NDLI: Inflation prediction for China based on the Grey Markov model?

Content Provider	IEEE Xplore Digital Library
Author	Xiaoyan Chen Keshen Jiang Yong Liu
Copyright Year	2015
Description	Author affiliation: Coll. of Econ. & Manage., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China (Xiaoyan Chen; Keshen Jiang) \|\| Sch. of Bus., Jiangnan Univ., Wuxi, China (Yong Liu)
Abstract	In order to solve the inflation forecasting problem with small samples and inherent uncertainty, this paper employs the Grey Markov model for inflation prediction by using the annual data from the year of 2005 to 2013. In contrast, the traditional econometric regression models are invalid for the small sample because the estimator of coefficients lose the BLUE properties under the small sample circumstances. Based on the model, the forecasted values are given for the years of 2014 to 2017. The result indicates that the expected price of the economy will experience slow growth for the next three years, then change to high inflation for the year of 2017. Further, the result implies the increase of the price level and the decrease of the natural level of output through the channel of aggregate supply and aggregate demand. From the view of policy, the government should employ the mix expansion of fiscal policy and monetary policy in order to eliminate the fluctuation in output. Specifically for the year of 2017, the government should pay more attention to the increase of the price level besides improving the output. As a result the policy would change to be more prudent.
Sponsorship	IEEE
Starting Page	301
Ending Page	306
File Size	486202
Page Count	6
File Format	PDF
ISBN	9781479983742
e-ISBN	9781479983759
DOI	10.1109/GSIS.2015.7301873
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-18
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Irrigation price expectation Grey Markov model Biological system modeling GM(1,1) model Predictive models Random processes inflation prediction
Content Type	Text
Resource Type	Article

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