Dynamic Bayesian beta models

da-Silva, Cibele Q.; Migon, Hélio S.; Correia, Leandro T.

doi:10.1016/j.csda.2010.12.011

Cited by 40 publications

(26 citation statements)

References 13 publications

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“…A Bayesian probabilistic model is comprised of a prior distribution to induce a posterior distribution, hyperparameters, and a likelihood function. A Bayesian sequential analysis of the dynamic Bayesian model can be used to reflect the latest trends of time-series data [15,16].…”

Section: Bayesian Probabilistic Modelmentioning

confidence: 99%

Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program

2018

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Cartels cause tremendous damage to the market economy and disadvantage consumers by creating higher prices and lower-quality goods; moreover, they are difficult to detect. We need to prevent them through scientific analysis, which includes the determination of an indicator to explain antitrust enforcement. In particular, the probability of cartel penalization is a useful indicator for evaluating competition enforcement. This study estimates the probability of cartel penalization using a Bayesian approach. In the empirical study, the probability of cartel penalization is estimated by a Bayesian approach from the cartel data of the Department of Justice in the United States between 1970 and 2009. The probability of cartel penalization is seen as sensitive to changes in competition law, and the results have implications for market efficiency and the antitrust authority's efforts against cartel formation and demise. The result of policy simulation shows the effectiveness of the leniency program. Antitrust enforcement is evaluated from the estimation results, and can therefore be improved.

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Section: Bayesian Probabilistic Modelmentioning

confidence: 99%

Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program

2018

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“…A Bayesian version of the static beta regression was proposed by Branscum et al (2007). More recently, Da-Silva et al (2011) proposed a method for beta time series data, in which the model parameters that are related to the means follow a dynamic model. However, the most frequently proposed use of the beta distribution in the context of regression has been restricted to cases where there is only one dependent variable.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Beta Regression with Application in Small Area Estimation

Souza

Moura²

2016

Journal of Official Statistics

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Multivariate beta regression models for jointly modelling two or more variables whose values belong in the (0,1) interval, such as indexes, rates or proportions, are proposed for making small area predictions. The multivariate model can help the estimation process by borrowing strength between units and obtaining more precise estimates, especially for small samples. Each response variable is assumed to have a beta distribution so the models could accommodate multivariate asymmetric data. Copula functions are used to construct the joint distribution of the dependent variables; all the marginal distributions are fixed as beta. A hierarchical beta regression model is additionally proposed with correlated random effects. We present an illustration of the proposed approach by estimating two indexes of educational attainment at school level in a Brazilian state. Our predictions are compared with separate univariate beta regressions. The inference process was conducted using a full Bayesian approach.

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“…O modelo sem variáveis exógenasé um caso particular do modelo apresentado, o qualé obtido fixando φ k = 0 , ∀ k = 1, 2, ... em (3-10) e (3)(4)(5)(6)(7)(8)(9)(10)(11).…”

Section: Modelo Gas Gama Com Evolução Do Tipo Sarimaunclassified

“…Dessa forma, a média condicional do processo será dada por λ t = exp(f t ), e a equação do vetor score ponderadoé dada pela Eq. (3)(4)(5)(6)(7)(8)(9)(10)(11). Nesta construção, a inclusão de variáveis exógenasé feita na equação de evolução do parâmetro variante no tempo f t , sendo possível separar o efeito da tendência, da sazonalidade e de variáveis externas ao modelo.…”

Section: Modelo Gas Gama De Componentes Não Observáveisunclassified

“…A linha preta correspondeà reta de regressão da série de Vazão pelo Fator de Capacidade. Os resultados apresentados nesta seção correspondem a modelos GAS (12,12) definidos pelas equações (3-10) - (3)(4)(5)(6)(7)(8)(9)(10)(11)(12). Os coeficientes A i e B j da equação de evolução dos parâmetros variantes no tempo foram escolhidos por tentativa e erro, de tal forma que os resíduos quantílicos fossem bem comportados, do ponto de vista dos correlogramas (FAC e FACP) e dos testes de autocorrelação serial nos resíduos e em seus quadrados.…”

Section: Análise Descritivaunclassified

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Modelos Gas Aplicados a Series Temporais De Vazao E Vento

MATOS¹

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The GAS models (generalized autoregressive score) are time series models with time-varying parameters, which have their update mechanism drived by the scaled score of the likelihood function. The likelihood evaluation in these models is quite simple, as well as the incorporation of effects like asymmetry, long memory and other dynamics. Because they are based in the scaled score of the likelihood, it exploits the full structure of the predictive distribution to the update mechanism of the parameters, and not just mean or higher order moments. These characteristics, coupled with the ability to handle with multivariate and non-stationary processes, make the studied class a new alternative to the construction of models with time-varying parameters, particularly for non-Gaussian time series. In this dissertation, univariate GAS models were developed to analyze monthly series of streamflow of Paraibuna river (MG) and of capacity factor of a wind farm undisclosed in Northeast, using the gamma and beta distributions, respectively. In addition, a new bivariate GAS model with gamma and beta marginals was derived for the joint modeling of the streamflow and wind processes, in order to explore the seasonal complementarity between the series.

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Dynamic Bayesian beta models

Cited by 40 publications

References 13 publications

Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program

Bayesian Approach for Estimating the Probability of Cartel Penalization under the Leniency Program

Multivariate Beta Regression with Application in Small Area Estimation

Modelos Gas Aplicados a Series Temporais De Vazao E Vento

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