We propose a new model in which relationship-specific e!ects or shocks are identified in a bipartite network under mild covariance restrictions, generalizing the influential Abowd et al. (1999) framework. For example, separate demand shocks are identified for each bank from which a firm borrows....

Total factor productivity (TFP) is crucial for evaluating technological innovation, yet its accurate measurement remains challenging due to diverse methodologies. Notably, the selection of proxy variables in control function approach (CFA) significantly influences TFP estimation, yet existing...

This paper presents a novel method for estimating treatment effects in cases where prior knowledge of the exogeneity of the treatment variable is limited. We employ a machine learning technique, double selection via Lasso, to identify a robust set of control variables without requiring prior...

Applied researchers commonly interpret coefficient movements across OLS and difference-in-differences specifications with varying controls as evidence of bias reduction or improved identification. This note shows that such interpretations generally fail under heterogeneous treatment effects. In...

We derive an admissible parameter space for vector Multiplicative Error Models (vMEMs), explicitly formulating it in terms of the model's matrix parameters through a set of matrix inequalities. Another key contribution is the adoption of constrained maximum likelihood estimation for the...

We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand...