Deep Learning Models for Economic Research

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A01=Andrzej Dudek
Artificial Intelligence
Author_Andrzej Dudek
Category=KCA
Category=KCH
Category=KCJ
Category=UYQ
Discriminant analysis
Econometrics
eq_bestseller
eq_business-finance-law
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
interpretable deep learning for economists
Machine Learning
machine learning economics
Mathematical Modeling
neural network interpretability
Neural Networks
Python data analysis
quantitative finance methods
sentiment analysis techniques
Time series analysis
time series forecasting

Product details

  • ISBN 9781041062707
  • Weight: 1060g
  • Dimensions: 156 x 234mm
  • Publication Date: 21 Oct 2025
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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In today’s data-driven world, the ability to make sense of complex, high-dimensional datasets is crucial for economists and data scientists. Traditional quantitative methods, while powerful, often struggle to keep up with the complexities of modern economic challenges. This book bridges this gap, integrating cutting-edge machine learning techniques with established economic analysis to provide new, more accurate insights.

The book offers a comprehensive approach to understanding and applying neural networks and deep learning models in the context of conducting economic research. It starts by laying the groundwork with essential quantitative methods such as cluster analysis, regression, and factor analysis, then demonstrates how these can be enhanced with deep learning techniques like recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers. By guiding readers through real-world examples, complete with Python code and access to datasets, it showcases the practical benefits of neural networks in solving complex economic problems, such as fraud detection, sentiment analysis, stock price forecasting, and inflation factor analysis. Importantly, the book also addresses critical concerns about the “black box” nature of deep learning, offering interpretability techniques like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to demystify model predictions.

The book is essential reading for economists, data scientists, and professionals looking to deepen their understanding of AI’s role in economic modeling. It is also an accessible resource for non-experts interested in how machine learning is transforming economic analysis.

Andrzej Dudek is a Professor in the Department of Computer Science and Econometrics, Wrocław University of Economics and Business, Wrocław, Poland.

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