Appendix to Quantitative Easing and Tapering Uncertainty: Evidence from Twitter

Transcription

1 Appendix to Quantitative Easing and Tapering Uncertainty: Evidence from Twitter Annette Meinusch Justus-Liebig-University Gießen, Germany Peter Tillmann Justus-Liebig-University Gießen, Germany November, 26 This appendix contains additional material, mostly about the robustness of the results. It is not meant for publication but will be made available on our website. In order to shed some light on the distribution of Twitter users and their written messages, Figure () illustrates the distribution of tweets generated by different senders on the log scale. We plot the (log) number of users on the ordinate versus the ranked (log) number of tweets on the abscissa. Obviously, the resulting graph resembles a Zipf-like distribution, indicating that a small number of active users frequently share their opinions about the Fed s future policy stance and that a large number of users generate tweets about Fed tapering rather infrequently. Excluding retweets does not change the distributional properties of the data. forthcoming in the International Journal of Central Banking. annette.meinusch@wirtschaft.uni-giessen.de peter.tillmann@wirtschaft.uni-giessen.de

2 Figure : Distribution of tweets 9 8 log (frequency of users) log (number of tweets) Notes: Distribution of all tweets (black) and all tweets excluding retweets (gray) and users in log scale. In Figure (2) we include day-of-the-week dummies to account for seasonality in the data. This is important as there might be elevated Twitter activity after the weekend or on Wednesdays and Thursdays when most central banks meet. Since the presented impulse responses only differ marginally from a model without those dummies we abstain from using them for other model specifications. 2

3 Figure 2: Impulse responses to shock to tapering soon belief: with day-of-the-week dummies Impulse Response for Rate () Notes: The solid line is the median impulse response function. The dotted line is the Fry-Pagan (2) median-target impulse response. The confidence band reflects the 6 th and 84 th percentiles of all accepted draws. Figure (3) depicts impulse responses to a tapering soon belief shock which is identified by a Cholesky decomposition. Here, the belief data is ordered first. In fact, an unanticipated shock to tapering soon beliefs leads to similar responses as the identification with sign restrictions. Figure (4) reports the responses for a model that includes Federal Funds futures instead of the -year bond yields. The results show a fall in Future as a response to a tapering soon shock, which is consistent with an increase in the expected Federal Funds rate. Figure () presents the historical decomposition of the VAR model with Federal Funds futures for the tapering soon shock. The shock contributes to the fall in futures around the June and the September FOMC meeting. 3

4 Figure 3: Impulse responses to shock to tapering soon belief shock: with Cholesky identification Impulse Response for Rate () Notes: The beliefs are ordered first. The confidence band reflects the 6 th and 84 th percentiles of all draws. Figure 4: Impulse responses to shock to tapering soon belief shock: Federal Funds futures instead of bond yields x 3. Impulse Response for Futures Notes: The solid line is the median impulse response function. The dotted line is the Fry-Pagan (2) median-target impulse response. The confidence band reflects the 6 th and 84 th percentiles of all accepted draws. 4

5 Figure : Historical decomposition for tapering soon belief shock: Federal Funds futures instead of bond yields x 3 Futures.2. Apr 8 May 2 Jun 23 Jul 2 Aug 27 Sep 3 Oct USD/EUR Apr 8 May 2 Jun 23 Jul 2 Aug 27 Sep 3 Oct Notes: Contribution of tapering soon belief shock (left scale) and observable asset prices (right scale). The shaded areas indicate FOMC meetings.