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Found 7 papers, 0 papers with code
Synthetic Decomposition for Counterfactual Predictions
Counterfactual predictions are challenging when the policy variable goes beyond its pre-policy support.
Estimating Dynamic Spillover Effects along Multiple Networks in a Linear Panel Model
Spillover of economic outcomes often arises over multiple networks, and distinguishing their separate roles is important in empirical research.
Some Impossibility Results for Inference With Cluster Dependence with Large Clusters
When within-cluster observations satisfy the uniform central limit theorem, we also show that a sufficient condition for consistent $\sqrt{n}$-discrimination of the mean is that we have at least two large clusters.
The Law of Large Numbers for Large Stable Matchings
In many empirical studies of a large two-sided matching market (such as in a college admissions problem), the researcher performs statistical inference under the assumption that they observe a random sample from a large matching market.
A Decomposition Approach to Counterfactual Analysis in Game-Theoretic Models
Decomposition methods are often used for producing counterfactual predictions in non-strategic settings.
Counterfactual Analysis under Partial Identification Using Locally Robust Refinement
In particular, within the identified set, some counterfactual predictions can exhibit more robustness than others, against local perturbations of the reduced forms (e. g. the equilibrium selection rule).
Limit Theorems for Network Dependent Random Variables
This paper is concerned with cross-sectional dependence arising because observations are interconnected through an observed network.
