Forecasting models for the Chinese macroeconomy: the simpler the better?

Chris Heaton*, Natalia Ponomareva, Qin Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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We consider the problem of macroeconomic forecasting for China. Our objective is to determine whether well-established forecasting models that are commonly used to compute forecasts for Western macroeconomies are also useful for China. Our study includes 19 different forecasting models, ranging from simple approaches such as the naive forecast to more sophisticated techniques such as ARMA, Bayesian VAR, and factor models. We use these models to forecast two different measures of price inflation and two different measures of real activity, with forecast horizons ranging from 1 to 12 months, over a period that stretches from March 2005 to December 2018. We test null hypotheses of equal mean squared forecasting error between each candidate model and a simple benchmark. We find evidence that AR, ARMA, VAR, and Bayesian VAR models provide superior 1-month-ahead forecasts of the producer price index when compared to simple benchmarks, but find no evidence of superiority over simple benchmarks at longer horizons, or for any of our other variables.

Original languageEnglish
Pages (from-to)139-167
Number of pages29
JournalEmpirical Economics
Issue number1
Early online date7 Nov 2019
Publication statusPublished - Jan 2020

Bibliographical note

Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • China
  • Electricity production
  • Forecasting
  • Industrial production
  • Inflation
  • Real activity


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