ENGLISH

Macroeconomic Forecasting In The Era Of Big Data: Theory And Practice

Book information

Publisher
Springer
Year
2020
ISBN
303031149X, 9783030311490, 9783030311506
Language
english
Format
PDF
Filesize
11 MB (11850330 bytes)
Series
Advanced Studies In Theoretical And Applied Econometrics Vol. 52
Pages
716\716
Topic
Economy\\Econometrics
Time added
2019-11-28 20:38:58

Description

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. Front Matter ....Pages i-xiii Front Matter ....Pages 1-1 Sources and Types of Big Data for Macroeconomic Forecasting (Philip M. E. Garboden)....Pages 3-23 Front Matter ....Pages 25-25 Dynamic Factor Models (Catherine Doz, Peter Fuleky)....Pages 27-64 Factor Augmented Vector Autoregressions, Panel VARs, and Global VARs (Martin Feldkircher, Florian Huber, Michael Pfarrhofer)....Pages 65-93 Large Bayesian Vector Autoregressions (Joshua C. C. Chan)....Pages 95-125 Volatility Forecasting in a Data Rich Environment (Mauro Bernardi, Giovanni Bonaccolto, Massimiliano Caporin, Michele Costola)....Pages 127-160 Neural Networks (Thomas R. Cook)....Pages 161-189 Front Matter ....Pages 191-191 Penalized Time Series Regression (Anders Bredahl Kock, Marcelo Medeiros, Gabriel Vasconcelos)....Pages 193-228 Principal Component and Static Factor Analysis (Jianfei Cao, Chris Gu, Yike Wang)....Pages 229-266 Subspace Methods (Tom Boot, Didier Nibbering)....Pages 267-291 Variable Selection and Feature Screening (Wanjun Liu, Runze Li)....Pages 293-326 Front Matter ....Pages 327-327 Frequentist Averaging (Felix Chan, Laurent Pauwels, Sylvia Soltyk)....Pages 329-357 Bayesian Model Averaging (Paul Hofmarcher, Bettina Grün)....Pages 359-388 Bootstrap Aggregating and Random Forest (Tae-Hwy Lee, Aman Ullah, Ran Wang)....Pages 389-429 Boosting (Jianghao Chu, Tae-Hwy Lee, Aman Ullah, Ran Wang)....Pages 431-463 Density Forecasting (Federico Bassetti, Roberto Casarin, Francesco Ravazzolo)....Pages 465-494 Forecast Evaluation (Mingmian Cheng, Norman R. Swanson, Chun Yao)....Pages 495-537 Front Matter ....Pages 539-539 Unit Roots and Cointegration (Stephan Smeekes, Etienne Wijler)....Pages 541-584 Turning Points and Classification (Jeremy Piger)....Pages 585-624 Robust Methods for High-Dimensional Regression and Covariance Matrix Estimation (Marco Avella-Medina)....Pages 625-653 Frequency Domain (Felix Chan, Marco Reale)....Pages 655-687 Hierarchical Forecasting (George Athanasopoulos, Puwasala Gamakumara, Anastasios Panagiotelis, Rob J. Hyndman, Mohamed Affan)....Pages 689-719

Similar books