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Testing Parameters in GMM Without Assuming that They Are Identified
Abstract
We propose a generalized method of moments (GMM) Lagrange multiplier statistic, i.e., the K statistic, that uses a Jacobian estimator based on the continuous updating estimator that is asymptotically uncorrelated with the sample average of the moments. Its asymptotic χ-super-2 distribution therefore holds under a wider set of circumstances, like weak instruments, than the standard full rank case for the expected Jacobian under which the asymptotic χ-super-2 distributions of the traditional statistics are valid. The behavior of the K statistic can be spurious around inflection points and maxima of the objective function. This inadequacy is overcome by combining the K statistic with a statistic that tests the validity of the moment equations and by an extension of Moreira's (2003) conditional likelihood ratio statistic toward GMM. We conduct a power comparison to test for the risk aversion parameter in a stochastic discount factor model and construct its confidence set for observed consumption growth and asset return series. Copyright The Econometric Society 2005.
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Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. (1994) · doi:10.3386/t0151 · Cited by 5,302
Cited by
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Bun, M. J. G., & Windmeijer, F. (2010) · The Econometrics Journal · doi:10.1111/j.1368-423x.2009.00299.x · Cited by 498
On the effect of mean-nonstationarity in dynamic panel data models
Hayakawa, K. (2009) · Journal of Econometrics · doi:10.1016/j.jeconom.2009.04.008 · Cited by 41
Metadata
- Authors
- Kleibergen, F.
- Venue
- Econometrica
- Publisher
- The Econometric Society
- Published
- 2005-07
- Volume
- 73
- Issue
- 4
- Pages
- 1103-1123
- ISSN
- 0012-9682 (ISSN-L)
- 1468-0262
- OpenAlex
- W2567119387
- Cited by
- 360
- References
- 1
- FWCI
- 37.16
- Type
- journal article
- Language
- en
- Open access
- closed
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