Related papers: A Conversation with George C. Tiao
We develop a Bayesian spatio-temporal model to study pre-industrial grain market integration during the Finnish famine of the 1860s. Our model takes into account several problematic features often present when analysing multiple spatially…
Analyzing time series data is crucial to a wide spectrum of applications, including economics, online marketplaces, and human healthcare. In particular, time series classification plays an indispensable role in segmenting different phases…
Explaining online time series monitoring models is crucial across sensitive domains such as healthcare and finance, where temporal and contextual prediction dynamics underpin critical decisions. While recent XAI methods have improved the…
The expression "wage transition" refers to the fact that over the past two or three decades in all developed economies wage increases have levelled off. There has been a widening divergence and decoupling between wages on the one hand and…
Lawrence (2001)found computer science articles that were openly accessible (OA) on the Web were cited more. We replicated this in physics. We tested 1,307,038 articles published across 12 years (1992-2003) in 10 disciplines (Biology,…
This paper develops the first comprehensive theoretical and empirical framework for analyzing AI-driven spatial distribution dynamics in metropolitan areas undergoing demographic transition. We extend New Economic Geography by formalizing…
Accurately quantifying tail risks-rare but high-impact events such as financial crashes or extreme weather-is a central challenge in risk management, with serially dependent data. We develop a Bayesian framework based on the Generalized…
Macroeconomic theories of growth and wealth distribution have an outsized influence on national and international social and economic policies. Yet, due to a relative lack of reliable, system wide data, many such theories remain, at best,…
For many economic questions, the empirical results are not interesting unless they are strong. For these questions, theorizing before the results are known is not always optimal. Instead, the optimal sequencing of theory and empirics trades…
IAU Inter-Commission B2-C1-C2 WG Data-driven Astronomy Education and Public Outreach (DAEPO) was launched officially in April 2017. With the development of many mega-science astronomical projects, for example CTA, DESI, EUCLID, FAST, GAIA,…
We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs. ZOOMER is designed for tackling two challenges presented by the massive…
This study investigates the shifting global dynamics of Artificial Intelligence (AI) research by analysing the trajectories of countries dominating AI publications between 2000 and 2025. Drawing on the comprehensive OpenAlex datasets and…
Reliable prediction of large chaotic sytems in the short to middle time range is of interest in a number of fields, including climate, ecology, seismology, and economics. In this paper, results from chaos theory, and statistical theory are…
A non-Bayesian time-varying model is developed by introducing the concept of the degree of market efficiency that varies over time. This model may be seen as a reflection of the idea that continuous technological progress alters the trading…
Urban causal research is essential for understanding the complex, dynamic processes that shape cities and for informing evidence-based policies. However, current practices are often constrained by inefficient and biased hypothesis…
We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO's AI Patent Dataset. Our classifier substantially improves the existing USPTO…
China and Latin America (LATAM) are now key players in global research production. This study presents a comparative study on research on innovation in management and decision sciences based on data from Scopus and Web of Knowledge (WoS)…
This study presents a comprehensive climatological benchmarking of tropical cyclones (TCs) generated by AI-based global weather prediction models. Using all TC events from the North Atlantic and Western Pacific basins between 2020 and 2025,…
The dissertation describes ergodic properties of some stochastic dynamical systems generated by Markov chains with values in the state space which is a Polish space. The mathematical model describing the process of cell division is…
Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several settings. These architectures take advantage of a graph…