Abstract
In this paper we report results of 3- and 6-months ahead forecasts of Gross Domestic Product (GDP) of China. In total we use 124 predictors from various sources and dates ranging from 2000 through 2017. We use China specific macroeconomic time series data and a large number of predictor variables. In our study we follow the latest state of the art, as outlined by, [Stock and Watson, 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.
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