The DOI matches its Crossref record.
Hypothesis Testing with Efficient Method of Moments Estimation
Abstract
Efficient method of moments estimation techniques include many commonly used techniques, including ordinary least squares, two- and three-stage least squares, quasi maximum likelihood, and versions of these for nonlinear environments. For models estimated by any efficient method of moments technique, the authors define analogues to the maximum likeliho od based Wald, likelihood ratio, Lagrange multiplier, and minimum chi-squared statistics. They prove the mutual asymptotic equivalence of the four in an environment that allows for disturbances that are auto correlated and heteroskedastic. They also describe a very convenient way to test a linear hypothesis in a linear model. Copyright 1987 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
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
Mostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2009) · doi:10.1515/9781400829828 · Cited by 9,467
Financial intermediation and growth: Causality and causes
Levine, R., Loayza, N., & Beck, T. (2000) · Journal of Monetary Economics · doi:10.1016/s0304-3932(00)00017-9 · Cited by 4,414
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
Self-citationReturn Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility
ANDERSEN, T. G. (1996) · The Journal of Finance · doi:10.1111/j.1540-6261.1996.tb05206.x · Cited by 1,020
Arbitrage risk and the book-to-market anomaly
Ali, A., Hwang, L.-S., & Trombley, M. A. (2003) · Journal of Financial Economics · doi:10.1016/s0304-405x(03)00116-8 · Cited by 518
Metadata
- Authors
- Newey, W. K., & West, K. D.
- Venue
- International Economic Review
- Publisher
- JSTOR
- Published
- 1987-10
- Volume
- 28
- Issue
- 3
- Pages
- 777
- ISSN
- 0020-6598 (ISSN-L)
- OpenAlex
- W2003498865
- Cited by
- 1,844
- References
- 0
- FWCI
- 3.79
- 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: Hypothesis Testing with Efficient Method of Moments Estimation. https://app.cochat.ai/papers/10.2307/2526578
- The paper
- Newey, W. K., & West, K. D. (1987). Hypothesis Testing with Efficient Method of Moments Estimation. International Economic Review. https://doi.org/10.2307/2526578
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.