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异方差回归中广义 CL、交叉验证与广义交叉验证的渐近最优性AI 解读

The problem considered here is that of using a data-driven procedure to select a good estimate from a class of linear estimates indexed by a discrete parameter. In contrast to other papers on this subject, we consider models with heteroskedastic errors. The results apply to model selection problems in linear regression and to nonparametric regression estimation via series estimators, nearest-neighbor estimators, and local regression estimators, among others. Generalized CL…

似然支配准则AI 解读

The dominance ordering and the LDC provide a new approach to model selection. The dominance ordering is described in terms of a fictive experiment in which the two competing hypotheses are nested in a composite. If estimating the composite would lead to accepting one hypothesis and rejecting the other, then which would be accepted and which rejected is determined by the two adjusted likelihood values and does not require estimating the composite, although it does require…

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无限方差误差下的 Durbin–Watson 比率AI 解读

This paper studies the properties of the von Neumann ratio for time series with infinite variance. Our asymptotics cover the null of iid variates and general moving-average (MA) alternatives. Regression residuals are also considered. In the static regression model the Durbin-Watson statistic has the same limit distribution as the von Neumann ratio. In dynamic models the results are more complex. In finite-variance models our results specialize to those of the Durbin…

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利用截面、面板或二者结合估计线性模型的时变参数AI 解读

In this paper we consider the estimation of time-dependent parameters in linear models from panel data, cross-sections, or both. We determine the fraction of individuals that should be reinterviewed each period in order to minimize the variance of the most efficient estimator of linear combinations of the parameters. Moreover we derive simple sufficient conditions for the optimal fraction to be zero or one, respectively.

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Tobit 对数似然的多重根AI 解读

We show that the occurence of multiple roots of the Tobit log-likelihood function in the untransformed parameter space is not nearly so likely as heretofore accepted. The conditions which must be met for them to occur require data configurations which could not be met in random sampling. Moreover, to find a ‘second root’ in practice would require computer programs far more accurate than those which currently exist. Similar results are obtained for a number of other limited…

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最优化模型的拟合优度AI 解读

Conventional econometric tests of optimizing models typically involve embedding the optimizing model in a parametric specification and then examining the parametric restrictions imposed by the optimization hypothesis. The optimization hypothesis is rejected if the estimated parameters are significantly different, in the statistical sense, from the values implied by optimization. I argue that a more fruitful approach to testing optimizing behavior is to measure the departure…

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