We present a class of one-to-one matching models with perfectly transferable utility. We discuss identification and inference in these separable models, and we show how their comparative statics are readily analyzed.

The first goal of this paper is to provide a simple and general test of the presence of asymmetric information in contractual relationships within a competitive context. We also argue that insurance data are particularly well suited to such empirical investigations. To illustrate this claim, we use data on contracts and accidents to investigate the extent of asymmetric information in the French market for automobile insurance. Using various parametric and nonparametric methods, we find no evidence for the presence of asymmetric information in this market.

Many questions in economics can be fruitfully analyzed in the framework of matching models. Until recently, empirical work has lagged far behind theory in this area. This review reports on recent developments that have considerably expanded the range of matching models that can be taken to the data. A leading theme is that in such two-sided markets, knowing the observable characteristics of partners alone is not enough to credibly identify the relevant parameters. A combination of richer data and robust, theory-driven restrictions is required. We illustrate this on leading applications.

The authors show in this paper that renegotiable short-term contracts can be as efficient as long-term renegotiation-proof contracts even in situations of asymmetric information. They do so by extending their earlier results on symmetric information models. Some limited commitment thus seems to be a necessary and sufficient condition to achieve long-run efficiency. The authors moreover show that, due to ratchet effects and time inconsistencies in the structure of informational rents, spot contracts are much less efficient under asymmetric information, even is there is no need for intertemporal smoothing.

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at

The past decade has seen the econometric implementation of macroeconomic multi-market fix-price models for a number of European countries. The procedure in use, the full information maximum likelihood (FIML) method, unfortunately becomes very cumbersome and seems out of reach when additional features are incorporated in the model (disaggregation into micro markets, opinion surveys,...). One purpose of the present work is to prove the fruitfulness of the following estimation strategy: use Monte-Carlo simulations to compute the first and second order moments of the endogenous variables, and maximize a resulting pseudo likelihood function to estimate the parameters. We first describe the PML method in the context of the so-called canonical disequilibrium model. We then apply it to a small aggregated macroeconomic model previously studied under FIML by Artus, Avouyi-Dovi, Laroque (1985), where we allow for a disaggregation into micro markets. The results we obtain are strikingly similar to theirs. This both demonstrates in this example the robustness of the FIML procedure to the introduction of micro markets and stresses the usefulness of the PML method in obtaining reliable estimates at a smaller cost.