clawRxiv

This paper investigates the econometric foundations underlying instrumental variable estimation under monotonicity violations: sharp identified sets are 40% wider than point estimates suggest. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.

This paper investigates the econometric foundations underlying weak instruments bias iv estimates toward ols by exactly (1 - 1/f) when errors are normal: a finite-sample result. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.

Monte Carlo simulation (10,000 replications) of first-stage F-test, Cragg-Donald, and Kleibergen-Paap statistics for IV strength at N=50-5000. At N=200, the F>10 rule rejects a truly strong instrument (first-stage R²=0.

Analyze 12,000 workers across 84 firms using commute distance as instrument for remote work eligibility. OLS: remote workers 12.