https://orcid.org/0000-0001-8437-2933
![Sample our Politics & International Relations journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5947/TOC-Subject-Sample-2021-Politics-International-Relations.jpg"})
ABSTRACT
Central bank communication plays a crucial role in the conduct of monetary policy, yet the research on central bank communication, while growing, is still scarce. In this paper, we analyze the communication reaction function of the European Central Bank (ECB) through topic-based indices derived from the bank’s speeches. These indices are used as dependent variables in policy and communication reaction function models, as suggested by recent literature. The topics are extracted using Latent Dirichlet Allocation (LDA), a popular text mining algorithm for topic extraction. The ECB has recently reviewed its monetary policy strategy, which led to an increase in studies incorporating the new methods offered by text mining for analyzing the policy reaction function of the bank. We show how indices built through topic modelling can be used to study the communication reaction function of a central bank, and we examine which variables are significant for every topic communicated by the ECB.
Acknowledgements
We wish to thank Jaan Masso, Lenno Uusküla, Dmitry Kulikov, Alari Paulus, Karsten Staehr, Orsolya Soosaar, Michael Rose, and Anastasia Sinitsyna for their reviews and their valuable comments on previous versions of the paper. We are extremely grateful to the Research Division of the Bank of Estonia for the incredible help we received in completing this paper. We would also like to thank Shakshi Sharma and Rajesh Sharma for their contribution to solving important issues related to the paper. We are indebted to Philippine Cour-Thimann and Alexander Jung for sending us some of their data and providing very useful comments. Lastly, we want to thank our colleagues at the University of Tartu for their suggestions.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 The term n-grams in text analysis is used for two or more words that are consecutive and have a specific meaning in the texts. When n is 2, the sequence is called a bigram and when n is 3, the sequence is called a trigram.
2 The list is available upon request.
3 A typical score used in LDA as shown in Blei et al. (Citation2003). We specifically use the log-perplexity method provided by Python's gensim package.
4 As a coherence score, we employ the one suggested by Röder et al. (Citation2015). The perplexity score measures how well the natural language processing-based probability model can predict a sample. The perplexity score is the inverse probability of the test set normalized by the number of words in the same set. The lower the level of perplexity is, the better the model is. The coherence score meanwhile measures the degree of semantic similarity between the most important words in a specific topic, or in other words, how good a topic model is in generation topics that are coherent.
5 To construct we use the Mallet package in Python.
6 The models without TF-IDF use a standard document-term matrix with unigrams and bigrams (see ). The combination of LDA with the TF-IDF is tested to observe any possible increase in the precision of LDA in separating the topics.
7 More information on LDA can be found in Appendix 1.
8 Aggregating the speeches by quarter and directly creating quarterly indices leads to problems with the LDA in disentangling the topics, given the inferior number of documents available. Codes with the attempts at this approach are available on request.
9 We adapt the model to quarterly data. The differences in time arise from when the ECB gets new information on the independent variables.
10 The interactions are inserted in order to see potential asymmetries in the responses of ECB as observed in Hartmann and Smets (Citation2018). For the authors, the ECB appears to respond to anticipated slowdowns in growth by easing the monetary policy and anticipated inflation over its inflation target primarily by tightening the monetary policy.
11 Armelius et al. (Citation2020) show the importance of the FED as a ‘leading spillover generator’ among central banks through its communications. For Priola et al. (Citation2021) this leading role increased after the Great Recession.
12 See Mirkov and Natvik (Citation2016)
13 Pearson correlation estimations suggest a strong correlation between the EMUG and CMP topics (0.71). We observe that these topics were quite important in the same timeframe. Other variables have correlations smaller than 0.5. Results are available upon request.
14 Available upon request
15 Usually, the base outcome is the most frequent one. In our case, this is the CMP topic.
Additional information
Funding
Notes on contributors
Luca Alfieri
Luca Alfieri is a PhD student at the university of Tartu and PostDoc at Politecnico di Milano. His research interests include forecasting, time series analysts, machine learning applications in macroeconomics, and remote working.
Diana Gabrielyan
Diana Gabrielyan is a PhD student at the university of Tartu. Her research interests include inflation forecasting, time series analysts and machine learning applications in macroeconomics.