You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Least Squares, Conditional Predictions, and Estimator Properties
Wade P. Sewell
Vol. 37, No. 1 (Jan., 1969), pp. 39-43
Published by: The Econometric Society
Stable URL: http://www.jstor.org/stable/1909202
Page Count: 5
You can always find the topics here!Topics: Estimators, Econometrics, Consistent estimators, Matrices, Classical economics, Statistical estimation, Least squares, Economic forecasting, Unbiased estimators, Economic modeling
Were these topics helpful?See something inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
The problem of prediction of a subset of the endogenous variables conditioned on the balance of the endogenous variables, as well as the predetermined variables, is discussed in the context of a general linear model with normal disturbances. It is shown that the conditional least-squares predictor is unbiased, correcting Srinivasan's  treatment of Waugh's paper  in a less general setting. In the course of the argument, a simple derivation of the multivariate t density is presented. Srinivasan's  remark that consistency of an estimator does not imply its asymptotic unbiasedness (in one of the common senses of that term) is illustrated by two examples, one with a discrete and one with a continuous distribution. They show that neither asymptotic unbiasedness nor zero asymptotic variance is necessary for consistency. The example with a continuous distribution is the two-stage least-squares estimator.
Econometrica © 1969 The Econometric Society