Recent working papers
Confidence set for group membership
with Ryo Okui
Working paper (v4, December 2020) - revision requested by Quantitative Economics
Our confidence set quantifies the statistical uncertainty from data-driven cluster assignment in clustered panel models. It covers the true cluster memberships jointly for all units with pre-specified probability and is constructed by inverting many simultaneous unit-specific one-sided tests for group membership. We justify our approach under \(N,T \to \infty\) asymptotics using tools from high-dimensional statistics, some of which we extend or develop in this paper. We provide an empirical application as well as Monte Carlo evidence that the confidence set has adequate coverage in finite samples.
Convergence rate of estimators of clustered panel models with misclassification
with Ryo Okui
Economics Letters (2021), Volume 203
We study kmeans clustering estimation of panel data models with a latent group structure and \(N\) units and \(T\) time periods under long panel asymptotics. We show that the group-specific coefficients can be estimated at the parametric root \(NT\) rate even if error variances diverge as \(T \to \infty\) and consequently some units are asymptotically misclassified. This limit case approximates empirically relevant settings and is not covered by existing asymptotic results.
An empirical model of dyadic link formation in a network with unobserved heterogeneity
Review of Economics and Statistics (2019), Volume 101(5)
I study a dyadic linking model in which agents form directed links that exhibit homophily and reciprocity. A fixed effect approach accounts for unobserved sources of degree heterogeneity. I consider specification testing and inference with respect to the homophily and reciprocity parameters. The specification test compares observed transitivity to predicted transitivity. All test statistics account for the presence of an incidental parameter by using formulas based on an asymptotic approximation. In an application to favor networks in Indian villages, the specification test detects that the dyadic linking model underestimates the true transitivity of the network.
Projecting long-run compound returns: the limits of data-driven inference
with Adam Farago, Erik Hjalmarsson and Tamas Kiss
with Mikael Lindahl
Inference after multiple hypothesis testing
with Ryo Okui and Wenjie Wang
Old working papers
Overidentification test in a nonparametric treatment model with unobserved heterogeneity
with Florian Sarnetzki
Working paper (v4, April 2014)