Is it possible for an estimator to be biased in finite sample but consistent in large sample 一个估计量是否有可能在有限样本中是有偏的但又具有一致性？
When gauss - markov assumptions hold , we need not look for alternative unbiased estimators 当高斯马尔科夫假定成立时，我们不需要再去找其它无偏估计量了。
In the second section , we have introduced the formation for the estimators and the proposed conditions that the paper needed 第二节介绍了估计量的构造及文章所需的假设条件。
Consistency of an estimator is an important property , but it alone does not allow us to perform statistical inference 估计量的一致性是一条重要性质，但我们并不能只靠它来进行统计推断。
We say that ols estimators are asymptotically efficient among a certain class of estimators under the gauss - markov assumptions 我们说在高斯-马尔可夫假定下ols估计量是渐近有效的估计量。
While not all useful estimators are unbiased , virtually all economists agree that consistency is a minimal requirement for an estimator 计量经济学家一致同意，一致性是对估计量的最低要求。
A general proof of consistency of the ols estimators from the multivariate regression case can be shown through matrix manipulations 多元回归中ols估计量的一致性的证明可以通过矩阵运算得到。
In the third section , we have presented the main result of the paper , namely the uniformly strong convergence rates for each estimator 第三节给出了本文的主要结果,即各估计量的一致强收敛速度。
While not all useful estimators are unbiased , virtually all economists agree that consistency is a minimal requirement for an estimator 虽然并不是所有的有用的估计量是无偏的,但是,一致性则是经济学家对估计量的最低要求
Unbiased estimators are not necessarily consistent , but those whose variances shrink to zero as the sample size grows are consistent 无偏估计量未必是一致的，但是那些当样本容量增大时方差会收缩到零的无偏估计量是一致的。
