The Risk of a Mortality Catastrophe

We develop a continuous-time model for analyzing and valuing catastrophe mortality contingent claims based on stochastic modeling of the force of mortality. We derive parameter estimates from a 105-year time series of U.S. population mortality data using a simulated maximum likelihood approach based on a particle filter. Relying on the resulting parameters, we calculate loss profiles for a representative catastrophe mortality transaction and compare them to the “official” loss profiles that are provided by the issuers to investors and rating agencies. We find that although the loss profiles are subject to great uncertainties, the official figures fall significantly below the corresponding risk statistics based on our model. In particular, we find that the annualized incidence probability of a mortality catastrophe, defined as a 15% increase in aggregated mortality probabilities, is about 1.4%—compared to about 0.1% according to the official loss profiles.     

The Moran coefficient for non-normal data

This paper summarizes findings that extend statistical distribution properties of the Moran coefficient index measuring spatial autocorrelation to non-normal random variables. Pitman–Koopmans theorem results are extended for the mean and the variance of this index. This summary includes a corollary to this theorem, as well as a new theorem (with its proof) and two conjectures implied by it. The first of these statements is supported by asymptotic heuristics; the second is supported by simulation experiment results. Mixture random variables that include heteroscedasticity or overdispersion also are explored. In addition, a simple asymptotic variance for the Moran coefficient is presented, assessed, and found to be very precise for sample sizes as small as 25–100. The principal conclusion is that independence and sample size are the most relevant properties for Pitman–Koopmans theorem results to be extended to non-normal random variables. The independent and identically distributed property reduces the necessary sample size for this extension, as do the properties of symmetry and normal approximation.     

Inflationary Expectations in Israel: A Multiple Indicators Approach

This article derives unobservable short-term inflationary expectations in Israel from their postulated effects on the velocity of demand deposits and the nominal interest rate. A multiple-indicators model is estimated to assess the impact of inflationary expectations on these variables. The expectations-formation mechanism is estimated using a multiple indicators–multiple causes (MIMIC) model. This methodology allows us to test for the structural stability of the model and estimate the relative variances of the postulated error components. Moreover, this procedure contains a built-in specification test of the chosen expectations-formation mechanism.     

A Markov chain Monte Carlo algorithm for multiple imputation in large surveys

Important empirical information on household behavior and finances is obtained from surveys, and these data are used heavily by researchers, central banks, and for policy consulting. However, various interdependent factors that can be controlled only to a limited extent lead to unit and item nonresponse, and missing data on certain items is a frequent source of difficulties in statistical practice. More than ever, it is important to explore techniques for the imputation of large survey data. This paper presents the theoretical underpinnings of a Markov chain Monte Carlo multiple imputation procedure and outlines important technical aspects of the application of MCMC-type algorithms to large socio-economic data sets. In an illustrative application it is found that MCMC algorithms have good convergence properties even on large data sets with complex patterns of missingness, and that the use of a rich set of covariates in the imputation models has a substantial effect on the distributions of key financial variables.     

Generalized modulus-ratio tests for analysis of factorial designs with zero degrees of freedom for error

A method of statistical analysis of single replicate and fractional factorial designs requiring no estimate of error variance is given. By comparison of the relative magnitudes of independent effect .estimates, effects corresponding to relatively large effect estimates may be asserted to be nonzero. The procedure maintains a prespecified experimentwise error rate for a general class of modulus-ratio statistics.     

How socialization goals and peer social climate predict young children's concern for others: Evidence for a development shift between 2 and 4 years of age

Children's concern for others is shaped through socialization, but current theories make different predictions as to how and when in development this socializing occurs. Here we found that mothers' prosocial socialization goals (SGs) predicted concern for others in 2‐year‐old (n = 804) and 4‐year‐old (n = 714) children. In contrast, preschool teachers' SGs predicted concern for others only for 4‐year‐old children. In addition, a positive social climate among classroom peers predicted 4‐year‐olds' prosociality. These results suggest that the nuclear family environment impacts toddlers' concern for others before the broader social environment shapes their prosociality at preschool age.     

An Evaluation of the Emergence of Untrained Academic and Applied Skills After Instruction With Video Vignettes

The Analysis of Verbal Behavior - Applied behavior-analytic skills are derived from precise, technical, objective operational definitions and exemplars of natural phenomena. In some cases,...     

Dancing with the Devil: Partnering with Industry but Publishing in Academia*

We believe that partnering with industry can lead to research that is relevant, rigorous, and refreshing. Our experiences show that the potential benefits of partnering with industry are enormous, but that this is not an easy route for academics interested in operations research modeling or empirical methods. The need for grounded business research is greater now than ever, and, while academics have made great progress, there are still numerous opportunities to demonstrate the relevance of our research. We discuss how to establish industry contacts, identify fruitful academic—industry projects, and publish the resulting research.