There are many models in the current statistical literature for making inferences based on samples selected from a finite population. Parametric models may be problematic because statistical inference is sensitive to parametric assumptions. The Dirichlet process (DP) prior is very flexible and...

In this paper, I propose a tractable approach to Bayesian inference in linear regression models for which the standard exogeneity assumption does not hold. By specifying a beta prior for the squared correlation between an error term and regressor, I demonstrate that the implied prior for a bias...

Stochastic multiplicative dynamics characterize many complex natural phenomena such as selection and mutation in evolving populations, and the generation and distribution of wealth withinsocial systems. Population heterogeneity in stochastic growth rates has been shown to be the critical driver...

The study was conducted to assess the impact of macro factors on economic growth in emerging and growth-leading economies (EAGLEs) during the COVID-19 pandemic. The study applied a Bayesian simulation method, and the empirical results exhibited an ambiguous impact of trade openness and the...

Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. Using the quantum Monte Carlo (QMC) algorithm, we are the first to study whether quantum computing can improve the run time of economic applications and challenges in doing so....