Macroeconomic Forecasting in the Era of Big Data

Theory and Practice

  • Peter Fuleky

Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 52)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Introduction

    1. Front Matter
      Pages 1-1
  3. Capturing Dynamic Relationships

    1. Front Matter
      Pages 25-25
    2. Catherine Doz, Peter Fuleky
      Pages 27-64
    3. Martin Feldkircher, Florian Huber, Michael Pfarrhofer
      Pages 65-93
    4. Joshua C. C. Chan
      Pages 95-125
    5. Mauro Bernardi, Giovanni Bonaccolto, Massimiliano Caporin, Michele Costola
      Pages 127-160
    6. Thomas R. Cook
      Pages 161-189
  4. Seeking Parsimony

    1. Front Matter
      Pages 191-191
    2. Anders Bredahl Kock, Marcelo Medeiros, Gabriel Vasconcelos
      Pages 193-228
    3. Jianfei Cao, Chris Gu, Yike Wang
      Pages 229-266
    4. Tom Boot, Didier Nibbering
      Pages 267-291
    5. Wanjun Liu, Runze Li
      Pages 293-326
  5. Dealing with Model Uncertainty

    1. Front Matter
      Pages 327-327
    2. Felix Chan, Laurent Pauwels, Sylvia Soltyk
      Pages 329-357
    3. Paul Hofmarcher, Bettina Grün
      Pages 359-388
    4. Tae-Hwy Lee, Aman Ullah, Ran Wang
      Pages 389-429
    5. Jianghao Chu, Tae-Hwy Lee, Aman Ullah, Ran Wang
      Pages 431-463
    6. Federico Bassetti, Roberto Casarin, Francesco Ravazzolo
      Pages 465-494
    7. Mingmian Cheng, Norman R. Swanson, Chun Yao
      Pages 495-537
  6. Further Issues

    1. Front Matter
      Pages 539-539
    2. Stephan Smeekes, Etienne Wijler
      Pages 541-584
    3. Jeremy Piger
      Pages 585-624
    4. Felix Chan, Marco Reale
      Pages 655-687
    5. George Athanasopoulos, Puwasala Gamakumara, Anastasios Panagiotelis, Rob J. Hyndman, Mohamed Affan
      Pages 689-719

About this book


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

Editors and affiliations

  • Peter Fuleky
    • 1
  1. 1.UHERO and Department of EconomicsUniversity of Hawaii at ManoaHonoluluUSA

Bibliographic information