The DOI matches its Crossref record.
The Estimation of Relationships with Autocorrelated Residuals by the Use of Instrumental Variables
Abstract
SUMMARY: The instrumental variables method of estimating the coefficients of a linear relationship between time series which have measurement errors is applied to the case where the coefficients are functions of a set of parameters. The asymptotic error variance matrix of the estimates is obtained, asymptotic tests for the existence and identification of the suggested form of relationship are suggested, and asymptotic confidence regions are given. The general theory is then applied to the case of a relationship with an autoregressive residual, and practical computational methods are suggested.
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.
Integrity signals
Registry
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.
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
Large Sample Properties of Generalized Method of Moments Estimators
Hansen, L. P. (1982) · Econometrica · doi:10.2307/1912775 · Cited by 13,467
Arellano, M. (2003) · doi:10.1093/0199245282.001.0001 · Cited by 2,108
Chapter 36 Large sample estimation and hypothesis testing
Newey, W. K., & McFadden, D. (1994) · Handbook of Econometrics · doi:10.1016/s1573-4412(05)80005-4 · Cited by 1,743
Concrete Problems in AI Safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016) · arXiv · doi:10.48550/arxiv.1606.06565 · Cited by 1,314
What will take the con out of econometrics
(1986) · International Journal of Forecasting · doi:10.1016/0169-2070(86)90113-5 · Cited by 296
Metadata
- Authors
- Sargan, J. D.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1959-01-01
- Volume
- 21
- Issue
- 1
- Pages
- 91-105
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2903797534
- Cited by
- 222
- References
- 0
- FWCI
- 0.00
- 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 Estimation of Relationships with Autocorrelated Residuals by the Use of Instrumental Variables. https://app.cochat.ai/papers/10.1111/j.2517-6161.1959.tb00317.x
- The paper
- Sargan, J. D. (1959). The Estimation of Relationships with Autocorrelated Residuals by the Use of Instrumental Variables. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/j.2517-6161.1959.tb00317.x
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.
