Are Regional Housing Markets at Risk after Tornadoes? A Panel Quantile Local Projection Approach (draft under review for submission, will be available shortly)

(Job market paper for 2024-2025 academic market)

The paper investigates the dynamic relationship between tornado outbreaks and regional housing market uncertainty in the United States, using a novel methodological approach that integrates panel quantile regression with two-way fixed effects and local projection techniques. Specifically, it analyzes the dispersion between tail conditional quantiles and central quantiles in the predicted house price index growth distribution within each Metropolitan Statistical Area (MSA). The findings reveal that weaker tornadoes are predominantly associated with decreased housing demand, while stronger and more destructive tornadoes are more linked to decreased housing supply. Furthermore, the results show that these strong tornadoes are associated with increased dispersion of the regional house price growth distributions, signaling heightened uncertainty in these markets. This underscores the potential for extreme weather events, particularly tornadoes intensified by climate change, to raise uncertainty levels in regional housing markets, highlighting the need for targeted policy interventions to maintain market stability.

Monetary Policy and (Dis-)Inflationary Risk: A Quantile Local Projection Approach (with Ji Hyung Lee) (draft under review for submission, will be available shortly)

This paper examines the impact of monetary policy shocks on inflationary risks in the United States using a quantile local projection approach. We focus on the predictive effects of contractionary monetary policy on the distribution of future inflation, with Inflation-at-Risk (IaR) used to measure the tail behavior of inflation rates. Our key findings show that inflationary risks increase following a positive monetary policy shock, with the most pronounced effects observed in the lower tail of the inflation distribution, indicating heightened disinflationary or deflationary risks. These results are robust across multiple empirical specifications, reinforcing the importance of considering the broader distributional impacts of monetary policy beyond the mean. Based on these findings, we suggest caution in adopting overly aggressive tightening policies, as they may exacerbate downside risks to inflation, with potential implications for economic stability.

Forecasting the Distribution of Inflation Expectations: Machine Learning Applications (with Humberto Martinez-Garcia)

Rate Shocks and Risk Rebalancing: Evidence from the Yen Carry Trade (with Keitaro Ninomiya)

Systemic and Individual Risks: How Do They Respond to Monetary Policy?

Closer Look at Flight-to-Safety: A Mixed-Frequency VAR Approach