日期精度：到日 0 篇 · 到月 11 篇 · 到年 0 篇
非线性估计量的 Edgeworth 展开与鞍点展开
Simple methods are developed for deriving Edgeworth, saddlepoint, and related expansions for the estimators of multivariate and nonlinear models. Illustrations are provided. Simulations are reported indicating the methods work well compared to standard asymptotic and bootstrapped approaches.
含未观测异质性的持续期模型的有效半参数估计
This paper develops a new semiparametric approach for the estimation of hazard functions in the presence of unobserved heterogeneity. The hazard function is specified parametrically, whereas the distribution of the unobserved heterogeneity is indirectly estimated using the method of kernels. The semiparametric efficiency bounds are derived. The estimator obtains these bounds in large samples.The authors thank Yongmiao Chen, James Heckman, Hidehiko Ichimura, Tony Lancaster,…
潜在竞争风险模型的非参数识别
This paper shows that nonparametric identification of latent competing risks models is possible without the usual conditional independence and exclusion restrictions.The authors thank Jinyong Hahn, James Heckman, and Shinichi Sakata for useful discussions on the subject matter of this paper. Any errors are the fault of the authors. Research funding for Rilstone was provided by the Social Sciences and Humanities Research Council of Canada.
利用自助法置信区间改善似不相关回归方程的推断
The usual standard errors for the regression coefficients in a seemingly unrelated regression model have a substantial downward bias. Bootstrapping the standard errors does not seem to improve inferences. In this paper, Monte Carlo evidence is reported which indicates that bootstrapping can result in substantially better inferences when applied to t -ratios rather than to standard errors.
工具变量依赖参数时的半参数 IV 估计
A well-known result in the method of moments literature is that the efficient instruments for the estimation of a model are functions of the conditional expectation of its gradient. Some recent studies have suggested the nonparametric estimation of these instruments when they are of unknown functional form. When these instruments in turn depend on the unknown parameters it has been suggested that these be replaced by preliminary consistent estimates. It is shown here that…
One of the most common practical problems in statistics and econometrics is the estimation of linear regression models with heteroscedastic errors. This article reports the results of a Monte Carlo comparison of various parametric and semiparametric estimated generalized least squares (EGLS) estimators. In small-sized (20) and sometimes medium-sized (50) samples, ordinary least squares dominated the other techniques for low levels of heteroscedasticity. In medium-sized…