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Including news data in forecasting macro economic performance of China

Author

Listed:
  • Asger Lunde

    (Copenhagen Economics
    Aarhus University)

  • Miha Torkar

    (Jozef Stefan Institute
    Jozef Stefan International Postgraduate School)

Abstract

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 $$124$$ 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.

Suggested Citation

  • Asger Lunde & Miha Torkar, 2020. "Including news data in forecasting macro economic performance of China," Computational Management Science, Springer, vol. 17(4), pages 585-611, December.
    • Handle: RePEc:spr:comgts:v:17:y:2020:i:4:d:10.1007_s10287-020-00382-5
    DOI: 10.1007/s10287-020-00382-5
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