Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are...

Relations between constructs are estimated based on correlations between measures of constructs corrected for measurement error. This process assumes that the true scores on the measure are linearly related to construct scores, an assumption that may not hold. We examined the extent to which...

Benchmarking methods can be used to extrapolate (or 'nowcast') low-frequency benchmarks on the basis of available high-frequency indicators. Quarterly national accounts are a typical example, where a number of monthly and quarterly indicators of economic activity are used to calculate...

This paper develops a dynamic option-based model for the valuation of rental and other similarly structured lease contracts under the conditions of uncertainty that is then solved by statistical simulation (Monte Carlo). The motivation, research background and methodology of the paper follow up...

I examine treatment effect heterogeneity within an experiment to inform external validity. The local average treatment effect (LATE) gives an average treatment effect for compliers. I bound and estimate average treatment effects for always takers and never takers by extending marginal treatment...

Because many developing countries fail to report trade statistics to the United Nations, there has been an interest in using partner-country data to fill these information gaps. The author used partner-country statistics for 30 developing countries to quot;estimatequot; actual (concealed) trade...

Statistical Learning refers to statistical aspects of automated extraction of regularities (structure) in datasets. It is a broad area which includes neural networks, regression-trees, nonparametric statistics and sieve approximation, boosting, mixtures of models, computational complexity,...

Developing models to detect financial statement fraud involves challenges related to (i) the rarity of fraud observations, (ii) the relative abundance of explanatory variables identified in the prior literature, and (iii) the broad underlying definition of fraud. Following the emerging data...