With the increasing size of financial assets and the complexity of monetary patterns, countries around the world are gradually becoming more transparent in their monetary policies, using central bank monetary policy communication as a new type of monetary policy instrument. To measure central bank communication more accurately, this paper proposes a dynamic topic model, LDA-BP, based on branching processes, to construct the central bank communication index. At the same time, this paper does four things: it uses the constructed communication index as a proxy variable for the new monetary policy instrument; it builds a TVP-FAVAR model that can extract potential macroeconomic information from many variables, and its time-varying nature can better reflect the dynamic regulatory effect of monetary policy; it constructs a three-dimensional impulse response diagram; and it conducts a systematic analysis of macroeconomic impact. The experimental results of demonstrate its effectiveness on central bank monetary policy communication, as it captures timely information about conventional monetary policy instruments and immediately responds to changes in interest rates and money supply. All three monetary policy instruments are effective in smoothing output volatility, with monetary policy communication having a longer-term impact on the macroeconomy.

1. [1] Z. Ji and X. Zhou, “May central bank communication work as a monetary policy Tool?,” Study of Int. Finance, Vol.2011, No.2, pp. 25-34, 2011 (in Chinese).
2. [2] N. Jegadeesh and D. Wu, “Deciphering Fedspeak: The Information Content of FOMC Meetings,” ASSA 2016 Annual Meeting, 2015.
3. [3] J.-H. Lin, W.-Q. Zhao, and Z.-D. Li, “Monetary policy effects of central bank communication and actual intervention in frequency domains,” J. of Management Sciences in China, Vol.20, No.8, pp. 27-38, 2017 (in Chinese). https://doi.org/10.3969/j.issn.1007-9807.2017.08.003
4. [4] B. P. Tiberto, C. O. de Moraes, and P. P. Corrêa, “Does transparency of central banks communication affect credit market? Empirical evidence for advanced and emerging markets,” The North American J. of Economics and Finance, Vol.53, Article No.101207, 2020. https://doi.org/10.1016/j.najef.2020.101207
5. [5] Y. Xu, W. Tang, and B. Zhang, “Analysis of the expectation guidance effect of China’s central bank communication in the context of weakening expectations—based on the confidence expectation channel,” Wuhan Finance, Vol.2022, No.7, pp. 3-11, 2022 (in Chinese).
6. [6] M. Liu and S. Lv, “Does central bank communication affect firm financial constraints?—Evidence from bank credit channel,” The J. of Quantitative Economics, Vol.13, No.2, pp. 137-158, 2022 (in Chinese). https://doi.org/10.16699/b.cnki.jqe.2022.02.005
7. [7] X. Du et al., “Does central bank communication on financial stability work?—An empirical study based on Chinese stock market,” Int. Review of Economics & Finance, Vol.85, pp. 390-407, 2023. https://doi.org/10.1016/j.iref.2023.02.003
8. [8] J. Lin et al., “The stock market effect of the central bank governors’ oral communications,” J. of Management Science in China, Vol.26, No.2, pp. 66-86, 2023 (in Chinese). https://doi.org/10.19920/j.cnki.jmsc.2023.02.005
9. [9] C. Li, H. Yu, and Y. Li, “An empirical study of the impact of monetary policy uncertainty on macroeconomics,” Statistics & Decision, Vol.37, No.18, pp. 128-132, 2021 (in Chinese). https://doi.org/10.13546/j.cnki.tjyjc.2021.18.029
10. [10] C. Deng and Y. Wang, “An Analysis of financial and business cycle fluctuation and coordinated regulation of fiscal and monetary policy in China,” Contemporary Finance & Economics, Vol.2022, No.11, pp. 3-15, 2022 (in Chinese). https://doi.org/10.13676/j.cnki.cn36-1030/f.2022.11.009
11. [11] C. Rosa and G. Verga, “On the consistency and effectiveness of central bank communication: Evidence from the ECB,” European J. of Political Economy, Vol.23, No.1, pp. 146-175, 2007. https://doi.org/10.1016/j.ejpoleco.2006.09.016
12. [12] M. Picault and T. Renault, “Words are not all created equal: A new measure of ECB communication,” J. of Int. Money and Finance, Vol.79, pp. 136-156, 2017. https://doi.org/10.1016/j.jimonfin.2017.09.005
13. [13] J. Lin and W. Zhao, “Measurement and spectral analysis of China’s central bank communication,” Statistical Research, Vol.32, No.1, pp. 52-58, 2015 (in Chinese). https://doi.org/10.19343/j.cnki.11-1302/c.2015.01.008
14. [14] S. Hansen and M. McMahon, “Shocking language: Understanding the macroeconomic effects of central bank communication,” J. of Int. Economics, Vol.99, Suppl.1, pp. S114-S133, 2016. https://doi.org/10.1016/j.jinteco.2015.12.008
15. [15] Z. Bai, L. Yang, and Y. Miao, “Is the central bank communication efficient? – A computational linguistics approach,” The J. of Quantitative Economics, Vol.10, No.1, pp. 32-48, 2019 (in Chinese). https://doi.org/10.16699/b.cnki.jqe.2019.01.004
16. [16] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” The J. of Machine Learning Research, Vol.3, pp. 993-1022, 2003.
17. [17] C. Di and Y. Wu, “The influence of monetary policy regulation of the central bank on the stock market—based on the comparison between the behavior index and communication index,” Financial Regulation Research, Vol.2021, No.3, pp. 29-47, 2021 (in Chinese). https://doi.org/10.13490/j.cnki.frr.2021.03.002
18. [18] B. S. Bernanke and K. N. Kuttner, “What explains the stock market’s reaction to Federal Reserve policy?,” The J. of Finance, Vol.60, No.3, pp. 1221-1257, 2005. https://doi.org/10.1111/j.1540-6261.2005.00760.x
19. [19] J. G. Fernald, M. M. Spiegel, and E. T. Swanson, “Monetary policy effectiveness in China: Evidence from a FAVAR model,” J. of Int. Money and Finance, Vol.49, Part A, pp. 83-103, 2014. https://doi.org/10.1016/j.jimonfin.2014.05.007
20. [20] J. Dai and D. Liu, “The dynamic effectiveness of China’s monetary policy: An empirical analysis based on the TVP-SV-FAVAR model,” World Economy Studies, Vol.2016, No.12, pp. 12-24+132, 2016 (in Chinese). https://doi.org/10.13516/j.cnki.wes.2016.12.003