Bayesian Model Averaging in the Instrumental Variable Regression Model

This paper considers the instrumental variable regression model when there is uncertainty about the set of instruments, exogeneity restrictions, the validity of identifying restrictions and the set of exogenous regressors. This uncertainty can result in a huge number of models. To avoid statistical problems associated with standard model selection procedures, we develop a reversible jump Markov chain Monte Carlo algorithm that allows us to do Bayesian model averaging. The algorithm is very exible and can be easily adapted to analyze any of the di¤erent priors that have been proposed in the Bayesian instrumental variables literature. We show how to calculate the probability of any relevant restriction (e.g. the posterior probability that over-identifying restrictions hold) and discuss diagnostic checking using the posterior distribution of discrepancy vectors. We illustrate our methods in a returns-to-schooling application.

Expert Information and Majority Decisions

This paper studies dichotomous majority voting in common interest committees where each member receives not only a private signal but also a public signal observed by all of them. The public signal represents, e.g. expert information presented to an entire committee and its quality is higher than that of each individual private signal. We identify two informative symmetric strategy equilibria, namely i) the mixed strategy equilibrium where each member randomizes between following the private and public signals should they disagree; and ii) the pure strategy equilibrium where they follow the public signal for certain. The former outperforms the latter. The presence of the public signal precludes the equilibrium where every member follows their own signal, which is an equilibrium in the absence of the public signal. The mixed strategy equilibrium in the presence of the public signal outperforms the sincere voting equilibrium without the public signal, but the latter may be more efficient than the pure strategy equilibrium in the presence of the public signal. We suggest that whether expert information improves committee decision making depends on equilibrium selection.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

A Case of Framing Effects: The Elicitation of Time Preferences.

We compare three methods for the elicitation of time preferences in an experimental setting: the Becker-DeGroot-Marschak procedure (BDM); the second price auction; and the multiple price list format. The first two methods have been used rarely to elicit time preferences. All methods used are perfectly equivalent from a decision theoretic point of view, and they should induce the same ‘truthful’ revelation i dominant strategies. In spite of this, we find that framing does matter: the money discount rates elicited with the multiple price list tend to be higher than those elicited with the other two methods. In addition, our results shed some light on attitudes towards time, and they permit a broad classification of subjects depending on how the size of the elicited values varies with the time horizon.