Welcome to my website
I work on models for panel data with unobserved dynamic clustering structures, and their applications to finance. See here for my publications and work in progress. In my dissertation, I develop clustering techniques to solve specific problems emerging from empirical applications related to how noisy data produces inconsistent clustering. I approach solutions both from an econometric perspective, using score-driven models, and from machine learning techniques combined and adapted to smooth out undesirable features.
I refine the test for clustering of Patton and Weller (2022) to allow for cluster switching. In a multivariate panel setting, clustering on time-averages produces consistent estimators of means and group assignments. Once switching is introduced, we lose the consistency. In fact, under switching the time-averaged k-means clustering converges to equal, indistinguishable means. This causes the test for a single cluster to lose power under the alternative of multiple clusters. Power can be regained by clustering the N times T observations independently and carefully subsampling the time dimension. When applied to the empirical setting of Bonhomme and Manresa (2015) of an autoregression of democracy in a panel of countries, we are able to detect clusters in the data under noisier conditions than the original test.
You can reach me at firstname.lastname@example.org.
I am on the 2023/24 Job Market. My institution’s placement director is Eric Bartelsman (email@example.com).