Framework for Skew-Probit Links in Binary Regression

We review several asymmetrical links for binary regression models and present a unified approach for two skew-probit links proposed in the literature. Moreover, under skew-probit link, conditions for the existence of the ML estimators and the posterior distribution under improper priors are established. The framework proposed here considers two sets of latent variables which are helpful to implement the Bayesian MCMC approach. A simulation study to criteria for models comparison is conducted and two applications are made. Using different Bayesian criteria we show that, for these data sets, the skew-probit links are better than alternative links proposed in the literature.

Desenvolvimento de metodologia de rating baseada no modelo ordered probit

Nos últimos anos o mercado de crédito brasileiro apresentou grande crescimento em termos de volume e modalidade de operações de crédito. Além disso, observou-se também o aumento da participação dos bancos nesse setor, principais intermediários financeiros da economia. Com isso, em um mercado em desenvolvimento, torna-se cada vez mais importante a correta avaliação e administração do risco financeiro envolvido nas operações: o risco de crédito. Nesse contexto, a classificação de rating surge como referência para investidores. No entanto, como o mercado bancário brasileiro ainda é pouco desenvolvido, apenas instituições de grande porte são classificados pelas agências de rating em funcionamento no país. Este trabalho tem como objetivo o desenvolvimento de uma metodologia de rating baseada no modelo ordered probit, que seja capaz de replicar o nível de rating de uma determinada agência, e assim conseguir estimar o nível de rating para aqueles bancos que não têm a referida classificação de rating

Intellectual property and competition as complementary policies: a test using an ordered probit model

Este Artigo Testa a Proposição da Teoria Econômica de que Propriedade Intelectual e Defesa da Concorrência são Políticas Complementares. um Modelo Probit Ordenado é Utilizado para Estimar os Efeitos Marginais do Uso e Qualidade do Enforcement dos Direitos de Propriedade Intelectual em uma Medida da Gravidade dos Problemas Relacionados À Concorrência. os Resultados Obtidos Reforçam a Noção de que as Políticas de Concorrência e Propriedade Intelectual não são Contraditórias.

An analysis of quit and dismissal determinants between 1988 and 1999 using the bivariate probit model

Excessive labor turnover may be considered, to a great extent, an undesirable feature of a given economy. This follows from considerations such as underinvestment in human capital by firms. Understanding the determinants and the evolution of turnover in a particular labor market is therefore of paramount importance, including policy considerations. The present paper proposes an econometric analysis of turnover in the Brazilian labor market, based on a partial observability bivariate probit model. This model considers the interdependence of decisions taken by workers and firms, helping to elucidate the causes that lead each of them to end an employment relationship. The Employment and Unemployment Survey (PED) conducted by the State System of Data Analysis (SEADE) and by the Inter-Union Department of Statistics and Socioeconomic Studies (DIEESE) provides data at the individual worker level, allowing for the estimation of the joint probabilities of decisions to quit or stay on the job on the worker’s side, and to maintain or fire the employee on the firm’s side, during a given time period. The estimated parameters relate these estimated probabilities to the characteristics of workers, job contracts, and to the potential macroeconomic determinants in different time periods. The results confirm the theoretical prediction that the probability of termination of an employment relationship tends to be smaller as the worker acquires specific skills. The results also show that the establishment of a formal employment relationship reduces the probability of a quit decision by the worker, and also the firm’s firing decision in non-industrial sectors. With regard to the evolution of quit probability over time, the results show that an increase in the unemployment rate inhibits quitting, although this tends to wane as the unemployment rate rises.

Modelling firm innovation using panel probit estimators

Firm-level innovation is investigated using three probit panel estimators, which control for unobserved heterogeneity, and a standard probit estimator. Results indicate the standard probit model is misspecified and that inter-firm networks are important for innovation.

Em Algorithm for MLE of a Probit Model for Multiple Ordinal Outcomes

The correlated probit model is frequently used for multiple ordered data since it allows to incorporate seamlessly different correlation structures. The estimation of the probit model parameters based on direct maximization of the limited information maximum likelihood is a numerically intensive procedure. We propose an extension of the EM algorithm for obtaining maximum likelihood estimates for a correlated probit model for multiple ordinal outcomes. The algorithm is implemented in the free software environment for statistical computing and graphics R. We present two simulation studies to examine the performance of the developed algorithm. We apply the model to data on 121 women with cervical or endometrial cancer. Patients developed normal tissue reactions as a result of post-operative external beam pelvic radiotherapy. In this work we focused on modeling the effects of a genetic factor on early skin and early urogenital tissue reactions and on assessing the strength of association between the two types of reactions. We established that there was an association between skin reactions and polymorphism XRCC3 codon 241 (C>T) (rs861539) and that skin and urogenital reactions were positively correlated. ACM Computing Classification System (1998): G.3.

Taller 9: Modelos Logit y Probit Econometría 06216

Taller 9: Modelos Logit y Probit Econometría 06216. Elaborado por el profesor Julio César Alonso Cifuentes de la Facultad de Ciencias Administrativas y Económicas – Universidad Icesi. Contiene preguntas fórmulas y respuestas.

Application of a Bivariate Probit Model to Investigate the Intended Evacuation from Hurricane

With evidence of increasing hurricane risks in Georgia Coastal Area (GCA) and Virginia in the U.S. Southeast and elsewhere, understanding intended evacuation behavior is becoming more and more important for community planners. My research investigates intended evacuation behavior due to hurricane risks, a behavioral survey of the six counties in GCA under the direction of two social scientists with extensive experience in survey research related to citizen and household response to emergencies and disasters. Respondents gave answers whether they would evacuate under both voluntary and mandatory evacuation orders. Bivariate probit models are used to investigate the subjective belief structure of whether or not the respondents are concerned about the hurricane, and the intended probability of evacuating as a function of risk perception, and a lot of demographic and socioeconomic variables (e.g., gender, military, age, length of residence, owning vehicles).

Comparative labor market performance of visaed and non-visaed migrants: Pacific islanders in Sydney

Using survey data for Tongan and Samoan migrants in Sydney the effects of visa restrictions on labor market performance of migrants are assessed. Univariate analysis suggests a positive association between unemployment and the unrestricted entry of Samoan step-migrants from New Zealand. A probit model of the determinants of unemployment is estimated with controls for human capital and demographic variables. While human capital endowments are important, visa restrictions do not have a significant effect on either group's employability. Implications for policy are discussed highlighting the complementarities between host country immigration policies and foreign aid programs.

Testing market efficiency: Evidence from the NFL sports betting market

This article examines the efficiency of the National Football League (NFL) betting market. The standard ordinary least squares (OLS) regression methodology is replaced by a probit model. This circumvents potential econometric problems, and allows us to implement more sophisticated betting strategies where bets are placed only when there is a relatively high probability of success. In-sample tests indicate that probit-based betting strategies generate statistically significant profits. Whereas the profitability of a number of these betting strategies is confirmed by out-of-sample testing, there is some inconsistency among the remaining out-of-sample predictions. Our results also suggest that widely documented inefficiencies in this market tend to dissipate over time.

Evaluation of liquid Bacillus thuringiensis var. israelensis products for control of Australian Aedes arbovirus vectors

Laboratory bioassay studies were conducted in southeast Queensland, Australia,: on the efficacy of Teknar (R), VectoBac (R) 12AS, and Cybate (R) (active ingredient: 1,200 international toxic units Bacillus thuringiensis var, israelensis [Bti]) against 3rd instars of the arbovirus vectors Aedes aegypti. Ae. notoscriptus, Ae. vigilax, and Ae. camptorhynchus. Probit analyses were then used to determine LD,, (median lethal dose), LD95, and lethal dose ratios (LDR). Aedes aegypti and Ae. notoscriptus, both container-habitat species, tolerated the highest Bti concentrations compared with saltmarsh Ae. vigilax and Ae. camptorhynchus. For example, the LDR for Ae. vigilax versus Ae. notoscriptus exposed to Cybate was 0.14 (95% confidence limit [CL] 0.03-0.61). Similarly, the Cybate LDR for Ae. camptorhynchus versus Ae. notoscriptus was 0.22 (95% CL 0.07-0.70). Teknar produced similar results with an LDR of 0.21 (95% CL 0.04-1.10) for Aedes vigilax versus Aedes notoscriptus. Differences in product efficacy were found when tested against the 2 container-breeding species. Cybate was less effective than Teknar with LDRs of 1.55 (95% CL 0.65-3.67) and 1.87 (95% CL 0.68-5.15) for Aedes aegypti and Ae. notoscriptus, respectively. The significant differences in susceptibility between mosquito species and varying efficacy between products highlight the importance of evaluating concentration-response data prior to contracting with distributors of mosquito control products. This information is crucial to resistance management strategies.

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.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Detecção do gerenciamento de resultados pela análise do diferimento tributário

O objetivo deste trabalho é verificar se a análise do diferimento tributário aumenta signifi cativamente a detecção do gerenciamento de resultados nas companhias abertas brasileiras. Desenvolveu-se uma pesquisa empírica nos setores de mineração, siderurgia e metalurgia, e têxtil, durante o período de 2000 a 2004, utilizando-se as regressões probit e logit baseadas nos modelos Jones Modificado e Kang e Sivaramakrishnan (KS). Este estudo apresenta evidências de que a análise do diferimento tributário não aumenta o poder preditivo dos modelos analisados. Essa divergência pode ser explicada pelo fato de que a maioria dos trabalhos realizados foi direcionada ao contexto norte-americano, que supostamente apresenta diferentes fatores motivadores para o gerenciamento de resultados. Além disso, as diferenças das evidências empíricas no âmbito brasileiro podem ser originadas pelo uso da flexibilidade do método parcial de reconhecimento do diferimento dos tributos.