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Empirical research on time-varying characteristics and efficiency of the Chinese economy and monetary policy: evidence from the MI-TVP-VAR model

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  • Jian-qing Zhang
  • Tingting Chen
  • Fei Fan
  • Song Wang

Abstract

Based on the general time-varying parameter vector autoregressive model and data mining technology, this study proposes a new extension mixed innovation time-varying parameter stochastic volatility vector autoregressive model and investigates time-varying characteristics and efficiencies of different shock effects on China’s monetary policy towards inflation and GDP. Using sample monthly data for 1979–2014, we utilize typical time points to illustrate the mechanisms between different economic variables via the Markov Chain Monte Carlo method and impulse response function. The empirical results show that the monetary transmission mechanism in China can be effective in the real economy, but with delay and efficiency leakage. The average delay and maximum efficiency can be measured through the MI model, which can capture accurate information of economic variables, effectively improving the precision of macroeconomic regulation and control. Meanwhile, the difference between the impacts of different channels is obvious; while the impact of interest rates is not significant, the impact of stock market is significant. The action mechanism between GDP and the inflation rate undergoes a gradual structural change, evidently displaying time-varying characteristics and a gradually weakening impact over time.

Suggested Citation

  • Jian-qing Zhang & Tingting Chen & Fei Fan & Song Wang, 2018. "Empirical research on time-varying characteristics and efficiency of the Chinese economy and monetary policy: evidence from the MI-TVP-VAR model," Applied Economics, Taylor & Francis Journals, vol. 50(33), pages 3596-3613, July.
  • Handle: RePEc:taf:applec:v:50:y:2018:i:33:p:3596-3613
    DOI: 10.1080/00036846.2018.1430338
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    Cited by:

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    13. Deqing Wang & Yinqiu Song & Hongyan Zhang & Shengjie Pan, 2020. "The Effectiveness of Chinas Monetary Policy: Based on the Mixed-Frequency Data," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(3), pages 325-339, March.
    14. Jianwei Zhang & Heng Li & Guoxin Jiao & Jiayi Wang & Jingjing Li & Mengzhen Li & Haining Jiang, 2022. "Spatial Pattern of Technological Innovation in the Yangtze River Delta Region and Its Impact on Water Pollution," IJERPH, MDPI, vol. 19(12), pages 1-20, June.
    15. Min Qian & Zhenpeng Cheng & Zhengwen Wang & Dingyi Qi, 2022. "What Affects Rural Ecological Environment Governance Efficiency? Evidence from China," IJERPH, MDPI, vol. 19(10), pages 1-19, May.
    16. Wenyi Yang & Xueli Wang & Keke Zhang & Zikan Ke, 2020. "COVID-19, Urbanization Pattern and Economic Recovery: An Analysis of Hubei, China," IJERPH, MDPI, vol. 17(24), pages 1-21, December.
    17. Juan Hu & Chengjin Ma & Chen Li, 2022. "Can Green Innovation Improve Regional Environmental Carrying Capacity? An Empirical Analysis from China," IJERPH, MDPI, vol. 19(20), pages 1-15, October.
    18. Le Zhang & Qinyi Gu & Chen Li & Yi Huang, 2022. "Characteristics and Spatial–Temporal Differences of Urban “Production, Living and Ecological” Environmental Quality in China," IJERPH, MDPI, vol. 19(22), pages 1-22, November.
    19. Chen Li & Heng Li & Xionghe Qin, 2022. "Spatial Heterogeneity of Carbon Emissions and Its Influencing Factors in China: Evidence from 286 Prefecture-Level Cities," IJERPH, MDPI, vol. 19(3), pages 1-29, January.
    20. Ning Ma & Puyu Liu & Yadong Xiao & Hengyun Tang & Jianqing Zhang, 2022. "Can Green Technological Innovation Reduce Hazardous Air Pollutants?—An Empirical Test Based on 283 Cities in China," IJERPH, MDPI, vol. 19(3), pages 1-20, January.

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