This project proposes to tackle a number of important and challenging issues in econometric model building and forecasting under cross sectional dependence, heterogeneity and nonlinearity. This ...

A new study finds that AI-based analytical approaches provide more reliable insights into the efficiency of circular economy ...

Jon covers artificial intelligence. He previously led CNET's home energy and utilities category, with a focus on energy-saving advice, thermostats, and heating and cooling. Jon has more than a decade ...

Mass General Brigham investigators have developed a robust new artificial intelligence (AI) foundation model that is capable of analyzing brain MRI datasets to perform numerous medical tasks, ...

ABSTRACT: Variable selection using penalized estimation methods in quantile regression models is an important step in screening for relevant covariates. In this paper, we present a one-step estimation ...

Explore financial forecasting's importance in strategic decision-making, its methods, modern techniques, applications, and inherent challenges.

In the natural sciences, a laboratory experiment can isolate variables, various particles and their movements. Similarly, the employment of an econometric model is an attempt to produce an economic ...

ABSTRACT: This paper proposes a universal framework for constructing bivariate stochastic processes, going beyond the limitations of copulas and offering a potentially simpler alternative. The ...

Abstract: The current econometric models have the disadvantages of low prediction accuracy and poor model fitting effect. To solve these problems, this study combines Markov chain Monte Carlo ...

Abstract: The current econometric models have the disadvantages of low prediction accuracy and poor model fitting effect. In order to solve these problems, this study combines Markov chain Monte Carlo ...