Identifying Macroeconomic Policy Shocks: High Frequency Meets NLP
Ongoing Work
Identification of Shocks
Textual Analysis
High frequency Identification
Ongoing Work: Download Paper
Abstract
How can one measure the dynamic causal impact of a policy announcement in the absence of price changes to serve as an instrument? To address this question, I propose a method that utilizes natural language processing (NLP) to construct an instrument from the textual content of policy announcements. Building on the High-Frequency Identification (HFI) framework, this approach captures the signal of an announcement as a function of its magnitude and sentiment, thus circumventing the reliance on observed price changes. Using this instrument,I find that carbon pricing behaves similarly to negative supply shocks. I further provide significant cross country spillover effects of these carbon pricing shocks.