Macroeconomic Forecasting in the Era of Big Data

1. Front Matter

   Pages i-xiii

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2. Introduction

   1. Front Matter

      Pages 1-1

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   2. Sources and Types of Big Data for Macroeconomic Forecasting

      • Philip M. E. Garboden

      Pages 3-23

3. Capturing Dynamic Relationships

   1. Front Matter

      Pages 25-25

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   2. Dynamic Factor Models

      • Catherine Doz, Peter Fuleky

      Pages 27-64

   3. Factor Augmented Vector Autoregressions, Panel VARs, and Global VARs

      • Martin Feldkircher, Florian Huber, Michael Pfarrhofer

      Pages 65-93

   4. Large Bayesian Vector Autoregressions

      • Joshua C. C. Chan

      Pages 95-125

   5. Volatility Forecasting in a Data Rich Environment

      • Mauro Bernardi, Giovanni Bonaccolto, Massimiliano Caporin, Michele Costola

      Pages 127-160

   6. Neural Networks

      • Thomas R. Cook

      Pages 161-189

4. Seeking Parsimony

   1. Front Matter

      Pages 191-191

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   2. Penalized Time Series Regression

      • Anders Bredahl Kock, Marcelo Medeiros, Gabriel Vasconcelos

      Pages 193-228

   3. Principal Component and Static Factor Analysis

      • Jianfei Cao, Chris Gu, Yike Wang

      Pages 229-266

   4. Subspace Methods

      • Tom Boot, Didier Nibbering

      Pages 267-291

   5. Variable Selection and Feature Screening

      • Wanjun Liu, Runze Li

      Pages 293-326

5. Dealing with Model Uncertainty

   1. Front Matter

      Pages 327-327

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   2. Frequentist Averaging

      • Felix Chan, Laurent Pauwels, Sylvia Soltyk

      Pages 329-357

   3. Bayesian Model Averaging

      • Paul Hofmarcher, Bettina Grün

      Pages 359-388

   4. Bootstrap Aggregating and Random Forest

      • Tae-Hwy Lee, Aman Ullah, Ran Wang

      Pages 389-429

   5. Boosting

      • Jianghao Chu, Tae-Hwy Lee, Aman Ullah, Ran Wang

      Pages 431-463

   6. Density Forecasting

      • Federico Bassetti, Roberto Casarin, Francesco Ravazzolo

      Pages 465-494

This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.

• Big Data
• Macroeconomic forecasting
• Dimension reduction
• Shrinkage
• Model forecast combination
• Dynamic factor models
• Vector autoregressions
• Mixed frequency data sampling regressions
• Estimation of common factors
• Penalized regression
• Variable selection
• Feature screening
• Subspace methods
• Averaging
• Aggregation
• Unit roots
• Cointegration
• Forecasts
• Time varying parameters

• UHERO and Department of Economics, University of Hawaii at Manoa, Honolulu, USA

  Peter Fuleky

Peter Fuleky is an Associate Professor of Economics with a joint appointment at the University of Hawaii Economic Research Organization (UHERO), and the Department of Economics at the University of Hawaii at Manoa. His research focuses on econometrics, time series analysis, and forecasting. He is a co-author of UHERO's quarterly forecast reports on Hawaii's economy. He obtained his Ph.D. degree in Economics at the University of Washington, USA.

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