MATLAB simulation shows that under the condition that the fusion quality standard is certain, the accuracy of data fusion of machine learning on macroeconomics, Data fusion time is better than traditional data processing techniques. 展开 关键词:...
The course is based on classical computational techniques in macroeconomics (e.g., dynamic programming, perturbation, projection methods, simulation-based methods etc.), recent advancements in machine learning (deep learning and reinforcement learning), and high-performance computing (parallel computing, ...
Machine learning specialists are often primarily concerned with developing high-performance computer syste...
Applying four machine learning techniques to answer this real-world problem—C4.5 decision tree, two different rule induction classifiers (PART algorithm and Rough Set) and the logistic regression methodology—, our evidence suggests that by simply identifying the KAM topics disclosed in the report, ...
Economic complexity methods, and in particular relatedness measures, lack a systematic evaluation and comparison framework. We argue that out-of-sample forecast exercises should play this role, and we compare various machine learning models to set the prediction benchmark. We find that the key object...
We present a comprehensive comparative case study for the use of machine learning models for macroeconomics forecasting. We find that machine learning models mostly outperform conventional econometric approaches in forecasting changes in US unemployment on a 1-year horizon. To address the black box ...
We also thank the seminar participants at University of Orléans as well as the participants of the 16th Conference “Développements Récents de l’Econométrie Appliquée à la Finance” (Université Paris Nanterre), 7th PhD Student Conference in International Macroeconomics and Financial Econometrics,...
Macroeconomics- Topics in macroeconomics with notebook examples. Machine Learning EconML- Automated Learning and Intelligence for Causation andEconomics. Auctions- Optimal auctions using deep learning. Computational Quant Econ- Quantitative economics course by NYU ...
In the present study, we propose a machine learning-based variable selection method based on theoretical and regulatory considerations. Formally, the most influential risk drivers from a best-in-class machine learning model are identified using Shapley values and employed as clustering variables. Thus,...
Navigating a path toward net-zero, requires the assessment of physical climate risks for a broad range of future economic scenarios, and their associated carbon concentration pathways. Climate models typically simulate a limited number of possible pathwa