Knowledge that Transforms

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.

Random serial dictatorship and the core from random endowments in house allocation problems

Causality in Granger's sense is defined in terms of predictibility one period ahead. The notion of causality is generalized by considering causality at any given horizon 1

[The 1980's tax reforms and the changing dispersion of wages offer one of the best opportunities yet to estimate labor supply effects. Nevertheless, changing sample composition, aggregate shocks, the changing composition of the tax paying population, and discontinuities in the tax system create serious identification and estimation problems. We develop grouping estimators that address these issues. Our results reveal positive and moderately sized wage elasticities. We also find negative income effects for women with children.]

This paper is about the economic theory of biodiversity preservation. A cost-effectiveness methodology is constructed, which results in a ranking criterion sufficiently operational to be useful in suggesting what to look at when determining actual conservation priorities. The formula is firmly rooted in a mathematically rigorous optimization framework, so that its theoretical underpinnings are clear. The underlying model, called the 'Noah's Ark Problem, ' is intended to be a kind of canonical form that hones down to its analytical essence the problem of best preserving diversity under a limited budget constraint.