Breakthrough Hybrid Model Merges Deep Learning and Production Theory, Outperforming Standard Benchmarks
A research team comprising Zheng Wei, Huiyan Sang, Artem Prokhorov, and Yu Ma has published a paper titled “Shape-Aware Deep Learning for Models of Production” in Journal of Productivity Analysis.
The study proposes a breakthrough method that combines the power of Deep Neural Networks (DNNs) with fundamental economic principles.
Study Reveals Critical Flaws in Standard Methods for Assessing Firm Efficiency
An international research team including Subal C. Kumbhakar, A. Peresetsky, Y. Shchetynin, and A. Zaytsev has published a paper “Technical efficiency and inefficiency: Reliability of standard SFA models and a misspecification problem.” The study uncovers a fundamental issue in Stochastic Frontier Analysis (SFA) models used to evaluate the performance of firms and industries.
New Method for Pinpointing Breaks in Economic Trends
Researchers from the Centre for Big Data in Economics and Finance have developed a new method for accurately identifying structural breaks in economic and financial time series. Their paper, "Change-Point Detection in Time Series Using Mixed Integer Programming," introduces a framework based on Mixed Integer Optimization (MIO).