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Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM

Francis J. DiTraglia

arXiv 4 Aug 2014 · Statistics — Methodology · publishedJournal of Econometrics (2016) · 59 citations (OpenAlex)

arXiv:1408.0705 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In finite samples, the use of a slightly endogenous but highly relevant instrument can reduce mean-squared error (MSE). Building on this observation, I propose a novel moment selection procedure for GMM -- the Focused Moment Selection Criterion (FMSC) -- in which moment conditions are chosen not based on their validity but on the MSE of their associated estimator of a user-specified target parameter. The FMSC mimics the situation faced by an applied researcher who begins with a set of relatively mild "baseline" assumptions and must decide whether to impose any of a collection of stronger but more controversial "suspect" assumptions. When the (correctly specified) baseline moment conditions identify the model, the FMSC provides an asymptotically unbiased estimator of asymptotic MSE, allowing us to select over the suspect moment conditions. I go on to show how the framework used to derive the FMSC can address the problem of inference post-moment selection. Treating post-selection estimators as a special case of moment-averaging, in which estimators based on different moment sets are given data-dependent weights, I propose simulation-based procedures for inference that can be applied to a variety of formal and informal moment-selection and averaging procedures. Both the FMSC and confidence interval procedures perform well in simulations. I conclude with an empirical example examining the effect of instrument selection on the estimated relationship between malaria and income per capita.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Andrews, D. W. K., May (1999) Consistent moment selection procedures for generalized methods of moments estimation0.91613577%
2Guggenberger, P (2010) The impact of a Hausman pretest on the asymptotic size of a hypothesis test0.84333100%
3Hall, A. R (2005) Generalized Method of Moments0.84333100%
4Hall, A. R., Peixe, F. P (2003) A consistent method for the selection of relevant instruments in linear models0.7374350%
5Andrews, D. W. K., Lu, B (2001) Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models0.73732100%
6Claeskens, G., Hjort, N. L (2003) The focused information criterion0.73732100%
7Hong, H., Preston, B., Shum, M (2003) Generalized empirical likelihood-based model selection for moment condition models0.73732100%
8Leeb, H., Pötscher, B. M (2005) Model selection and inference: Facts and fiction0.73732100%
9Phillips, P. C. B (1980) The exact distribution of instrumental variables estimators in an equation containing $n+1$ endogenous variables0.73732100%
10Carstensen, K., Gundlach, E (2006) The primacy of institutions reconsidered: Direct income effects of malaria prevelance0.69381100%

Showing the top 10 of 67 scored citations.

Cited by, within the corpus

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1A Generalized Focused Information Criterion for GMM0.899115
2Sensitivity Analysis using Approximate Moment Condition Models0.73732
3Bayesian Model Averaging in Causal Instrumental Variable Models0.73732
4Tilting Approximate Models0.40511
5Focused econometric estimation for noisy and small datasets: A Bayesian Minimum Expected Loss estimator approach0.40511
6Frequentist Shrinkage Under Inequality Constraints0.40511
7GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications0.40511
8Confidence intervals for intentionally biased estimators0.40511
9Focused Weighted-Average Least Squares Estimator0.40511
10Inference methods for unit-specific coefficients in panel data models with latent group structure0.40511