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Robust inference in models identified via heteroskedasticity
Identification via heteroskedasticity exploits differences in variances across regimes to identify parameters in simultaneous equations. I study weak identification in such models, which arises when variances change very little or the variances of multiple shocks change close to proportionally. I show that this causes standard inference to become unreliable, outline two tests to detect weak identification, and establish conditions for the validity of nonconservative methods for robust inference on an empirically relevant subset of the parameter vector. I apply these tools to monetary policy ...
Robust Inference in First-Price Auctions : Experimental Findings as Identifying Restrictions
In laboratory experiments bidding in first-price auctions is more aggressive than predicted by the risk-neutral Bayesian Nash Equilibrium (RNBNE) - a finding known as the overbidding puzzle. Several models have been proposed to explain the overbidding puzzle, but no canonical alternative to RNBNE has emerged, and RNBNE remains the basis of the structural auction literature. Instead of estimating a particular model of overbidding, we use the overbidding restriction itself for identification, which allows us to bound the valuation distribution, the seller's payoff function, and the optimal ...