We investigate the testable implications of the theory of stable matchings. We provide a characterization of the matchings that are rationalizable as stable matchings when agents' preferences are unobserved. The characterization is a simple nonparametric test for stability, in the tradition of revealed preference tests. We also characterize the observed stable matchings when monetary transfers are allowed and the stable matchings that are best for one side of the market: extremal stable matchings. We find that the theory of extremal stable matchings is observationally equivalent to requiring that there be a unique stable matching or that the matching be consistent with unrestricted monetary transfers.

Fiscal foresight --the phenomenon that legislative and implementation lags ensure that private agents receive clear signals about the tax rates they face in the future --is intrinsic to the tax policy process.This paper develops an analytical framework to study the econometric implications of fiscal foresight.Simple theoretical examples show that foresight produces equilibrium time series with nonfundamental representations, which misalign the agents' and the econometrician's information sets.Economically meaningful shocks to taxes, therefore, cannot generally be extracted from statistical innovations in conventional ways.Econometric analyses that fail to align agents' and the econometrician's information sets can produce distorted inferences about the effects of tax policies.The paper documents the sensitivity of econometric inferences of tax effects to details about how tax information flows into the economy.We show that alternative assumptions about the information flows that give rise to fiscal foresight can reconcile the diverse empirical findings in the literature on anticipated tax changes.

This paper examines three distinct hypothesis testing problems that arise in the context of identification of some nonparametric models with endogeneity. The first hypothesis testing problem we study concerns testing necessary conditions for identification in some nonparametric models with endogeneity involving mean independence restrictions. These conditions are typically referred to as completeness conditions. The second and third hypothesis testing problems we examine concern testing for identification directly in some nonparametric models with endogeneity involving quantile independence restrictions. For each of these hypothesis testing problems, we provide conditions under which any test will have power no greater than size against any alternative. In this sense, we conclude that no nontrivial tests for these hypothesis testing problems exist.