Knowledge that Transforms

Recent theoretical work has shown the importance of measuring microeconomic uncertainty for models of both general and partial equilibrium under imperfect insurance. In this paper the assumption of i.i.d. income innovations used in previous empirical studies is removed and the focus of the analysis placed on models for the conditional variance of income shocks, which is related to the measure of risk emphasized by the theory. We first discriminate amongst various models of earnings determination that separate income shocks into idiosyncratic transitory and permanent components. We allow for education- and time-specific differences in the stochastic process for earnings and for measurement error. The conditional variance of the income shocks is modelled as a parsimonious ARCH process with both observable and unobserved heterogeneity. The empirical analysis is conducted on data drawn from the 1967-1992 Panel Study of Income Dynamics. We find strong evidence of sizeable ARCH effects as well as evidence of unobserved heterogeneity in the variances.

This paper analyses multivariate high frequency financial data using realized covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis, and covariance. It will be based on a fixed interval of time (e.g., a day or week), allowing the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions, and covariances change through time. In particular we provide confidence intervals for each of these quantities.

In a one principal two-agent model with adverse selection and collusion among agents, we show that delegating to one agent the right to subcontract with the other agent always earns lower profit for the principal compared with centralized contracting. Delegation to an intermediary is also not in the principal’s interest if the agents supply substitutes. It can be beneficial if the agents produce complements and the intermediary is well informed. Earlier versions of this paper have previously been circulated under different

We study inference in structural models with a jump in the conditional density, where location and size of the jump are described by regression curves. Two prominent examples are auction models, where the bid density jumps from zero to a positive value at the lowest cost, and equilibrium job-search models, where the wage density jumps from one positive level to another at the reservation wage. General inference in such models remained a long-standing, unresolved problem, primarily due to nonregularities and computational difficulties caused by discontinuous likelihood functions. This paper develops likelihood-based estimation and inference methods for these models, focusing on optimal (Bayes) and maximum likelihood procedures. We derive convergence rates and distribution theory, and develop Bayes and Wald inference. We show that Bayes estimators and confidence intervals are attractive both theoretically and computationally, and that Bayes confidence intervals, based on posterior quantiles, provide a valid large sample inference method.

This article discusses the history underlying the new literature, describes some of what has been learned thus far, and looks ahead towards making further progress

In this paper we propose a new estimator for a model with one endogenous regressor and many instrumental variables. Our motivation comes from the recent literature on the poor properties of standard instrumental variables estimators when the instrumental variables are weakly correlated with the endogenous regressor. Our proposed estimator puts a random coefficients structure on the relation between the endogenous regressor and the instruments. The variance of the random coefficients is modelled as an unknown parameter. In addition to proposing a new estimator, our analysis yields new insights into the properties of the standard two-stage least squares (TSLS) and limited-information maximum likelihood (LIML) estimators in the case with many weak instruments. We show that in some interesting cases, TSLS and LIML can be approximated by maximizing the random effects likelihood subject to particular constraints. We show that statistics based on comparisons of the unconstrained estimates of these parameters to the implicit TSLS and LIML restrictions can be used to identify settings when standard large sample approximations to the distributions of TSLS and LIML are likely to perform poorly. We also show that with many weak instruments, LIML confidence intervals are likely to have under-coverage, even though its finite sample distribution is approximately centered at the true value of the parameter. In an application with real data and simulations around this data set, the proposed estimator performs markedly better than TSLS and LIML, both in terms of coverage rate and in terms of risk. Copyright Econometric Society 2004.

An asymmetric information model of a finite horizon “nth order” rational asset price bubble is presented, where (all agents know that)n the asset is worthless. Also, the model has only two agents, so the first order version of the bubble is simpler than other first order bubbles in the literature.

With many semi-anonymous players, the equilibria of simultaneous-move games are extensively robust. This means that the equilibria survive even if the simultaneous-play assumption is relaxed to allow for a large variety of extensive modifications. Such modifications include sequential play with partial and differential revelation of information, commitments, multiple revisions of choices, cheap talk announcements, and more.

This paper examines direct broadcast satellites (DBS) as a competitor to cable. We first estimate a structural consumer level demand system for satellite, basic cable, premium cable and local antenna using micro data on almost 30000 households in 317 markets, including extensive controls for unobserved product quality and allowing the distribution of unobserved tastes to follow a fully flexible multivariate normal distribution. The estimated elasticity of expanded basic is about -15, with the demand for premium cable and DBS more elastic. The results identify strong correlations in the taste for different products not captured in conventional logit models. Estimates of the supply response of cable suggest that without DBS entry cable prices would be about 15 percent higher and cable quality would fall. We find a welfare gain of between $127 and $190 per year (aggregate $2.5 billion) for satellite buyers, and about $50 (aggregate $3 billion) for cable subscribers.

An asymptotically efficient likelihood-based semiparametric estimator is derived for the censored regression (tobit) model, based on a new approach for estimating the density function of the residuals in a partially observed regression. Smoothing the self-consistency equation for the nonparametric maximum likelihood estimator of the distribution of the residuals yields an integral equation, which in some cases can be solved explicitly. The resulting estimated density is smooth enough to be used in a practical implementation of the profile likelihood estimator, but is sufficiently close to the nonparametric maximum likelihood estimator to allow estimation of the semiparametric efficient score. The parameter estimates obtained by solving the estimated score equations are then asymptotically efficient. A summary of analogous results for truncated regression is also given. Copyright The Econometric Society 2004.