Quantitative economics & python series (21 résultats)

Etat : New.

Etat : As New. Unread book in perfect condition.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New.

Etat : As New. Unread book in perfect condition.

Taschenbuch. Etat : Neu. Neuware - Reactive PublishingEconomic systems are shaped by complex cause-and-effect relationships that traditional correlation-based models often fail to capture. Structural Causal Economics with Python introduces a rigorous, modern framework for understanding economic mechanisms using structural causal models, graph-based reasoning, and counterfactual simulation.This book bridges the gap between academic causal inference theory and real-world economic modeling. Readers learn how to move beyond prediction into explanation, policy testing, and scenario design using practical Python implementations. The focus is on building models that reflect how economic systems actually behave under intervention, shock, and policy change.Inside, you will learn how to: - Build and interpret causal graphs for economic systems- Design structural models that capture real economic mechanisms- Perform counterfactual analysis to evaluate alternative policy scenarios- Simulate policy interventions and measure downstream effects- Implement causal modeling workflows using modern Python tools- Connect causal inference methods to macroeconomic and microeconomic applicationsRather than treating economic data as purely statistical signals, this book teaches you how to model the underlying structure that generates economic outcomes. The result is more robust forecasting, clearer policy insight, and deeper strategic understanding.Written for quantitative economists, policy analysts, data scientists, and advanced finance professionals, this book assumes familiarity with Python and core economic concepts. It is designed as both a practical implementation guide and a conceptual reference for structural causal economic modeling.If you want economics models that explain why outcomes happen, not just what happens next, this book provides the tools and framework to build them.…

Taschenbuch. Etat : Neu. Neuware - Reactive PublishingModern forecasting is no longer about guessing the future. It is about engineering it. Economists, analysts, and quantitative leaders now demand models that explain why, not just what. This book shows how to build causal and predictive forecasting systems using the full power of econometrics and Python, bridging classical statistical tools with machine learning, structural modeling, and real-world business applications.Readers learn how to design time series pipelines, estimate causal effects, and translate empirical models into operational forecasts that drive executive decisions. From ARIMA to VAR, from causal inference to Bayesian time series, and from model selection to forecast evaluation, the book provides a rigorous yet accessible framework for forecasting markets, macroeconomic indicators, commodities, operational demand, financial performance, and policy scenarios.Beyond the theory, Applied Econometric Forecasting with Python emphasizes implementation. Full workflows demonstrate how to structure data, choose the correct econometric formulation, evaluate forecast accuracy, and deploy models at scale. The book closes with advanced chapters on structural breaks, adaptive forecasting, rolling horizons, scenario analysis, and machine learning augmentation.You will learn: - How to construct causal models that isolate drivers and explain economic behavior- How to implement econometric time series forecasting pipelines in Python- How to integrate machine learning with classical econometrics for more robust predictions- How to evaluate forecast performance and uncertainty- How to build rolling, scenario-based, and probabilistic forecasts- How to translate empirical models into operational decision frameworksIdeal for: Finance professionals, economists, data scientists, policy analysts, enterprise planning teams, academic researchers, and quantitative practitioners seeking a rigorous applied forecasting playbook.The future belongs to those who can quantify uncertainty, measure causality, and model change. This book shows you how.…

Taschenbuch. Etat : Neu. Neuware - Reactive PublishingFinancial Econometrics: Models, Methods, and Applications 2025 offers a cutting-edge exploration of how statistical and econometric techniques are transforming modern finance. This comprehensive guide bridges theory and practice, equipping readers with the models, tools, and insights necessary to evaluate risk, forecast returns, and make data-driven financial decisions.From time-series analysis and volatility modeling to factor models, machine learning integration, and real-world portfolio applications, this book provides a roadmap for professionals, students, and researchers seeking to master the art of financial econometrics in today's complex markets.Whether you are managing risk, designing trading strategies, or conducting academic research, this book delivers practical clarity on advanced quantitative methods, empowering you to harness econometrics for financial innovation and long-term success.…

Etat : New. Print on Demand.

Paperback. Etat : new. Paperback. Reactive PublishingModern forecasting is no longer about guessing the future. It is about engineering it. Economists, analysts, and quantitative leaders now demand models that explain why, not just what. This book shows how to build causal and predictive forecasting systems using the full power of econometrics and Python, bridging classical statistical tools with machine learning, structural modeling, and real-world business applications.Readers learn how to design time series pipelines, estimate causal effects, and translate empirical models into operational forecasts that drive executive decisions. From ARIMA to VAR, from causal inference to Bayesian time series, and from model selection to forecast evaluation, the book provides a rigorous yet accessible framework for forecasting markets, macroeconomic indicators, commodities, operational demand, financial performance, and policy scenarios.Beyond the theory, Applied Econometric Forecasting with Python emphasizes implementation. Full workflows demonstrate how to structure data, choose the correct econometric formulation, evaluate forecast accuracy, and deploy models at scale. The book closes with advanced chapters on structural breaks, adaptive forecasting, rolling horizons, scenario analysis, and machine learning augmentation.You will learn: - How to construct causal models that isolate drivers and explain economic behavior- How to implement econometric time series forecasting pipelines in Python- How to integrate machine learning with classical econometrics for more robust predictions- How to evaluate forecast performance and uncertainty- How to build rolling, scenario-based, and probabilistic forecasts- How to translate empirical models into operational decision frameworksIdeal for: Finance professionals, economists, data scientists, policy analysts, enterprise planning teams, academic researchers, and quantitative practitioners seeking a rigorous applied forecasting playbook.The future belongs to those who can quantify uncertainty, measure causality, and model change. This book shows you how. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Etat : new. Paperback. Reactive PublishingEconomic systems are shaped by complex cause-and-effect relationships that traditional correlation-based models often fail to capture. Structural Causal Economics with Python introduces a rigorous, modern framework for understanding economic mechanisms using structural causal models, graph-based reasoning, and counterfactual simulation.This book bridges the gap between academic causal inference theory and real-world economic modeling. Readers learn how to move beyond prediction into explanation, policy testing, and scenario design using practical Python implementations. The focus is on building models that reflect how economic systems actually behave under intervention, shock, and policy change.Inside, you will learn how to: - Build and interpret causal graphs for economic systems- Design structural models that capture real economic mechanisms- Perform counterfactual analysis to evaluate alternative policy scenarios- Simulate policy interventions and measure downstream effects- Implement causal modeling workflows using modern Python tools- Connect causal inference methods to macroeconomic and microeconomic applicationsRather than treating economic data as purely statistical signals, this book teaches you how to model the underlying structure that generates economic outcomes. The result is more robust forecasting, clearer policy insight, and deeper strategic understanding.Written for quantitative economists, policy analysts, data scientists, and advanced finance professionals, this book assumes familiarity with Python and core economic concepts. It is designed as both a practical implementation guide and a conceptual reference for structural causal economic modeling.If you want economics models that explain why outcomes happen, not just what happens next, this book provides the tools and framework to build them. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Etat : New. Print on Demand.

Paperback. Etat : new. Paperback. Reactive PublishingFinancial Econometrics: Models, Methods, and Applications 2025 offers a cutting-edge exploration of how statistical and econometric techniques are transforming modern finance. This comprehensive guide bridges theory and practice, equipping readers with the models, tools, and insights necessary to evaluate risk, forecast returns, and make data-driven financial decisions.From time-series analysis and volatility modeling to factor models, machine learning integration, and real-world portfolio applications, this book provides a roadmap for professionals, students, and researchers seeking to master the art of financial econometrics in today's complex markets.Whether you are managing risk, designing trading strategies, or conducting academic research, this book delivers practical clarity on advanced quantitative methods, empowering you to harness econometrics for financial innovation and long-term success. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Etat : new. Paperback. Reactive PublishingEconomic systems are shaped by complex cause-and-effect relationships that traditional correlation-based models often fail to capture. Structural Causal Economics with Python introduces a rigorous, modern framework for understanding economic mechanisms using structural causal models, graph-based reasoning, and counterfactual simulation.This book bridges the gap between academic causal inference theory and real-world economic modeling. Readers learn how to move beyond prediction into explanation, policy testing, and scenario design using practical Python implementations. The focus is on building models that reflect how economic systems actually behave under intervention, shock, and policy change.Inside, you will learn how to: - Build and interpret causal graphs for economic systems- Design structural models that capture real economic mechanisms- Perform counterfactual analysis to evaluate alternative policy scenarios- Simulate policy interventions and measure downstream effects- Implement causal modeling workflows using modern Python tools- Connect causal inference methods to macroeconomic and microeconomic applicationsRather than treating economic data as purely statistical signals, this book teaches you how to model the underlying structure that generates economic outcomes. The result is more robust forecasting, clearer policy insight, and deeper strategic understanding.Written for quantitative economists, policy analysts, data scientists, and advanced finance professionals, this book assumes familiarity with Python and core economic concepts. It is designed as both a practical implementation guide and a conceptual reference for structural causal economic modeling.If you want economics models that explain why outcomes happen, not just what happens next, this book provides the tools and framework to build them. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Etat : new. Paperback. Reactive PublishingModern forecasting is no longer about guessing the future. It is about engineering it. Economists, analysts, and quantitative leaders now demand models that explain why, not just what. This book shows how to build causal and predictive forecasting systems using the full power of econometrics and Python, bridging classical statistical tools with machine learning, structural modeling, and real-world business applications.Readers learn how to design time series pipelines, estimate causal effects, and translate empirical models into operational forecasts that drive executive decisions. From ARIMA to VAR, from causal inference to Bayesian time series, and from model selection to forecast evaluation, the book provides a rigorous yet accessible framework for forecasting markets, macroeconomic indicators, commodities, operational demand, financial performance, and policy scenarios.Beyond the theory, Applied Econometric Forecasting with Python emphasizes implementation. Full workflows demonstrate how to structure data, choose the correct econometric formulation, evaluate forecast accuracy, and deploy models at scale. The book closes with advanced chapters on structural breaks, adaptive forecasting, rolling horizons, scenario analysis, and machine learning augmentation.You will learn: - How to construct causal models that isolate drivers and explain economic behavior- How to implement econometric time series forecasting pipelines in Python- How to integrate machine learning with classical econometrics for more robust predictions- How to evaluate forecast performance and uncertainty- How to build rolling, scenario-based, and probabilistic forecasts- How to translate empirical models into operational decision frameworksIdeal for: Finance professionals, economists, data scientists, policy analysts, enterprise planning teams, academic researchers, and quantitative practitioners seeking a rigorous applied forecasting playbook.The future belongs to those who can quantify uncertainty, measure causality, and model change. This book shows you how. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Etat : new. Paperback. Reactive PublishingFinancial Econometrics: Models, Methods, and Applications 2025 offers a cutting-edge exploration of how statistical and econometric techniques are transforming modern finance. This comprehensive guide bridges theory and practice, equipping readers with the models, tools, and insights necessary to evaluate risk, forecast returns, and make data-driven financial decisions.From time-series analysis and volatility modeling to factor models, machine learning integration, and real-world portfolio applications, this book provides a roadmap for professionals, students, and researchers seeking to master the art of financial econometrics in today's complex markets.Whether you are managing risk, designing trading strategies, or conducting academic research, this book delivers practical clarity on advanced quantitative methods, empowering you to harness econometrics for financial innovation and long-term success. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…