In the microsimulation literature, it is still uncommon to test the statistical significance of results. In this paper we argue that this situation is both undesirable and unnecessary. Provided the parameters used in the microsimulation are exogenous, as is often the case in static...

Uncertainty in regression can be efficiently and effectively communicated using the visual properties of statistical objects in a regression display. Altering the "visual weight" of lines and shapes to depict the quality of information represented clearly communicates statistical confidence,...

Pearson's chi-square test is widely employed in social and health sciences to analyze categorical data and contingency tables. For the test to be valid, the sample size must be large enough to provide a minimum number of expected elements per category. This paper develops functions for...

In this paper it is proved that the Black-Scholes implied volatility satisfies a second order non-linear partial differential equation. The obtained PDE is then used to construct an algorithm for fast and accurate polynomial approximation for Black-Scholes implied volatility that improves on the...

Parameter shrinkage applied optimally can always reduce error and projection variances from those of maximum likelihood estimation. Many variables that actuaries use are on numerical scales, like age or year, which require parameters at each point. Rather than shrinking these towards zero,...

Theil (1968) proposed a transformation of regression residuals so that they are best (minimizes the trace of its covariance matrix), linear, unbiased and subject to the constraint that its covariance matrix is scalar (BLUS) in the sense that it is proportional to the identity matrix. Despite...

A standard test for weak instruments compares the first-stage F-statistic to a table of critical values obtained by Stock and Yogo (2005) using simulations. We derive a closed-form solution for the expectation from which these critical values are derived, as well as present some second-order...

The paper gives an overview on generalized linear models and its application in different branches of science. We introduce semiparametric extensions of the generalized linear model. One particular model of interest is the generalized partially linear model which allows a nonparametric modeling...

We propose a first order bias correction term for the Gini index to reduce the bias due to grouping. The first order correction term is obtained from studying the estimator of the Gini index within a measurement error framework. In addition, it reveals an intuitive formula for the remaining...