Gender differentials in industry wage premia, affirmative action, and employment growth on the industry level

While most previous work has examined the gender wage gap by controlling for productivity-related factors, we focus on gender differences in industry wage premia (efficiency wages) on the detailed industry level and examine industry characteristics that might explain such differences. Using data from the 1988 CPS, we find that the extent to which firms in an industry were likely to be targeted for Affirmation Action compliance review, industry employment growth, and industry profitability have helped narrow the gender gap in industry wage premia. Other characteristics, such as capital intensity, mean education, and sales and payroll growth were much less important.     

Graphical modelling and the Mahalanobis distance

I consider the problem of estimating the Mahalanobis distance between multivariate normal populations when the population covariance matrix satisfies a graphical model. In addition to providing a clear understanding of the dependencies in a multivariate data set, the use of graphical models can reduce the variability of the estimated distances and improve inferences. I derive the asymptotic distribution of the estimated Mahalanobis distance under a general covariance model, which includes graphical models as a special case. Two examples are discussed.     

Simple graphical methods of displaying multiple clinical results

A common challenge in clinical research trials is for applied statistics to manage, analyse, summarize and report an enormous amount of data. Nowadays, due to advances in medical technology, situations frequently arise where it is difficult to display and interpret results. Consequently, a creative approach is required to summarize the main outcomes of the statistical analyses in a form which is easy to grasp, to interpret and possibly to remember. In this paper a number of clinical case studies are provided. Firstly, a topographical map of the brain summarizing P-values obtained from comparisons across different EEG sites; secondly, a bulls eye plot, showing the agreement between observers in different regions of the heart; thirdly, a pictorial table reporting inter- and intra-rater reliability scores of a speech assessment; fourthly a star-plot to deal with numerous questionnaire results and finally a correlogram to illustrate significant correlation values between two diagnostic tools. The intention of this paper is to encourage the effort of visual representations of multiple statistical outcomes. Such representations do not only embellish the report, but aid interpretation by conveying a specific statistical meaning.     

New HEAVY Models for Fat-Tailed Realized Covariances and Returns

ABSTRACT

We develop a new score-driven model for the joint dynamics of fat-tailed realized covariance matrix observations and daily returns. The score dynamics for the unobserved true covariance matrix are robust to outliers and incidental large observations in both types of data by assuming a matrix-F distribution for the realized covariance measures and a multivariate Student's t distribution for the daily returns. The filter for the unknown covariance matrix has a computationally efficient matrix formulation, which proves beneficial for estimation and simulation purposes. We formulate parameter restrictions for stationarity and positive definiteness. Our simulation study shows that the new model is able to deal with high-dimensional settings (50 or more) and captures unobserved volatility dynamics even if the model is misspecified. We provide an empirical application to daily equity returns and realized covariance matrices up to 30 dimensions. The model statistically and economically outperforms competing multivariate volatility models out-of-sample. Supplementary materials for this article are available online.     

Subjective Bayesian Analysis of the Elliptical Model

For the multivariate elliptical model subjective Bayesian estimators of the location vector and some functions of the characteristic matrix with the normal-inverse Wishart and the normal-Wishart as prior, respectively, are derived. Fang and Li (1999 Fang, K.T., Li, R.Z. (1999). Bayesian statistical inference on elliptical matrix distributions. J. Multivariate Anal. 70: 66–85.[Crossref], [Web of Science ®] , [Google Scholar]) considered the elliptical model for Bayesian analysis for an objective prior structure. In addition, the newly developed results are applied to the multivariate normal- and t-distribution. A performance study is done to evaluate the normal-gamma and normal-inverse gamma distributions as suitable priors. A practical application for the posterior distributions of the multivariate t-distribution is included by means of Gibbs sampling and a Metropolis-Hastings algorithm.     

Estimating the Cost of a Warranty

The cost of certain types of warranties is closely related to functions that arise in renewal theory. The problem of estimating the warranty cost for a random sample of size n can be reduced to estimating these functions. In an earlier paper, I gave several methods of estimating the expected number of renewals, called the renewal function. This answered an important accounting question of how to arrive at a good approximation of the expected warranty cost. In this article, estimation of the renewal function is reviewed and several extensions are given. In particular, a resampling estimator of the renewal function is introduced. Further, I argue that managers may wish to examine other summary measures of the warranty cost, in particular the variability. To estimate this variability, I introduce estimators, both parametric and nonparametric, of the variance associated with the number of renewals. Several numerical examples are provided.