Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 178

The DOI matches its Crossref record.

The Unbiasedness of Zellner's Seemingly Unrelated Regression Equations Estimators

Kakwani, N. C.·1967·Journal of the American Statistical Association

Abstract

The purpose of the present note is to demonstrate the unbiasedness of Zellner's seemingly unrelated regression equations estimators under fairly general conditions.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/01621459.1967.10482895

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 178

Metadata

Authors
Kakwani, N. C.
Venue
Journal of the American Statistical Association
Published
1967-03
Volume
62
Issue
317
Pages
141-142
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
OpenAlex
W2013046871
Cited by
178
References
0
FWCI
3.18
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The Unbiasedness of Zellner's Seemingly Unrelated Regression Equations Estimators. https://app.cochat.ai/papers/10.1080/01621459.1967.10482895
The paper
Kakwani, N. C. (1967). The Unbiasedness of Zellner's Seemingly Unrelated Regression Equations Estimators. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1967.10482895

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.