Results 131 to 140 of about 4,529 (276)
Forecasting With Dynamic Factor Models Estimated by Partial Least Squares
Journal of Forecasting, EarlyView.ABSTRACT Dynamic factor models (DFMs) have found great success in nowcasting and short‐term macroeconomic forecasting when incorporating large sets of predictive information. The factor loadings are typically estimated cross‐sectionally with principal component analysis (PCA) or maximum likelihood (ML), which ignore whether the factors have predictive ...
Samuel Rauhala
wiley +1 more source
Point and Risk estImation Using an enSemble of Models for Nowcasting: PRISM‐Now
Journal of Forecasting, EarlyView.ABSTRACT We propose PRISM‐Now, a novel ensemble forecasting system for near‐term GDP projection. Recognizing that relevant economic information evolves over time, we treat forecasts from multiple base models as draws from a mixture distribution of “good” and “bad” estimates, whose composition changes continuously and cannot be identified ex ante.
Beomseok Seo, Hyungbae Cho, Dongjae Lee
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Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models
Journal of Forecasting, EarlyView.ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly +2 more
wiley +1 more source
Brexit and Its Impact on EU Financial Markets
International Journal of Finance &Economics, EarlyView.ABSTRACT We investigate the impact of Brexit on volatility spillovers across the EU countries. We introduce a Brexit intensity measure that assigns an intensity score reflective of the financial markets' reaction to the events that occurred as Brexit negotiations began to unfold.
Marwan Izzeldin +3 more
wiley +1 more source
International Studies of Economics, EarlyView.Abstract This paper examines the link between climate risk, energy consumption, and financial market performance in a sample of emerging countries over the period 2000–2024. The objective is to model the dynamic interactions between these three dimensions, in order to understand the extent to which energy dependence and exposure to climate risks ...
Abdelkader Mohamed Derbali
wiley +1 more source
Revisiting EWMA in High‐Frequency‐Based Portfolio Optimization: A Comparative Assessment
Journal of Applied Econometrics, EarlyView.ABSTRACT This paper compares the statistical and economic performance of state‐of‐the‐art high‐frequency (HF) based multivariate volatility models with a simpler, widely used alternative, the Exponentially Weighted Moving Average (EWMA) filter. Using over two decades of 100 U.S.
Laura Capera Romero, Anne Opschoor
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Identifying Peer Effects in Networks with Unobserved Effort and Isolated Students
Journal of Applied Econometrics, EarlyView.ABSTRACT Peer influence on effort devoted to some activity is often studied when effort is unobserved, and the researcher instead observes an outcome that combines effort with other shocks. For instance, in education, achievement measures such as GPA reflect both effort and idiosyncratic GPA shocks.
Aristide Houndetoungan +2 more
wiley +1 more source
Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance
Naval Research Logistics (NRL), EarlyView.ABSTRACT The rapid expansion of cross‐border e‐commerce (CBEC) has created significant opportunities for small‐ and medium‐sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third‐party logistics (3PL)‐led supply chain finance (SCF) has emerged as a promising solution, leveraging in‐transit inventory as ...
Qingkai Zhang, L. Jeff Hong, Houmin Yan
wiley +1 more source
Epistemic and aleatoric uncertainty quantification in weather and climate models
Quarterly Journal of the Royal Meteorological Society, EarlyView.Aleatoric and epistemic uncertainties over time on weather and climate time‐scales, estimated through ensembles that sample aleatoric and epistemic uncertainty using Bayesian neural networks for parameterisations in the Lorenz 1996 model. The spread shows the 16th and 84th percentiles.
Laura A. Mansfield +1 more
wiley +1 more source
Responsive Materials, EarlyView.Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen +8 more
wiley +1 more source