Related papers: Introducing a New Brexit-Related Uncertainty Index…
Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts ("a scene with objects that have the same color") and ad-hoc categories defined through goals…
This study evaluates large language models as estimable classifiers and clarifies how modeling choices shape downstream measurement error. Revisiting the Economic Policy Uncertainty index, we show that contemporary classifiers substantially…
Methods and applications are inextricably linked in science, and in particular in the domain of text-as-data. In this paper, we examine one such text-as-data application, an established economic index that measures economic policy…
The need for timely data analysis for economic decisions has prompted most economists and policy makers to search for non-traditional supplementary sources of data. In that context, text data is being explored to enrich traditional data…
This paper evaluates the dynamic response of economic activity to shocks in uncertainty as percieved by agents.The study focuses on the comparison between the perception of economic uncertainty by manufacturers and consumers.Since…
The purpose of this study is to investigate the effects of the COVID-19 pandemic on economic policy uncertainty in the US and the UK. The impact of the increase in COVID-19 cases and deaths in the country, and the increase in the number of…
We introduce Unified Multimodal Uncertain Inference (UMUI), a multimodal inference task spanning text, audio, and video, where models must produce calibrated probability estimates of hypotheses conditioned on a premise in any modality or…
We study macroeconomic fluctuations in the United Kingdom over seven centuries (1271--2022) using a time-varying VAR with stochastic volatility. We identify business cycle shocks as innovations explaining the largest share of future output…
Understanding the effects of economic shocks on firms is critical for analyzing economic growth and resilience. We introduce a Web-Based Affectedness Indicator (WAI), a general-purpose tool for real-time monitoring of economic disruptions…
Despite recent progress in systematic evaluation frameworks, benchmarking the uncertainty of large language models (LLMs) remains a highly challenging task. Existing methods for benchmarking the uncertainty of LLMs face three key…
This paper explores artificial intelligence's potential societal and economic impacts (AI) through generating scenarios that assess how AI may influence various sectors. We categorize and analyze key factors affecting AI's integration and…
Liquidity withdrawal is a critical indicator of market fragility. In this project, I test a framework for forecasting liquidity withdrawal at the individual-stock level, ranging from less liquid stocks to highly liquid large-cap tickers,…
Cryptocurrencies' values often respond aggressively to major policy changes, but none of the existing indices informs on the market risks associated with regulatory changes. In this paper, we quantify the risks originating from new…
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial.…
This paper constructs a global economic policy uncertainty index through the principal component analysis of the economic policy uncertainty indices for twenty primary economies around the world. We find that the PCA-based global economic…
We introduce a new identification strategy for uncertainty shocks to explain macroeconomic volatility in financial markets. The Chicago Board Options Exchange Volatility Index (VIX) measures market expectations of future volatility, but…
We applied the Johansen-Ledoit-Sornette (JLS) model to detect possible bubbles and crashes related to the Brexit/Bremain referendum scheduled for 23rd June 2016. Our implementation includes an enhanced model calibration using Genetic…
To interpret uncertainty estimates from differentiable probabilistic models, recent work has proposed generating Counterfactual Latent Uncertainty Explanations (CLUEs). However, for a single input, such approaches could output a variety of…
The purpose of this paper is to use the votes cast at the 2019 European elections held in United Kingdom to re-visit the analysis conducted subsequent to its 2016 European Union referendum vote. This exercise provides a staging post on…
This paper presents a methodological approach to financial time series analysis by combining causal discovery and uncertainty-aware forecasting. As a case study, we focus on four key U.S. macroeconomic indicators -- GDP, economic growth,…