The large sample behaviour of the generalized method of moments estimator in misspecified models

Hall, Alastair R.; Inoue, Atsushi

doi:10.1016/s0304-4076(03)00089-7

Cited by 141 publications

(160 citation statements)

References 22 publications

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“…Our estimation and inference results are developed to account for potential misspeci…cation of the moment restriction (1), and we also discuss simpli…cation of the results when the model is correctly speci…ed. The asymptotic results developed below for semiparametric GMM estimation under global misspeci…cation with non-smooth moment functions are of independent interest and complement some of the previous results given by Hall and Inoue (2003) and Ai and Chen (2007) for smooth moments and point-identi…ed models.…”

Section: Uniform Inferencesupporting

confidence: 66%

Set inferences and sensitivity analysis in semiparametric conditionally identified models

Escanciano

Zhu

2013

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This paper provides tools for partial identi…cation inference and sensitivity analysis in a general class of semiparametric models. The main working assumption is that the …nite-dimensional parameter of interest and the possibly in…nite-dimensional nuisance parameters are identi…ed conditionally on other nuisance parameters being known. This structure arises in numerous applications and leads to relatively simple inference procedures. The paper develops uniform convergence for a set of semiparametric two-step GMM estimators, and it uses the uniformity to establish set inferences, including con…dence regions for the identi…ed set and the true parameter. Sensitivity analysis considers a domain of variation for the unidenti…ed parameter that can be well outside its identi…ed set, which demands inference to be established under misspeci…cation. The paper also introduces new measures of sensitivity. Inferences are implemented with new bootstrap methods. Several example applications illustrate the wide applicability of our results.

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Section: Uniform Inferencesupporting

confidence: 66%

Set inferences and sensitivity analysis in semiparametric conditionally identified models

Escanciano

Zhu

2013

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“…Note, however, that in the time-series context where the asymptotic variance-covariance matrix is estimated by HAC methods, the uncertainty in the estimation of the weight matrix will dominate the uncertainty in estimation of parameters and moments. This is because HAC estimators converge at a slower than square root-n rate (see, for example, Hall and Inoue (2003) for details). As a result, such a test may have poor power in …nite samples.…”

Section: Comparison Resultsmentioning

confidence: 99%

Limited Participation in International Business Cycle Models: A Formal Evaluation

2012

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“…In the second case, a GMM local misspecification in the sense of Hall and Inoue (2003) arises. In particular, local robustness of our RGMM test statistics for structural breaks ensures automatically stability of level and power under possible local GMM misspecifications.…”

Section: Asymptotic Neighbourhoodsmentioning

confidence: 99%

“…Matrix A is computed in the robust estimation algorithm, using an empirical version of the second implicit equation, and ensures a self-standardized sensitivity of the RGMM estimator bounded by c; see again equation (17). Models 3f and 3g represent local deviations which induce a local GMM model misspecification in the sense of Hall and Inoue (2003). More specifically, Model 3f implies a time homogeneous local departure from the reference model P 0 .…”

Section: Testing For Structural Breaks In Overidentified Modelsmentioning

confidence: 99%

Robust GMM Tests for Structural Breaks

Gagliardini

Trojani

Urga³

2003

SSRN Journal

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We propose a class of new robust GMM tests for endogenous structural breaks. The tests are based on supremum, average and exponential functionals derived from robust GMM estimators with bounded influence function. We study the theoretical local robustness properties of the new tests and show that they imply a uniformly bounded asymptotic sensitivity of size and power under general local deviations from a reference model. We then analyze the finite sample performance of the new robust tests in some Monte Carlo simulations, and compare it with that of classical GMM tests for structural breaks. In large samples, we find that the performance of classical asymptotic GMM tests can be quite unstable already under slight departures from some given reference distribution. In particular, the loss in power can be substantial in some models. Robust asymptotic tests for structural breaks yield important power improvements already under slight local departures from the reference model. This holds both in exactly identified and overidentified model settings. In small samples, bootstrapped versions of both the classical and the robust GMM tests provide a very accurate and very stable empirical size also for quite small sample sizes. However, bootstrapped robust GMM tests are found to provide again a higher finite sample efficiency.

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The large sample behaviour of the generalized method of moments estimator in misspecified models

Cited by 141 publications

References 22 publications

Set inferences and sensitivity analysis in semiparametric conditionally identified models

Set inferences and sensitivity analysis in semiparametric conditionally identified models

Limited Participation in International Business Cycle Models: A Formal Evaluation

Robust GMM Tests for Structural Breaks

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