Knowledge that Transforms

anonymous referees for comments and Tel–Aviv University for its hospitality during part of the work on this paper. Dekel thanks the NSF and Lipman thanks SSHRCC for financial support for this research. Dekel and Lipman particularly thank Phil Reny for a series of discussions which led to this project. This paper was formerly titled “Possibility Correspondences Preclude Unawareness.” 2

This paper develops and applies semiparametric econometric methods to estimate the form of selection bias that arises from using nonexperimental comparison groups to evaluate social programs and to test the identifying assumptions that justify three widely-used classes of estimators and our extensions of them: (a) the method of matching; (b) the classical econometric selection model which represents the bias solely as a function of the probability of participation; and (c) the method of difference-in-differences. Using data from an experiment on a prototypical social program combined with unusually rich data from a nonexperimental comparison group, we reject the assumptions justifying matching and our extensions of that method but find evidence in support of the index-sufficient selection bias model and the assumptions that justify application of a conditional semiparametric version of the method of difference-in-difference. Fa comparable people and to appropriately weight participants and nonparticipants a sources of selection bias as conveniently measured. We present a rigorous defin bias and find that in our data it is a small component of conventially meausred it is still substantial when compared with experimentally-estimated program impa matching participants to comparison group members in the same labor market, givi same questionnaire, and making sure they have comparable characteristics substan the performance of any econometric program evaluation estimator. We show how t analysis to estimate the impact of treatment on the treated using ordinary obser

[One-step efficient GMM estimation has been developed in the recent papers of Back and Brown (1990), Imbens (1993), and Qin and Lawless (1994). These papers emphasized methods that correspond to using Owen's (1988) method of empirical likelihood to reweight the data so that the reweighted sample obeys all the moment restrictions at the parameter estimates. In this paper we consider an alternative KLIC motivated weighting and show how it and similar discrete reweightings define a class of unconstrained optimization problems which includes GMM as a special case. Such KLIC-motivated reweightings introduce M auxiliary "tilting" parameters, where M is the number of moments; parameter and overidentification hypotheses can be recast in terms of these tilting parameters. Such tests are often startlingly more effective than their conventional counterparts. These differences are not completely explained by differences in the leading terms of the asymptotic expansions of the test statistics.]

We show that quasi-maximum likelihood (QML) estimators for conditional dispersion models can be severely affected by a small number of outliers such as market crashes and rallies, and we propose new estimation strategies (the two-stage Hampel estimators and two-stage S-estimators) resistant to the effects of outliers and study the properties of these estimators. We apply our methods to estimate models of the conditional volatility of the daily returns of the S&P 500 Cash Index series. In contrast to QML estimators, our proposed method resists outliers, revealing an informative new picture of volatility dynamics during typical daily market activity.

In structural empirical models of labor market search, the distribution of wage offers is usually assumed to be exogenous. However, because in setting their wages profit-maximizing firms should consider the reservation wages of job seekers, the wage offer distribution is essentially endogenous. We investigate whether a proposed equilibrium search model, in which the wage offer distribution is endogenous, is able to describe observed labor market histories. We find that the distributions of job and unemployment spells are consistent with the data, and that the qualitative predictions of the model for the wages set by employers are confirmed by wage regressions. The model is estimated using panel data on unemployed and employed individuals. We distinguish between separate segments of the labor market, and we show that productivity heterogeneity is important to obtain an acceptable fit to the data. The results are used to estimate the degree of monopsony power of firms. Further, the effects of changes in the mandatory minimum wage are examined.

In a number of econometric models, rules of large-sample inference require a consistent estimate of f(O), where f(A) is the spectral density matrix of Y, = U 0 xt, for covariance stationary vectors it, xt. Typically y, is allowed to have nonparametric autocorrelation, and smoothing is used in the estimation of f(O). We give conditions under which f(O) can be consistently estimated without smoothing. The conditions are relevant to inference on slope parameters in models with an intercept and strictly exogenous regressors, and allow regressors and disturbances to collectively have considerable stationary long memory and to satisfy only mild, in some cases minimal, moment conditions. The estimate of f(O) dominates smoothed ones in the sense that it can have mean squared error of order n -1, where n is sample size. Under standard additional regularity conditions, we extend the estimate of f(O) to studentize asymptotically normal estimates of structural parameters in linear simultaneous equations systems. A small Monte Carlo study of finite sample behavior is included.

The role of propensity score in the efficient estimation of the average treatment effects is examined. If the treatment is ignorable given some observed characteristics, it is shown that the propensity score is ancillary for estimation of the average treatment effects but not for estimation of average treatment effects on the treated. Efficient semiparametric estimators take the form of relevant sample averages of the data completed by the nonparametric imputation method. Projection on the propensity score is not necessary for efficient semiparametric estimation of the average treatment effects on the treated even if the propensity score is known.