Using Word Embedding to Reveal Monetary Policy Explanation Changes (2021)

Abstract: Documents have been an essential tool of communication for governments to announce their policy operations. Most policy announcements have taken the form of text to inform their new policies or changes to the public. To understand such policymakers’ communication, many researchers exploit published policy documents. However, the methods well-used in other research domains such as sentiment analysis or topic modeling are not suitable for studying policy communications. Their training corpora and methods are not for policy documents where technical terminologies are used, and sentiment expressions are refrained. We leverage word embedding techniques to extract semantic changes in the monetary policy documents. Our empirical study shows that the policymaker uses different semantics according to the type of documents when they change their policy.

## introduction disentangling how policymakers explain their policy is crucial to understand how policymakers supported by democracy uphold their responsibility. however, extracting such abstract information from policy documents is challenging. first of all, policy documents are often crabbedly written with technical terminologies, meaning that machine learning models trained on general corpora may not work well. in addition, popular text mining techniques for social science such as sentiment analysis or topic modeling may not be applicable. it would be a strong assumption, for example, that changes in policy explanation reflect its sentiments. even if we observed topic changes in documents during policy changes, it only tells "what" was explained but does not disclose "how" policy explanations changed. this paper proposes a word embedding-based framework for understanding how policymakers change their explanation when they change their policies. our framework trains the word embedding model only on policy documents, which allows us to resolve the aforementioned challenges by defining the semantic of the policy words solely on the policy documents. we use our approach to study the monetary policy operation by the federal reserve bank (frb) in the united states. our framework reveals how the policymaker explains their policy change in the policy documents and finds that different policy documents have a different role in policy change explanation even though the same policymaker publishes these documents. we also find that our word embedding-based framework can be used to predict policy changes. we summarize our framework and study design at figure 1 . ## results this section reports the results of the impulse response analysis, and the prediction analysis. ## discussion and conclusion this paper has presented the framework to study the policy explanation using the published policy documents. we utilized the word embedding technique to extract the semantic changes of policyrelated words. our framework provided the interpretable analysis, calculating the semantics between the policy words and the reference words with explicit semantics. to conduct the empirical analysis with our framework, we used the dataset from the central bank in the united states (frb), where the federal law requires them to publish the policy documents regularly to keep their policy's transparency, and their policy behavior is observable qualitative by their policy rate (the federal funds rate). our simulation results with the estimated time series model well used for finance (var model) revealed that the policy authority (frb) uses different documents to explain their policy changes depending on the phase of their policy. we also conducted the prediction analysis of the policy behavior to study the performance of the different time series models and language models. even for the prediction, our proposed framework showed a better performance than the others. those results may suggest that the proposed framework provides not only interpretable results but also a better predictor. while we found the policy explanation's semantics dynamics, some issues remain that further research needs to address. first, we need to expand our dataset and prove that our results are consistent. for example, we could conduct the same analysis for other central banks such as the bank of england, european central bank, or the bank of japan. such a comparative study is feasible because our embedding method do not depend on a specific language. moreover, our idea does not depend on specific word embedding model. our paper's central argument is that we should not rely on the language features that do not directly capture the policy explanation. therefore, if there is a method that captures the semantic shift of a given set of words in more sophisticated or robust way, we could apply our idea to that method.