Tests for Specification Errors in Classical Linear Least-Squares Regression Analysis

Clicks: 1
ID: 290016
1969
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #94 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 145 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Summary The effects on the distribution of least-squares residuals of a series of model mis-specifications are considered. It is shown that for a variety of specification errors the distributions of the least-squares residuals are normal, but with non-zero means. An alternative predictor of the disturbance vector is used in developing four procedures for testing for the presence of specification error. The specification errors considered are omitted variables, incorrect functional form, simultaneous equation problems and heteroskedasticity.
Reference Key
openalex_W953353200 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors James B. Ramsey
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 1969
DOI
10.1111/j.2517-6161.1969.tb00796.x
URL
https://doi.org/10.1111/j.2517-6161.1969.tb00796.x
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.