Including News Data in Forecasting Macro Economic Performance of China

22 Pages Posted: 20 Nov 2019

See all articles by Asger Lunde

Asger Lunde

Aarhus University - School of Business and Social Sciences; CREATES

Miha Torkar

Jožef Stefan Institute; Jožef Stefan Institute - International Postgraduate School

Date Written: September 30, 2019

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.

Keywords: Macroeconomics,News,Sentiment,Factor Models,Principal Component Analysis

JEL Classification: E01

Suggested Citation

Lunde, Asger and Lunde, Asger and Torkar, Miha, Including News Data in Forecasting Macro Economic Performance of China (September 30, 2019). Available at SSRN: https://ssrn.com/abstract=3481066 or http://dx.doi.org/10.2139/ssrn.3481066

Asger Lunde (Contact Author)

Aarhus University - School of Business and Social Sciences ( email )

Aarhus
Denmark

CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

Miha Torkar

Jožef Stefan Institute ( email )

Jamova cesta 39
Ljubljana, 1000
Slovenia

Jožef Stefan Institute - International Postgraduate School ( email )

Jamova 39
Ljubljana, SI-1000
Slovenia

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