anticiparse a esos conflictos y para diseñar medidas de política agraria orientadas a potenciar la compatibilidad de la producción de biocombustibles con la de alimentos y con la conservación de los ecosistemas naturales. A partir de una muestra de explotaciones de tres Estados de México – Hidalgo, Querétaro y Tamaulipas- y de un modelo logit multinomial mixto, se estima la elasticidad de la superficie destinada a cultivos alimentarios respecto a cambios en los márgenes económicos de los cultivos agroenergéticos. Esa elasticidad resulta ser significativa. Mostramos que su estimación es útil para anticipar cambios en la superficie destinada a los cultivos alimentarios y a los forestales. Se evalúa el impacto de varios escenarios relativos a los márgenes brutos de los cultivos sobre las decisiones de los agricultores y se muestra la utilidad del modelo para detectar tendencias de cambio a largo plazo en la alternativa de cultivos, incluyendo los forestales. ABSTRACT The Law for the Promotion and Development of Biofuels in Mexico adopted in 2007 allows for the production of bioethanol and biodiesel. This production may conflict with food production and natural ecosystems and this thesis develops a microeconometric model that can serve as a basis to anticipate such conflicts and to implement agricultural policy measures designed to enhance the compatibility of biofuels with production food and natural ecosystems conservation. We estimate the elasticity of the area devoted to food crops with respect to changes in economic margins of energy crops, using a sample of farms in three states of Mexico - Hidalgo, Queretaro and Tamaulipas - , and a multinomial mixed logit model. We found that this elasticity is significant. And we show how it can be useful to anticipate changes in area under food crops and forests. The impact of various scenarios about gross margins on farmers' decisions is assessed and it is shown the usefulness of the model to detect trends of long-term change in the crops area, including forests.

Reemigrar en España: una aproximación a sus determinantes

En este trabajo se investiga sobre las razones que inducen a los migrantes a realizar migraciones múltiples. Tras el análisis descriptivo de las características de estos inmigrantes, se especifica un logit binomial y un probit multinomial en los que la probabilidad de volver a emigrar depende de las características personales y de las cadenas migratorias de orígenes y destinos. La información utilizada procede de los microdatos de la Estadística de Variaciones Residenciales. Los resultados indican que las migraciones repetidas de españoles y extranjeros responden a motivos diferentes. El análisis sugiere que las reemigraciones de los extranjeros obedecerían a los resultados de sus experiencias laborales, mientras que en las de los españoles parecen hallarse otros motivos adicionales.

MGOF: Stata module to perform goodness-of-fit tests for multinomial data

mgof computes goodness-of-fit tests for the distribution of a discrete (categorical, multinomial) variable. The default is to perform classical large sample chi-squared approximation tests based on Pearson's X2 statistic and the log likelihood ratio (G2) statistic or a statistic from the Cressie-Read family. Alternatively, mgof computes exact tests using Monte Carlo methods or exhaustive enumeration. A Kolmogorov-Smirnov test for discrete data is also provided. The moremata package, also available from SSC, is required.

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

A new Stata command called -mgof- is introduced. The command is used to compute distributional tests for discrete (categorical, multinomial) variables. Apart from classic large sample $\chi^2$-approximation tests based on Pearson's $X^2$, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read 1984), large sample tests for complex survey designs and exact tests for small samples are supported. The complex survey correction is based on the approach by Rao and Scott (1981) and parallels the survey design correction used for independence tests in -svy:tabulate-. The exact tests are computed using Monte Carlo methods or exhaustive enumeration. An exact Kolmogorov-Smirnov test for discrete data is also provided.

A diszkrét kiválasztási modell becslése Cox-regresszióval (Parameter estimation for multinomial discrete choice model using Cox-regression)

Considering the so-called "multinomial discrete choice" model the focus of this paper is on the estimation problem of the parameters. Especially, the basic question arises how to carry out the point and interval estimation of the parameters when the model is mixed i.e. includes both individual and choice-specific explanatory variables while a standard MDC computer program is not available for use. The basic idea behind the solution is the use of the Cox-proportional hazards method of survival analysis which is available in any standard statistical package and provided a data structure satisfying certain special requirements it yields the MDC solutions desired. The paper describes the features of the data set to be analysed.

A Bayesian Dirichlet-Multinomial Test for Cross-Group Differences

Testing for differences within data sets is an important issue across various applications. Our work is primarily motivated by the analysis of microbiomial composition, which has been increasingly relevant and important with the rise of DNA sequencing. We first review classical frequentist tests that are commonly used in tackling such problems. We then propose a Bayesian Dirichlet-multinomial framework for modeling the metagenomic data and for testing underlying differences between the samples. A parametric Dirichlet-multinomial model uses an intuitive hierarchical structure that allows for flexibility in characterizing both the within-group variation and the cross-group difference and provides very interpretable parameters. A computational method for evaluating the marginal likelihoods under the null and alternative hypotheses is also given. Through simulations, we show that our Bayesian model performs competitively against frequentist counterparts. We illustrate the method through analyzing metagenomic applications using the Human Microbiome Project data.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.