引入信息成本的信息结构与股权融资成本

本文放松了Easley和O’Hara信息成本为0的假设,在他们的信息结构模型的理论框架下,构建了一个引入信息成本因素的信息结构模型。从信息结构的四个方面：信息成本、信息风险、信息披露的质量和先验信息质量研究了信息结构与股权融资成本之间的关系,得出了四个推论,从而拓展了信息结构模型。在进一步的实证研究中,选取市场微观结构理论中的逆向选择成本、知情交易概率-PIN分别作为信息成本和信息风险的衡量指标,研究发现：信息成本与股权融资成本之间呈倒‘U’型曲线关系;信息风险越高的股票股权融资成本越高;信息披露质量越高的公司,股权融资成本越低;先验信息质量越高,股权融资成本越低;从而对推论进行了有效验证。本文与Easley和O’Hara最大不同在于引入了信息成本因素,并且用实证方法对推论进行了验证,具有一定的开创性。     

Risk Perception, Federal Spending, and the Savannah River Site: Attitudes of Hunters and Fishermen

This paper examines the attitudes of 285 hunters and fishermen from South Carolina about hunting and fishing, risk, environmental issues, and future land use of the Savannah River Site. We test the null hypothesis that there is no difference in hunting and fishing rates, attitudes toward the safety of fish and deer obtained from SRS, attitudes toward future land use at SRS, and perceptions of the severity of environmental problems as a function of how far respondents lived from the site. Respondents hunted or fished an average of over 40 days a year, and only half felt that the fish and deer from SRS were safe to eat. Willingness to expend federal funds was correlated with perceptions of the severity of the problem. Preferences for future land use at SRS fell into three categories: high (environmental research park, hunting, fishing, camping), medium (nuclear production, factories, preserve only), and low (nuclear waste storage, residential). There were no differences in hunting and fishing rates, ranking of the severity of environmental problems, and willingness to expend federal funds as a function of distance of residence from SRS, but attitudes toward future land use differed significantly as a function of location of residence. Those living close to SRS were more willing to have the site used for factories, residential, nuclear material production and to store nuclear wastes than those living farther from the site. Our data on recreational rates, attitudes toward future land use, and willingness to expend federal funds to solve environmental problems reiterate the importance of assessing stakeholder attitudes toward decisions regarding future land use at DOE sites     

On designing an acceptance sampling plan for the Pareto lifetime model

In this paper, the design of reliability sampling plans for the Pareto lifetime model under progressive Type-II right censoring is considered. Sampling plans are derived using the decision theoretic approach with a suitable loss or cost function that consists of sampling cost, rejection cost, and acceptance cost. The decision rule is based on the estimated reliability function. Plans are constructed within the Bayesian context using the natural conjugate prior. Simulations for evaluating the Bayes risk are carried out and the optimal sampling plans are reported for various sample sizes, observed number of failures and removal probabilities.     

Bayesian inference for the randomly censored Weibull distribution

In this paper, we consider the Bayesian inference of the unknown parameters of the randomly censored Weibull distribution. A joint conjugate prior on the model parameters does not exist; we assume that the parameters have independent gamma priors. Since closed-form expressions for the Bayes estimators cannot be obtained, we use Lindley's approximation, importance sampling and Gibbs sampling techniques to obtain the approximate Bayes estimates and the corresponding credible intervals. A simulation study is performed to observe the behaviour of the proposed estimators. A real data analysis is presented for illustrative purposes.     

Bayesian composite quantile regression

One advantage of quantile regression, relative to the ordinary least-square (OLS) regression, is that the quantile regression estimates are more robust against outliers and non-normal errors in the response measurements. However, the relative efficiency of the quantile regression estimator with respect to the OLS estimator can be arbitrarily small. To overcome this problem, composite quantile regression methods have been proposed in the literature which are resistant to heavy-tailed errors or outliers in the response and at the same time are more efficient than the traditional single quantile-based quantile regression method. This paper studies the composite quantile regression from a Bayesian perspective. The advantage of the Bayesian hierarchical framework is that the weight of each component in the composite model can be treated as open parameter and automatically estimated through Markov chain Monte Carlo sampling procedure. Moreover, the lasso regularization can be naturally incorporated into the model to perform variable selection. The performance of the proposed method over the single quantile-based method was demonstrated via extensive simulations and real data analysis.     

Survival in the U.S. petroleum refining industry

Of the 324 petroleum refineries operating in the U.S. in 1982, only 149 were still in the hands of their original owners in 2007. Using duration analysis, this paper explores why refineries change ownership or shut down. Plants are more likely to ‘survive’ with their original owners if they are older or larger, but less likely if the owner is a major integrated firm, or the refinery is a more technologically complex one. This latter result differs from existing research on the issue. This paper also presents a split population model to relax the general assumption of the duration model that all refiners will eventually close down; the empirical results show that the split population model converges on a standard hazard model; the log-logistic version fits best. Finally, a multinomial logit model is estimated to analyze the factors that influence the refinery plant's choices of staying open, closing, or changing ownership. Plant size, age and technology usage have positive impacts on the likelihood that a refinery will stay open, or change ownership (rather than close down).     

Bayesian predictive densities based on latent information priors

Construction methods for prior densities are investigated from a predictive viewpoint. Predictive densities for future observables are constructed by using observed data. The simultaneous distribution of future observables and observed data is assumed to belong to a parametric submodel of a multinomial model. Future observables and data are possibly dependent. The discrepancy of a predictive density to the true conditional density of future observables given observed data is evaluated by the Kullback-Leibler divergence. It is proved that limits of Bayesian predictive densities form an essentially complete class. Latent information priors are defined as priors maximizing the conditional mutual information between the parameter and the future observables given the observed data. Minimax predictive densities are constructed as limits of Bayesian predictive densities based on prior sequences converging to the latent information priors.     

Exact and Monte Carlo calculations of integrated likelihoods for the latent class model

The latent class model or multivariate multinomial mixture is a powerful approach for clustering categorical data. It uses a conditional independence assumption given the latent class to which a statistical unit is belonging. In this paper, we exploit the fact that a fully Bayesian analysis with Jeffreys non-informative prior distributions does not involve technical difficulty to propose an exact expression of the integrated complete-data likelihood, which is known as being a meaningful model selection criterion in a clustering perspective. Similarly, a Monte Carlo approximation of the integrated observed-data likelihood can be obtained in two steps: an exact integration over the parameters is followed by an approximation of the sum over all possible partitions through an importance sampling strategy. Then, the exact and the approximate criteria experimentally compete, respectively, with their standard asymptotic BIC approximations for choosing the number of mixture components. Numerical experiments on simulated data and a biological example highlight that asymptotic criteria are usually dramatically more conservative than the non-asymptotic presented criteria, not only for moderate sample sizes as expected but also for quite large sample sizes. This research highlights that asymptotic standard criteria could often fail to select some interesting structures present in the data.     

Estimation of the intercept parameter for linear regression model with uncertain non-sample prior information

This paper considers alternative estimators of the intercept parameter of the linear regression model with normal error when uncertain non-sample prior information about the value of the slope parameter is available. The maximum likelihood, restricted, preliminary test and shrinkage estimators are considered. Based on their quadratic biases and mean square errors the relative performances of the estimators are investigated. Both analytical and graphical comparisons are explored. None of the estimators is found to be uniformly dominating the others. However, if the non-sample prior information regarding the value of the slope is not too far from its true value, the shrinkage estimator of the intercept parameter dominates the rest of the estimators.     

Microdata Access and Labor Market Research: The U. S. Experience

Summary: The paper illustrates the value of broad researcher access to survey and administrative microdata using examples drawn from the U. S. experience, outlining how analyses of trends in earnings inequality, poverty and employment dynamics using such data have benefited policy makers and contributed to improvements in statistical agency data products. Methods of facilitating researcher access, including the release of public use files, the use of licensing agreements, and the establishment of research data centers, are discussed. * The author thanks Anne Polivka, Marilyn Seastrom and two anonymous referees for helpful comments on an earlier draft of the paper.