2 citations · 4 across the 6 of their papers we have counts for
7 papers
Inference for Fixed Effects Estimators when Panels are Unbalanced
Daniel Czarnowske, Amrei Stammann
We develop the asymptotic theory for two-way fixed effects M-estimators in unbalanced panels, within a framework where both panel dimensions grow large at proportional rates. The s…
(Debiased) Inference for Fixed Effects Estimators with Three-Dimensional Panel and Network Data
Daniel Czarnowske, Amrei Stammann
Inference for fixed effects estimators is often unreliable due to Nickell- and incidental parameter biases. While these issues are well understood for classical two-dimensional pan…
The Effects of Flipped Classrooms in Higher Education: A Causal Machine Learning Analysis
Daniel Czarnowske, Florian Heiss, Theresa M. A. Schmitz +1
This study uses double/debiased machine learning (DML) to evaluate the impact of transitioning from lecture-based blended teaching to a flipped classroom concept. Our findings indi…
Latent Unbalancedness in Three-Way Gravity Models
Daniel Czarnowske, Amrei Stammann
Many panel data sets used for pseudo-poisson estimation of three-way gravity models are implicitly unbalanced because uninformative observations are redundant for the estimation. W…
A Classifier-Lasso Approach for Estimating Production Functions with Latent Group Structures
Daniel Czarnowske
I present a new estimation procedure for production functions with latent group structures. I consider production functions that are heterogeneous across groups but time-homogeneou…
Inference in Unbalanced Panel Data Models with Interactive Fixed Effects
Daniel Czarnowske, Amrei Stammann
We derive the asymptotic theory of Bai (2009)'s interactive fixed effects estimator for unbalanced panels in which the source of attrition is conditionally random. For inference, w…
Fixed Effects Binary Choice Models: Estimation and Inference with Long Panels
Daniel Czarnowske, Amrei Stammann
Empirical economists are often deterred from the application of fixed effects binary choice models mainly for two reasons: the incidental parameter problem and the computational ch…