Predicting China’s Monetary Policy with Forecast Combinations
Access status:
Open Access
Type
Working PaperAuthor/s
Pauwels, LaurentAbstract
China’s monetary policy is unconventional and constantly evolving as a result of its rapid economic development. This paper proposes to use forecast combinations to predict the People’s Bank of China’s monetary policy stance with a large set of 73 macroeconomic and financial ...
See moreChina’s monetary policy is unconventional and constantly evolving as a result of its rapid economic development. This paper proposes to use forecast combinations to predict the People’s Bank of China’s monetary policy stance with a large set of 73 macroeconomic and financial predictors covering various aspects of China’s economy. The multiple instruments utilised by the People’s Bank of China are aggregated into a Monetary Policy Index (MPI). The intention is to capture the overall monetary policy stance of the People’s Bank of China into a single variable that can be forecasted. Forecast combination assign weights to predictors according to their forecasting performance to produce a consensus forecast. The out-of-sample forecast results demonstrate that optimal forecast combinations are superior in predicting the MPI over other models such as the Taylor rule and simple autoregressive models. The corporate goods price index and the US nominal effective exchange rate are the most important predictors.
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See moreChina’s monetary policy is unconventional and constantly evolving as a result of its rapid economic development. This paper proposes to use forecast combinations to predict the People’s Bank of China’s monetary policy stance with a large set of 73 macroeconomic and financial predictors covering various aspects of China’s economy. The multiple instruments utilised by the People’s Bank of China are aggregated into a Monetary Policy Index (MPI). The intention is to capture the overall monetary policy stance of the People’s Bank of China into a single variable that can be forecasted. Forecast combination assign weights to predictors according to their forecasting performance to produce a consensus forecast. The out-of-sample forecast results demonstrate that optimal forecast combinations are superior in predicting the MPI over other models such as the Taylor rule and simple autoregressive models. The corporate goods price index and the US nominal effective exchange rate are the most important predictors.
See less
Date
2019-05-14Publisher
Business Analytics.Department, Discipline or Centre
Discipline of Business Analytics, The University of Sydney Business SchoolShare