Applied Macroeconomic Simulation with Python : DSGE Modeling, Agent-Based Systems, and Policy Stress Testing
Language: English
Published by Amazon Digital Services LLC - Kdp Mai 2026, 2026
- Softcover
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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£ 43.30
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Add to basketItem description from seller
Neuware - Reactive PublishingApplied Macroeconomic Simulation with Python is a practical guide to building, testing, and interpreting computational macroeconomic models using Python. Designed for advanced students, economists, analysts, researchers, and technically minded finance professionals, this book focuses on the modeling tools used to study economic systems, policy shocks, market dynamics, and structural uncertainty.The book introduces core approaches to macroeconomic simulation, including DSGE modeling, agent-based systems, numerical methods, calibration, sensitivity analysis, and policy stress testing. Readers will learn how computational models can be used to examine inflation, output, employment, monetary policy, fiscal policy, financial instability, and broader economic interactions.Rather than treating macroeconomics as a purely theoretical discipline, this book emphasizes implementation. It shows how Python can be used to construct models, run simulations, analyze outcomes, and compare different policy scenarios. Topics are presented with a focus on clarity, reproducibility, and practical application.Inside, readers will explore: DSGE model structure and simulation logicAgent-based approaches to heterogeneous economic behaviorPolicy stress testing and scenario designCalibration, shocks, and sensitivity analysisNumerical tools for solving and simulating macroeconomic systemsPython workflows for economic modeling and interpretationThis book is suitable for readers who already have a basic understanding of economics, statistics, and Python, and who want to move into more advanced computational macroeconomic analysis. It is not a shortcut or a promise of forecasting certainty. Instead, it provides a structured foundation for building models, testing assumptions, and studying how complex economies respond to changing conditions.…
Seller Inventory # 9798196177187
- Title
- Applied Macroeconomic Simulation with Python : DSGE Modeling, Agent-Based Systems, and Policy Stress Testing
- Author
- Oliver J Thatch
- Publisher
- Amazon Digital Services LLC - Kdp Mai 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798196177187
- Item weight
- 549 grams
- Dimensions
- 229x152x21 mm
Applied Macroeconomic Simulation with Python is a practical guide to building, testing, and interpreting computational macroeconomic models using Python. Designed for advanced students, economists, analysts, researchers, and technically minded finance professionals, this book focuses on the modeling tools used to study economic systems, policy shocks, market dynamics, and structural uncertainty.
The book introduces core approaches to macroeconomic simulation, including DSGE modeling, agent-based systems, numerical methods, calibration, sensitivity analysis, and policy stress testing. Readers will learn how computational models can be used to examine inflation, output, employment, monetary policy, fiscal policy, financial instability, and broader economic interactions.
Rather than treating macroeconomics as a purely theoretical discipline, this book emphasizes implementation. It shows how Python can be used to construct models, run simulations, analyze outcomes, and compare different policy scenarios. Topics are presented with a focus on clarity, reproducibility, and practical application.
Inside, readers will explore:
DSGE model structure and simulation logic
Agent-based approaches to heterogeneous economic behavior
Policy stress testing and scenario design
Calibration, shocks, and sensitivity analysis
Numerical tools for solving and simulating macroeconomic systems
Python workflows for economic modeling and interpretation
This book is suitable for readers who already have a basic understanding of economics, statistics, and Python, and who want to move into more advanced computational macroeconomic analysis. It is not a shortcut or a promise of forecasting certainty. Instead, it provides a structured foundation for building models, testing assumptions, and studying how complex economies respond to changing conditions.
"Synopsis" may belong to another edition of this title.
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