Are CBRT’s Monetary Policy Statements Affected by ECB and FED Statements?

Year 2020, Volume: 14 Issue: 2, 205 - 226, 08.12.2020

Necmettin Alpay Koçak

https://doi.org/10.46520/bddkdergisi.841216

Abstract

This paper aims to examine whether CBRT’s monetary policy statements are affected by ECB and FED monetary policy statements. The sentiment analysis is performed to build the balance sentiment indicators (BSI) for CBRT, ECB, and FED from 2008 January to 2020 August using by their monetary policy statements. Individual and group properties of monthly BSI’s are examined by unit root tests and the Bounds cointegration tests. The short and long-run effects of ECB and
FED sentiments on CBRT’s sentiments are investigated by ARDL and UECM models. The analysis is applied over the full sample period and two sub-samples which they represent the periods before and after 2013 May. That date is an important for Turkey in terms of the effects of the Fed stimulus program as well as relations with the IMF. The results imply that the CBRT, ECB, and FED’s BSI’s have statistically significant cointegration relationship over the full sample period and the period
after 2013 June. Particularly after June 2013, the CBRT’s statements are positively related with the ECB’s and FED’s statements in the long run however CBRT’s statements are negatively affected by FED’s statements in the short-run.

Keywords

Communication, Central bank, Monetary policy statements, Sentiment analysis, Cointegration

References

• 1. Armelius, H., Bertsch, C., Hull, I., & Zhang, X. (2020). Spread the Word: International spillovers from central bank communication. Journal of International Money and Finance, 103, 102116.
• 2. Bårdsen, G. (1989). Estimation of long run coefficients in error correction models. Oxford Bulletin of Economics and Statistics, 51(3), 345-350.
• 3. Bec, F., Salem, M. B., & Collard, F. (2002). Asymmetries in monetary policy reaction function: evidence for US French and German central banks. Studies in Nonlinear Dynamics & Econometrics, 6(2).
• 4. Berger, H., De Haan, J., & Sturm, J. E. (2011). Does money matter in the ECB strategy? New evidence based on ECB communication. International Journal of Finance & Economics, 16(1), 16-31.
• 5. Bernanke, B. S., & Reinhart, V. R. (2004). Conducting monetary policy at very low short-term interest rates. American Economic Review, 94(2), 85-90.
• 6. Bholat, D. (2015). Big data and central banks. Big Data & Society, 2(1).
• 7. Breusch, T. S. (1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17(31), 334-355.
• 8. Coenen, G., Ehrmann, M., Gaballo, G., Hoffmann, P., Nakov, A., Nardelli, S., ... & Strasser, G. (2017). Communication of Monetary Policy in Unconventional Times. ECB Working Paper. No. 2080.
• 9. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 1057-1072.
• 10. Elliott, G., Rothenberg, T. J., & James, H. (1996). Stock, 1996,“Efficient tests for an autoregressive unit root,”. Econometrica, 64(4), 813-836.
• 11. Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251-276.
• 12. Goldfeld, S. M., & Quandt, R. E. (1965). Some tests for homoscedasticity. Journal of the American statistical Association, 60(310), 539-547.
• 13. Gujarati, D. N. (2009). Basic econometrics. Tata McGraw-Hill Education.
• 14. Hansen, S., & McMahon, M. (2016). Shocking language: Understanding the macroeconomic effects of central bank communication. Journal of International Economics, 99, S114-S133.
• 15. Hansen, S., McMahon, M., & Prat, A. (2018). Transparency and deliberation within the FOMC: a computational linguistics approach. The Quarterly Journal of Economics, 133(2), 801-870.
• 16. Harvey, A. C., & Collier, P. (1977). Testing for functional misspecification in regression analysis. Journal of Econometrics, 6(1), 103-119.
• 17. Henry, E. (2008). Are investors influenced by how earnings press releases are written?. The Journal of Business Communication (1973), 45(4), 363-407.
• 18. Hu, M., & Liu, B. (2004, August). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 168-177).
• 19. Iglesias, J., Ortiz, A., & Rodrigo, T. (2017). How do the emerging markets central bank talk? A big data approach to the Central Bank of Turkey (Vol. 24). BBVA Working Paper.
• 20. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration—with appucations to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169-210.
• 21. Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of economic dynamics and control, 12(2-3), 231-254.
• 22. Kahveci, E., & Odabaş, A. (2016). Central banks’ communication strategy and content analysis of monetary policy statements: The case of Fed, ECB and CBRT. Procedia-Social and Behavioral Sciences, 235, 618-629.
• 23. Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of econometrics, 54(1-3), 159-178.
• 24. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of Finance, 66(1), 35-65.
• 25. Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3), 436-465.
• 26. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics, 16(3), 289-326.
• 27. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
• 28. Rinker, T. W. (2016). sentimentr: Calculate text polarity sentiment. University at Buffalo/SUNY, Buffalo, New York. version 0.5, 3.
• 29. Siklos, P. L., & Wohar, M. E. (1997). Convergence in interest rates and inflation rates across countries and over time. Review of International Economics, 5(1), 129-141.
• 30. Stegmann, J. (2019). Federal Open Market Committee communication: a text mining analysis, unpublished Bachelor thesis.
• 31. Throop, A. W. (1994). International financial market integration and linkages of national interest rates. Economic Review-Federal Reserve Bank of San Francisco, (3), 3.
• 32. Young, L., & Soroka, S. (2012). Affective news: The automated coding of sentiment in political texts. Political Communication, 29(2), 205-231.
• 33. Zeileis, A. (2006). Implementing a class of structural change tests: An econometric computing approach. Computational Statistics & Data Analysis, 50(11), 2987-3008.
• 34. Zivot, E., & Andrews, D. W. K. (2002). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of business & economic statistics, 20(1), 25-44.