Knowledge that Transforms

This paper is concerned with the Bayesian estimation of nonlinear stochastic differential equations when observations are discretely sampled. The estimation framework relies on the introduction of latent auxiliary data to complete the missing diffusion between each pair of measurements. Tuned Markov chain Monte Carlo (MCMC) methods based on the Metropolis-Hastings algorithm, in conjunction with the Euler-Maruyama discretization scheme, are used to sample the posterior distribution of the latent data and the model parameters. Techniques for computing the likelihood function, the marginal likelihood, and diagnostic measures (all based on the MCMC output) are developed. Examples using simulated and real data are presented and discussed in detail.

This paper studies necessity of transversality conditions for the continuous time, reduced form model. By generalizing Benveniste and Scheinkman's (1982) “envelope” condition and Michel's (1990) version of the squeezing argument, we show a generalization of Michel's (1990, Theorem 1) necessity result that does not assume concavity. The generalization enables us to generalize Ekeland and Scheinkman's (1986) result as well as to establish a new result that does not require the objective functional to be finite. The new result implies that homogeneity of the return function alone is sufficient for the necessity of the most standard transversality condition. Our results are also applied to a nonstationary version of the one-sector growth model. It is shown that bubbles never arise in an equilibrium asset pricing model with a nonlinear constraint.

We study a coordination game with randomly changing payoffs and small frictions in changing actions. Using only backwards induction, we find that players must coordinate on the risk-dominant equilibrium. More precisely, a continuum of fully rational players are randomly matched to play a symmetric 2×2 game. The payoff matrix changes according to a random walk. Players observe these payoffs and the population distribution of actions as they evolve. The game has frictions: opportunities to change strategies arrive from independent random processes, so that the players are locked into their actions for some time. As the frictions disappear, each player ignores what the others are doing and switches at her first opportunity to the risk-dominant action. History dependence emerges in some cases when frictions remain positive.

This paper extends the revelation principle to environments in which the mechanism designer cannot fully commit to the outcome induced by the mechanism. We show that he may optimally use a direct mechanism under which truthful revelation is an optimal strategy for the agent. In contrast with the conventional revelation principle, however, the agent may not use this strategy with probability one. Our results apply to contracting problems between a principal and a single agent. By reducing such problems to well-defined programming problems they provide a basic tool for studying imperfect commitment.

It is widely known that when there are errors with a moving-average root close to −1, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and the BIC tend to select a truncation lag (k) that is very small. We consider a class of Modified Information Criteria (MIC) with a penalty factor that is sample dependent. It takes into account the fact that the bias in the sum of the autoregressive coefficients is highly dependent on k and adapts to the type of deterministic components present. We use a local asymptotic framework in which the moving-average root is local to −1 to document how the MIC performs better in selecting appropriate values of k. In Monte-Carlo experiments, the MIC is found to yield huge size improvements to the DFGLS and the feasible point optimal PT test developed in Elliott, Rothenberg, and Stock (1996). We also extend the M tests developed in Perron and Ng (1996) to allow for GLS detrending of the data. The MIC along with GLS detrended data yield a set of tests with desirable size and power properties.

Potential games are games with potential functions.Technically, the potential function defines a refinement concept.We provide justification for this refinement concept using the notion of robustness of equilibria.A Nash equilibrium of a complete information game is said to be robust if every incomplete information game where payoffs are almost always given by the complete information game has an equilibrium which generates behavior close to the Nash equilibrium.We show that Nash equilibria that maximize potential functions are generically robust.

generous referee; remaining errors are my own. The exact finite sample distribution for the two-stage least square estimator has been derived for quite general situations (see Phillips (1983) for a survey of the literature). Unfortunately, these general expressions do not lend themselves to easy interpretations. Consequently, a number of authors have looked at special cases to illustrate some of the problems that can arise when using asymptotic distribution results for finite samples. Two such special cases of note are: 1) The totally unidentified case, where the population covariance between the instruments and the endogenous variable is zero; 2) The very weakly correlated case (first stage R2 much less than the inverse of the sample size) with one equation and one endogenous regressor. Nelson and Startz ’ (1990a&b) pioneered the study of the very weakly correlated case. Their work dramatized the substantial differences that can arise between the exact distribution of the IV estimator and the asymptotic distribution. They argue that: 1) the IV estimate will be concentrated around a value more biased than the plim of the OLS estimate and the ratio of the two biases falls as the correlation between the endogenous variable and

Laboratory and field studies of time preference find that discount rates are much greater in the short-run than in the long-run. Hyperbolic discount functions capture this property. This paper solves the decision problem of a hyperbolic consumer who faces stochastic income and a borrowing constraint. The paper uses the bounded variation calculus to derive the Hyperbolic Euler Relation, a natural generalization of the standard Exponential Euler Relation. The Hyperbolic Euler Relation implies that consumers act as if they have endogenous rates of time preference that rise and fall with the future marginal propensity to consume (e.g., discount rates that endogenously range from 5% to 41% for the example discussed in the paper).