This paper examines how uncertainty regarding future mortality and life expectancy outcomes, i.e. longevity risk, affects employer-provided defined benefit (DB) private pension plans liabilities. The paper argues that to assess uncertainty and associated risks adequately, a stochastic approach...

The environmental variables infl uence must be considered in the effi ciency analysis in order to account for exogenous factors out of the managerial control. The fi rst objective of this work is to show a survey of the effi ciency frontier methods that include environmental variables. Later on,...

An algorithm to generate samples with approximate first-, second-, and third-order moments is presented extending the Cholesky matrix decomposition to a Cholesky tensor decomposition of an arbitrary order. The tensor decomposition of the first-, second-, and third-order objective moments...

A meta-analysis (MA) aggregates estimated effects from many studies to calculate a single, overall effect. There is no one, generally accepted procedure for how to do this. Several estimators are commonly used, though little is known about their relative performance. A complication arises when...

This paper replicates the estimates of a fractional response model for share data reported in the seminal paper of Leslie E. Papke and Jeffrey M. Wooldridge published in the Journal of Applied Econometrics 11(6), 1996, pp.619-632. We have been able to replicate all of the reported estimation...

Hungarian debt level has steadily increased since 2001, with the debt-to-GDP ratio reaching about 84% at end-2011. This high level combined with significant volatility of macroeconomic variable influencing potential future debt paths – GDP growth, exchange rate and interest spreads – put...

The forecasting uncertainty around point macroeconomic forecasts is usually measured by the historical performance of the forecasting model, using measures such as root mean squared forecasting errors (RMSE). This measure, however, has the major drawback that it is constant over time and hence...

We develop a new method to sample from posterior distributions in hierarchical models without using Markov chain Monte Carlo. This method, which is a variant of importance sampling ideas, is generally applicable to high-dimensional models involving large data sets. Samples are independent, so...