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Including news data in forecasting macro economic performance of China

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  • Asger Lunde, Copenhagen Economics
  • ,
  • Miha Torkar, J. Stefan Institute

In this work we predict changes in the Gross Domestic Product (GDP) of China using dynamic factor models. We report results of 3- and 6-months ahead forecasts, where we use 124 predictors from various sources and dates ranging from 2000 through 2017. Our analysis includes China specific macroeconomic time series data and a large number of predictor variables. We follow the latest state of the art, as outlined by, Stock and Watson (in: Handbook of macroeconomics vol 2, Elsevier, pp 415–525, 2016) who use principal component analysis (PCA) to reduce number of variables and apply dynamic factor model (DFM) to make predictions. The results suggest that including news sentiment significantly improves forecasts and this approach outperforms univariate autoregression. The contributions of this paper are two fold, namely, the use of news to improve forecasts and superior forecast of China’s GDP.

Original languageEnglish
JournalComputational Management Science
Pages (from-to)585-611
Number of pages27
Publication statusPublished - Dec 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

Copyright 2021 Elsevier B.V., All rights reserved.

    Research areas

  • Factor models, Macroeconomics, News, Principal component analysis, Sentiment

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