The Information Channel of Monetary Policy Shocks - Insights from Text Analysis
Measures of monetary shocks commonly give rise to the puzzling result where a monetary tightening has an expansionary effect. A possible reason is that agents may believe that monetary shocks contain information regarding the central bank’s assessment of the economic environment (Nakamura and Steinsson, 2018). Under this hypothesis, the estimated response to monetary policy shocks would contain two conflating effects: the actual effect of monetary policy and the reaction of private agents to the newly acquired information. This paper overcomes this problem by extracting a novel series of monetary shocks using text analysis methods on central bank documents. The resulting text-based variables contain the informational content from changes in the policy rate. Thus, they can be used to extract exogenous changes in monetary policy that are orthogonal to any central bank information. Using this information-free measure of monetary policy shocks reveals that a monetary tightening is not expansionary, even when estimated on more recent periods.
State-Dependent Macroeconomic Policy Effects: A Varying-Coefficient VAR
with C. Rörig (University of Cambridge)
This article proposes a flexible framework to identify state-dependent effects of macroeconomic policies. In the literature, it is common to either estimate constant policy effects or introduce state-dependency in a parametric fashion. This, however, demands prior assumptions about the functional form. Our new method allows identifying state-dependent effects and possible interactions in a data-driven way. Specifically, we estimate heterogeneous policy effects using semi-parametric varying-coefficient models in an otherwise standard VAR structure. While keeping a parametric reduced form for interpretability and efficiency, we estimate the coefficients as functions of modifying macroeconomic variables, using random forests as the underlying non-parametric estimator. Simulation studies show that this method correctly identifies multiple states even for relatively small sample sizes. To further validate our method, we apply the semi-parametric framework to the historical data set by Ramey & Zubairy and offer a more granular perspective on the dependence of the fiscal policy efficacy on unemployment and interest rates.
Work in Progress
- Double Machine Learning for Time-Series in Macroeconomics (with C. Rörig, University of Cambridge)
We apply double machine learning (DML) (Chernozhukov et al. (2018) to a panel setting with limited data available on the time dimension. Due to a limited number of observations, our efforts are focused on improving the efficiency of the DML methodology. Furthermore, in simulation studies, we show that our approach is able to account for autocorrelation in time-series data.
- The Effect of Climate Risk on Sovereign Bond Spreads (with Z. Zhan, IMF)
We develop a newspaper-based index to assess the effect of climate change risk on bond yields. Since our new climate-change index is available on a high-frequency, fixed effects can be used to implicitly control for slow-moving macroeconomic variables that are often not available for developing countries. Panel estimates show that climate change risks increase bond yields. This effect is even more pronounced in developing countries.