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Advanced Bayesian Macroeconomics with Python: Hierarchical Models, Nowcasting, and Real-Time Policy Decision Frameworks
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Publisher: Independently Published
Pages: 396
Weight: 1.05
Height: 0.98 Width: 6.00 Depth: 9.00
Language: English
Advanced Bayesian Macroeconomics with Python provides a rigorous, hands-on treatment of modern Bayesian methods applied to macroeconomic modeling and policy analysis.
This book bridges advanced macroeconomic theory with practical implementation in Python. It focuses on hierarchical Bayesian models, nowcasting techniques, and real-time decision frameworks that allow economists and researchers to incorporate uncertainty, update beliefs with new data, and support timely policy decisions.
- Detailed coverage of hierarchical Bayesian models for macroeconomics
- Practical nowcasting methods using Bayesian approaches
- Real-time policy decision frameworks that integrate high-frequency and mixed-frequency data
- Complete Python code examples using libraries such as PyMC, NumPy, Pandas, and ArviZ
- Model diagnostics, prior selection, and posterior analysis tailored to macroeconomic applications
- Techniques for handling large-scale datasets and computational efficiency
Written for graduate students, academic researchers, central bank economists, and quantitative analysts, this book assumes familiarity with intermediate macroeconomics, Bayesian statistics, and basic Python programming. It emphasizes clarity, reproducibility, and technical precision over superficial applications.
Whether you are building models for forecasting, conducting structural analysis, or supporting real-time policy work, this book offers the tools and code necessary to implement advanced Bayesian methods in professional macroeconomic research.
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