AbstractThe following sections are included:IntroductionIntegrationIntegrals on Abstract Measure SpacesIntegrals on Sets of Real NumbersProperties of E(·)Expectations as Informative FunctionalsMoments of Random VariablesGenerating FunctionsConditional ExpectationMoments of Conditional...

Despite the widespread use of graphs in empirical research, little is known about readers' ability to process the statistical information they are meant to convey ("visual inference"). We study visual inference within the context of regression discontinuity (RD) designs by measuring how...

Despite the widespread use of graphs in empirical research, little is known about readers' ability to process the statistical information they are meant to convey ("visual inference"). We study visual inference within the context of regression discontinuity (RD) designs by measuring how...

This paper develops a Bayesian approach to inference in a class of partially identified econometric models. Models in this class are characterized by a known mapping between a point identified reduced-form parameter μ and the identified set for a partially identified parameter θ . The approach...

This book approaches the elementary statistical concepts. It presents also some statistical methods of collecting and analysis of data.

This monograph presents the basics of the composite marginal likelihood (CML) inference approach, discussing the asymptotic properties of the CML estimator and the advantages and limitations of the approach. The composite marginal likelihood (CML) inference approach is a relatively simple...

To avert the impending global Cyber-Finance Insurance Crisis based upon large-scale commercial reliance upon quantitative models with inherent model risks, tail risks, and systemic risks in current form, this post-doctoral thesis makes the following key contributions: Develops the first known...

Financial analysts assume that the reliability of predictions derived from regression analysis improves with sample size. This is generally true because larger samples tend to produce less noisy results than smaller samples. But this is not always the case. Some observations are more relevant...

Recently, there has been much discussion about replicability and credibility. By integrating the full research record, increasing statistical power, reducing bias and enhancing credibility, meta-analysis is widely regarded as 'best evidence'. Through Monte Carlo simulation, closely calibrated on...

The traditional time series methodology requires at least a preliminary transformation of the data to get stationarity. On the other hand, robust Bayesian dynamic models (RBDMs) do not assume a regular pattern or stability of the underlying system but can include points of statement breaks. In...