Macroeconomic Forecasting Using Alternative Data

Techniques for Applying Big Data and Machine Learning
Erscheinungsjahr 2023
ISBN: 978-0-12-819121-7
Verlag: Elsevier Science

Buch, Englisch, 250 Seiten, Format (B × H): 152 mm x 229 mm

Macroeconomic Forecasting Using Alternative Data: Techniques for Applying Big Data and Machine Learning applies computer science to the demands of macroeconomic forecasting. It is the first book to combine machine learning methods with macroeconomics. By using artificial intelligence and machine learning techniques, it unlocks the increased forecasting accuracy offered by alternative data sources. Through its interdisciplinary approach, readers learn how to use big datasets efficiently and effectively.

Zielgruppe

<p>Upper-division undergraduates, graduate students, and professionals working in economic forecasting, in macroeconomics, and in data applications in economics </p>

Autoren/Hrsg.

Jain, Apurv

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Makroökonomie

Inhaltsverzeichnis

1. The Importance of Macro Prediction2. Macro Data are Noisy3. Our Goal: Macro Data with Less Noise and Lag4. Alternate Data5. A Framework for Alternate Data6. Predicting Data Releases with Search7. Modeling Case Study: Non-Farm Payrolls8. Accounting Data9. Prediction in Practice10. Public Good: Visualizing World Economic Growth in Real Time11. Interviews with Policy Makers and Asset Managers

Über Autor(innen)

Jain, Apurv
Apurv Jain is the Senior Finance Lead and Co-Founder of the Economic Measurement Group at Microsoft. His team of scientists from Microsoft Research, ML experts from BingPredicts, and traders from Capital Markets Group use web-scale data (search, twitter etc.) to understand and predict the economy and the financial markets. Apurv sets the external product and research agenda, and he is the portfolio manager for an internal $150 mm portfolio devoted to testing our ideas. His alternate data and AI based strategies have a positive 3 year track record. He is also a visiting researcher at Harvard Business School.