Research

Testing for Clustering Under Switching

This is my Job Market Paper. You can download it in PDF here, or see here for a presentation. 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.

Dynamic clustering of multivariate panel data

2023 with André Lucas, Julia Schaumburg and Bernd Schwaab. Journal of Econometrics.. Computer code available at http://www.gasmodel.com/code.htm. We propose a dynamic clustering model for uncovering latent time-varying group structures in multivariate panel data. The model is dynamic in three ways. First, the cluster location and scale matrices are time-varying to track gradual changes in cluster characteristics over time. Second, all units can transition between clusters based on a Hidden Markov model (HMM).

Dynamic Nonparametric Clustering of Multivariate Panel Data

2022 with Julia Schaumburg, André Lucas and Bernd Schwaab. Published at the Journal of Financial Econometrics. Python code available on Github. Also a Tinbergen Institute discussion paper No. 21-040/III and ECB Working Paper No. 2577. Formely entitled Clustering dynamics and persistence for financial multivariate panel data. We introduce a new method for dynamic clustering of panel data with dynamics for cluster location and shape, cluster composition, and for the number of clusters.

The natural interest rate and the Taylor rule in Brazil

2016 with Fernando de Holanda Barbosa and Felipe Diogo Camêlo. Published at the Revista Brasileira de Economia. This paper estimates the natural rate and the Taylor Rule for the Brazilian economy from 2003 to 2015. The natural rate in a small open economy is equal to the international real rate of interest, adjusted for the premium due to country risk and exchange rate risk. This framework allows decomposing the interest rate into components to understand why the Brazilian interbank market rate is so high when compared to other countries.

Dynamic Nonparametric Clustering of Multivariate Panel Data

2022

with Julia Schaumburg, André Lucas and Bernd Schwaab.
Published at the Journal of Financial Econometrics.
Python code available on Github.
Also a Tinbergen Institute discussion paper No. 21-040/III and ECB Working Paper No. 2577.
Formely entitled Clustering dynamics and persistence for financial multivariate panel data.

We introduce a new dynamic clustering method for multivariate panel data characterized by time-variation in cluster locations and shapes, cluster compositions, and possibly the number of clusters. To avoid overly frequent cluster switching (flickering), we extend standard cross-sectional clustering techniques with a penalty that shrinks observations toward the current center of their previous cluster assignment. This links consecutive cross-sections in the panel together, substantially reduces flickering, and enhances the economic interpretability of the outcome. We choose the shrinkage parameter in a data-driven way and study its misclassification properties theoretically as well as in several challenging simulation settings. The method is illustrated using a multivariate panel of four accounting ratios for 28 large European insurance firms between 2010 and 2020.