Modified logit life table system: Principles, empirical validation, and application

Despite its widespread use, the Coale-Demeny model life table system does not capture the extensive variation in age-specific mortality patterns observed in contemporary populations, particularly those of the countries of Eastern Europe and populations affected by HIV/AIDS. Although relational mortality models, such as the Brass logit system, can identify these variations, these models show systematic bias in their predictive ability as mortality levels depart from the standard. We propose a modification of the two-parameter Brass relational model. The modified model incorporates two additional age-specific correction factors (gamma(x), and theta(x)) based on mortality levels among children and adults, relative to the standard. Tests of predictive validity show deviations in age-specific mortality rates predicted by the proposed system to be 30-50 per cent lower than those predicted by the Coale-Demeny system and 15-40 per cent lower than those predicted using the original Brass system. The modified logit system is a two-parameter system, parameterized using values of l(5) and l(60).

Logit models for identifying the factors that influence the uptake of new 'no-tillage' technologies by farmers in the rice-wheat and the cotton-wheat farming systems of Pakistan's Punjab

In the 'rice-wheat' and the 'cotton-wheat' farming systems of Pakistan's Punjab, late planting of wheat is a perennial problem due to often delayed harvesting of the previously planted and late maturing rice and cotton crops. This leaves very limited time for land preparation for 'on-time' planting of wheat. 'No-tillage' technologies that reduce the turn-round time for wheat cultivation after rice and cotton have been developed, but their uptake has not been as expected.-This paper attempts to determine the farm and farmer characteristics and other socio-economic factors that influence the adoption of 'no-tillage' technologies'. Logit models were developed for the analysis undertaken. In the 'cotton-wheat' system personal characteristics like education, tenancy status, attitude towards risk implied in the use of new technologies and contact with extension agents are the main factors that affect adoption. As regards the 'rice-wheat' system, resource endowments such as farm size, access to a 'no-tillage' drill, clayey soils and the area sown to the rice-wheat sequence along with tenancy and contact with extension agents were dominant in explaining adoption. (C) 2002 Elsevier Science Ltd. All rights reserved.

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.

Generalized linear mixed models for the genetic evaluation of binary reproductive traits: a simulation study

The objective of this study was to evaluate the use of probit and logit link functions for the genetic evaluation of early pregnancy using simulated data. The following simulation/analysis structures were constructed: logit/logit, logit/probit, probit/logit, and probit/probit. The percentages of precocious females were 5, 10, 15, 20, 25 and 30% and were adjusted based on a change in the mean of the latent variable. The parametric heritability (h²) was 0.40. Simulation and genetic evaluation were implemented in the R software. Heritability estimates (ĥ²) were compared with h² using the mean squared error. Pearson correlations between predicted and true breeding values and the percentage of coincidence between true and predicted ranking, considering the 10% of bulls with the highest breeding values (TOP10) were calculated. The mean ĥ² values were under- and overestimated for all percentages of precocious females when logit/probit and probit/logit models used. In addition, the mean squared errors of these models were high when compared with those obtained with the probit/probit and logit/logit models. Considering ĥ², probit/probit and logit/logit were also superior to logit/probit and probit/logit, providing values close to the parametric heritability. Logit/probit and probit/logit presented low Pearson correlations, whereas the correlations obtained with probit/probit and logit/logit ranged from moderate to high. With respect to the TOP10 bulls, logit/probit and probit/logit presented much lower percentages than probit/probit and logit/logit. The genetic parameter estimates and predictions of breeding values of the animals obtained with the logit/logit and probit/probit models were similar. In contrast, the results obtained with probit/logit and logit/probit were not satisfactory. There is need to compare the estimation and prediction ability of logit and probit link functions.

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.

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. ^

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.

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.

A valuation of patients` willingness-to-pay for insulin delivery in diabetes

Objectives: The aim of this study was to determine the insulin-delivery system and the attributes of insulin therapy that best meet patients` preferences, and to estimate patients` willingness-to-pay (WTP) for them. Methods: This was a cross-sectional discrete choice experiment (DCE) study involving 378 Canadian patients with type 1 or type 2 diabetes. Patients were asked to choose between two hypothetical insulin treatment options made up of different combinations of the attribute levels. Regression coefficients derived using conditional logit models were used to calculate patients` WTP. Stratification of the sample was performed to evaluate WTP by predefined subgroups. Results: A total of 274 patients successfully completed the survey. Overall, patients were willing to pay the most for better blood glucose control followed by weight gain. Surprisingly, route of insulin administration was the least important attribute overall. Segmented models indicated that insulin naive diabetics were willing to pay significantly more for both oral and inhaled short-acting insulin compared with insulin users. Surprisingly, type 1 diabetics were willing to pay $C11.53 for subcutaneous short-acting insulin, while type 2 diabetics were willing to pay $C47.23 to avoid subcutaneous short-acting insulin (p < .05). These findings support the hypothesis of a psychological barrier to initiating insulin therapy, but once that this barrier has been overcome, they accommodate and accept injectable therapy as a treatment option. Conclusions: By understanding and addressing patients` preferences for insulin therapy, diabetes educators can use this information to find an optimal treatment approach for each individual patient, which may ultimately lead to improved control, through improved compliance, and better diabetes outcomes.

Predictors of non-response to a questionnaire survey of a volunteer twin panel: Findings from the Australian 1989 twin cohort

Questionnaire surveys, while more economical, typically achieve poorer response rates than interview surveys. We used data from a national volunteer cohort of young adult twins, who were scheduled for assessment by questionnaire in 1989 and by interview in 1996-2000, to identify predictors of questionnaire non-response. Out of a total of 8536 twins, 5058 completed the questionnaire survey (59% response rate), and 6255 completed a telephone interview survey conducted a decade later (73% response rate). Multinomial logit models were fitted to the interview data to identify socioeconomic, psychiatric and health behavior correlates of non-response in the earlier questionnaire survey. Male gender, education below University level, and being a dizygotic rather than monozygotic twin, all predicted reduced likelihood of participating in the questionnaire survey. Associations between questionnaire response status and psychiatric history and health behavior variables were modest, with history of alcohol dependence and childhood conduct disorder predicting decreased probability of returning a questionnaire, and history of smoking and heavy drinking more weakly associated with non-response. Body-mass index showed no association with questionnaire non-response. Despite a poor response rate to the self-report questionnaire survey, we found only limited sampling biases for most variables. While not appropriate for studies where socioeconomic variables are critical, it appears that survey by questionnaire, with questionnaire administration by telephone to non-responders, will represent a viable strategy for gene-mapping studies requiring that large numbers of relatives be screened.

Mobilidade geográfica de trabalhadores qualificados : principais evidências para o Brasil e o Espírito Santo

O objetivo desta dissertação, de forma geral, foi estimar empiricamente a probabilidade de imigração interestadual de trabalhadores qualificados para o Brasil. Consideraram-se tanto as variáveis relativas ao indivíduo quanto as variáveis relacionadas aos fatores regionais de origem e destino do imigrante e as análises foram feitas para os anos de 2001, 2006 e 2011. Para estimar os coeficientes das variáveis explicativas foram utilizados os modelos probit e logit. Os bancos de dados utilizados foram os microdados da PNAD e os principais resultados mostram que o principal polo de atração de trabalhadores qualificados é o estado de São Paulo. Em geral a probabilidade de migração de trabalhadores qualificados é maior para os indivíduos do sexo masculino, brancos e solteiros. Pessoas mais jovens e com maiores salários também são mais propensas a serem imigrantes qualificados.

Remuneração executiva e desempenho: evidências empíricas no mercado de capitais brasileiro

O objetivo desta dissertação é analisar a relação existente entre remuneração executiva e desempenho em companhias brasileiras de capital aberto listadas na BM&FBOVESPA. A linha teórica parte do pressuposto que o contrato de incentivos corrobora com o alinhamento de interesses entre acionistas e executivos e atua como um mecanismo de governança corporativa a fim de direcionar os esforços dos executivos para maximização de valor da companhia. A amostra foi composta pelas 100 companhias mais líquidas listadas em quantidade de negociações de ações na BM&FBOVESPA durante o período 2010-2012, totalizando 296 observações. Os dados foram extraídos dos Formulários de Referência disponibilizados pela CVM e a partir dos softwares Economática® e Thomson Reuters ®. Foram estabelecidas oito hipóteses de pesquisa e estimados modelos de regressão linear múltipla com a técnica de dados em painel desbalanceado, empregando como variável dependente a remuneração total e a remuneração média individual e como regressores variáveis concernentes ao desempenho operacional, valor de mercado, tamanho, estrutura de propriedade, governança corporativa, além de variáveis de controle. Para verificar os fatores que explicam a utilização de stock options, programa de bônus e maior percentual de remuneração variável foram estimados modelos de regressão logit. Os resultados demonstram que, na amostra selecionada, existe relação positiva entre remuneração executiva e valor de mercado. Verificou-se também que os setores de mineração, química, petróleo e gás exercem influência positiva na remuneração executiva. Não obstante, exerce relação inversa com a remuneração total à concentração acionária, o controle acionário público e o fato da companhia pertencer ao nível 2 ou novo mercado conforme classificação da BMF&BOVESPA. O maior valor de mercado influencia na utilização de stock options, assim como no emprego de bônus, sendo que este também é impactado pelo maior desempenho contábil. Foram empregados também testes de robustez com estimações por efeitos aleatórios, regressões com erros-padrão robustos clusterizados, modelos dinâmicos e os resultados foram similares. Conclui-se que a remuneração executiva está relacionada com o valor corporativo gerando riqueza aos acionistas, mas que a ausência de relação com o desempenho operacional sugere falhas no sistema remuneratório que ainda depende de maior transparência e outros mecanismos de governança para alinhar os interesses entre executivos e acionistas.

Transações com partes relacionadas : determinantes e impactos no desempenho das empresas

A literatura internacional que analisa os fatores impactantes das transações com partes relacionadas concentra-se no Reino Unido, nos EUA e no continente asiático, sendo o Brasil um ambiente pouco investigado. Esta pesquisa tem por objetivo investigar tanto os fatores impactantes dos contratos com partes relacionadas, quanto o impacto dessas transações no desempenho das empresas brasileiras. Estudos recentes que investigaram as determinantes das transações com partes relacionadas (TPRs), assim como seus impactos no desempenho das empresas, levaram em consideração as vertentes apresentadas por Gordon, Henry e Palia (2004): (a) de conflitos de interesses, as quais apoiam a visão de que as TPRs são danosas para os acionistas minoritários, implicando expropriação da riqueza deles, por parte dos controladores (acionistas majoritários); e (b) transações eficientes que podem ser benéficas às empresas, atendendo, desse modo, aos objetivos econômicos subjacentes delas. Esta pesquisa apoia-se na vertente de conflito de interesses, com base na teoria da agência e no fato de que o cenário brasileiro apresenta ter como característica uma estrutura de propriedade concentrada e ser um país emergente com ambiente legal caracterizado pela baixa proteção aos acionistas minoritários. Para operacionalizar a pesquisa, utilizou-se uma amostra inicial composta de 70 empresas com ações listadas na BM&FBovespa, observando o período de 2010 a 2012. Os contratos relacionados foram identificados e quantificados de duas formas, de acordo com a metodologia aplicada por Kohlbeck e Mayhew (2004; 2010) e Silveira, Prado e Sasso (2009). Como principais determinantes foram investigadas proxies para captar os efeitos dos mecanismos de governança corporativa e ambiente legal, do desempenho das empresas, dos desvios entre direitos sobre controle e direitos sobre fluxo de caixa e do excesso de remuneração executiva. Também foram adicionadas variáveis de controle para isolar as características intrínsecas das firmas. Nas análises econométricas foram estimados os modelos pelos métodos de Poisson, corte transversal agrupado (Pooled-OLS) e logit. A estimação foi feita pelo método dos mínimos quadrados ordinários (MQO), e para aumentar a robustez das estimativas econométricas, foram utilizadas variáveis instrumentais estimadas pelo método dos momentos generalizados (MMG). As evidências indicam que os fatores investigados impactam diferentemente as diversas medidas de TPRs das empresas analisadas. Verificou-se que os contratos relacionados, em geral, são danosos às empresas, impactando negativamente o desempenho delas, desempenho este que é aumentado pela presença de mecanismos eficazes de governança corporativa. Os resultados do impacto das medidas de governança corporativa e das características intrínsecas das firmas no desempenho das empresas são robustos à presença de endogeneidade com base nas regressões com variáveis instrumentais.

A produção de educação nos Açores: a nota de matemática

Tese de Doutoramento, Ciências Económicas e Empresariais (Desenvolvimento Económico e Social e Economia Pública), 16 de Janeiro de 2014, Universidade dos Açores.