9798245339887 - applied econometric forecasting with python: causal models, time series, and predictive analytics: 7 di van der post, hayden (5 risultati)
Lingua: Inglese
Editore: Independently Published, 2026
Serie: Libro 5 di 16 - Quantitative Economics & Python Series
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Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 41,75
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Lingua: Inglese
Editore: Independently Published, 2026
Serie: Libro 5 di 16 - Quantitative Economics & Python Series
- Brossura
Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 37,86
EUR 5,87 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Lingua: Inglese
Editore: Independently Published Jan 2026, 2026
Serie: Libro 5 di 16 - Quantitative Economics & Python Series
- Brossura
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 54,00
EUR 63,18 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: 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.
Lingua: Inglese
Editore: Independently Published, 2026
Serie: Libro 5 di 16 - Quantitative Economics & Python Series
- Brossura
- Print on Demand
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 41,74
Spedizione gratuitaSpedito in U.S.A.Quantità: 1 disponibili
Paperback. Condizione: 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 p…ower 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.
Lingua: Inglese
Editore: Independently Published, 2026
Serie: Libro 5 di 16 - Quantitative Economics & Python Series
- Brossura
- Print on Demand
Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 42,12
EUR 43,24 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: 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 p…ower 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.
