Most hypotheses in binary response models are composite. The null hypothesis is usually that one or more slope coefficients are zero. Typically, the sequence of alternatives of interest is one in which the slope coefficients are increasing in absolute value. In this paper, we prove that the...

We test for the presence of long memory in daily stock returns and their squares using a robust semiparametric procedure. Spurious results can be produced by nonstationarity and aggregation. We address these problems by analyzing subperiods of returns and using individual stocks. The test...

In econometrics, most null hypotheses are composite, dividing the parameters into parameters of interest and nuisance parameters. The domain of the nuisance parameters can influence the size-corrected critical value and hence the power of a test. We show that the domain of the nuisance...

This paper uses average monthly returns and linear spline regressions to investigate the relation between expected return and firm size during 1980-1994. We find that the average monthly returns are approximately constant across size deciles. The estimated spline regressions vary substantially...

Censoring of outcomes (selection) is a common consequence of survey nonresponse and attrition in panels, and has received much attention. Joint censoring of regressors and outcomes is also common, but it has remained unexplored. This paper shows that the problem of identification when regressors...

The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data. It amounts to treating the data as if they were the population for the purpose of evaluating the distribution of interest. Under mild regularity conditions, the bootstrap yields...

In this paper we develo psemiparametric estimators of L and y in the model L(Y) = min[b›X + U,C], where Y is a nonnegative dependent variable, X is a vector of explanatory variables, U is an unobserved random "error" term with unknown distribution function y, C is a random censoring variable,...

The smoothed maximum score estimator of the coefficient vector of a binary response model is consistent and asymptotically normal under weak distributional assumptions. However, the differences between the true and nominal levels of tests based on smoothed maximum score estimates can be very...

The least-absolute-deviations (LAD) estimator for a median-regression model does not satisfy the standard conditions for obtaining asymptotic refinements through use of the bootstrap because the LAD objective function is not smooth. This paper overcomes this problem by smoothing the objective...

The optimal minimum distance (OMD) estimator for models of covariance structures is asymptotically efficient but has much worse finite-sample properties than does the equally-weighted minimum distance (EWMD) estimator. This paper shows how the bootstrap can be used to improve the finite-sample...