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Springer Prediction and Causality in Econometrics - Studies in Computational Intelligence 983

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Description

This comprehensive book from Springer examines prediction and causality within econometrics, addressing the fundamental economic question of how the global economy develops and what effects different policy implementations will produce. The work combines traditional econometric methods with innovative techniques, including quantum-inspired approaches, to forecast economic outcomes across multiple scales. Readers will find in-depth analysis of international economic phenomena such as trade flows and exchange rate movements, alongside country-level indicators including gross domestic product, stock market indices, and inflation rates. The book synthesizes research from contributors working in computational intelligence and econometric modelling, making it essential reading for economists, researchers, and policy analysts seeking rigorous frameworks for understanding causal relationships in economic systems and developing evidence-based economic forecasts.

Key Features

Springer publishes this volume as part of the Studies in Computational Intelligence series, ensuring academic rigour and peer-reviewed content for econometric research.

The book integrates traditional econometric techniques with innovative approaches, including quantum-inspired methods, to improve economic prediction accuracy.

Coverage spans multiple economic scales from international trade and exchange rates to country-level metrics such as GDP, stock indices, and inflation rates.

The work emphasises causal analysis alongside prediction, helping readers understand what causes what in economic systems rather than relying on correlation alone.

This volume addresses both theoretical foundations and practical applications of prediction and causality in computational intelligence for economics.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
27 July 2021
Listed Since
15 August 2020

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