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

DiTraglia, Francis J.

doi:10.2139/ssrn.2516255

Cited by 16 publications

(15 citation statements)

References 64 publications

(93 reference statements)

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“…11 Given true (B, Ω), let C(B, Ω) ⊂ C be the subset of averaging weights such thatĉ = c(δ S c ,B,Ω) is continuous a.e. inδ S c when (B,Ω)…”

Section: At T Smentioning

confidence: 99%

Model averaging in semiparametric estimation of treatment effects

Kitagawa

Muris²

2015

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In the practice of program evaluation, choosing the covariates and the functional form of the propensity score is an important choice that the researchers make when estimating treatment effects. This paper proposes a data-driven way of averaging the estimators over the candidate specifications in order to resolve the issue of specification uncertainty in the propensity score weighting estimation of the average treatment effects for treated (ATT). The proposed averaging procedures aim to minimize the estimated mean squared error (MSE) of the ATT estimator in a local asymptotic framework. We formulate model averaging as a statistical decision problem in a limit experiment, and derive an averaging scheme that is Bayes optimal with respect to a given prior for the localization parameters. Analytical comparisons of the Bayes asymptotic MSE show that the averaging estimator outperforms post model selection estimators and the estimators in any of the candidate models. Our Monte Carlo studies confirm these theoretical results and illustrate the size of the MSE gains from averaging. We apply the averaging procedure to evaluate the effect of the labor market program analyzed in LaLonde (1986).Keywords: Treatment effects, Propensity score, Model averaging, Limit experiment.JEL Classification: C13, C21, C52. * We thank Debopam Bhattacharya, Irene Botosaru, Xiaohong Chen, Christian Hansen, Yuichi Kitamura, Frank Kleibergen, Michael Lechner, Simon Lee, Richard Smith, and Frank Windmeijer for valuable comments and discussions. We thank an associate editor and three referees for their thorough reading and constructive comments on an earlier draft. We also thank the seminar participants at AMES 2013, University of Bristol, University of British Columbia, Brown University, the Cemmap/PEPA workshop on Program Evaluation, University of Groningen, University of Sankt Gallen, Tilburg University, Toulouse School of Economics, and Yale Econometrics Lunch for their helpful comments. All remaining errors are ours. Financial support from the ESRC through the ESRC Centre for Microdata Methods and Practice (CeMMAP) (grant number RES-589-28-0001) is gratefully acknowledged.

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“…11 Given true (B, Ω), let C(B, Ω) ⊂ C be the subset of averaging weights such thatĉ = c(δ S c ,B,Ω) is continuous a.e. inδ S c when (B,Ω)…”

Section: At T Smentioning

confidence: 99%

Model averaging in semiparametric estimation of treatment effects

Kitagawa

Muris²

2015

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“…9 Using stratification and matching on the estimated propensity score, DW's adjusted estimates are $1774 (standard error: $1152) and $1616 (standard error: $751), respectively. DW do not provide an in-depth discussion of how the covariates for the propensity score were chosen, but they describe that their results are sensitive to excluding higher 7 The data is available from Raheev Dehejia's website.…”

Section: Empirical Applicationmentioning

confidence: 99%

“…We prefer the CPS over the PSID because of the larger sample size (n = 15992). 9 The unadjusted estimate is $1794 with a standard error of $633. order terms and to excluding 1974 earnings.…”

Section: Empirical Applicationmentioning

confidence: 99%

Covariate selection and model averaging in semiparametric estimation of treatment effects

Kitagawa¹,

Muris²

2013

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In the practice of program evaluation, choosing the covariates and the functional form of the propensity score is an important choice for estimating treatment effects. This paper proposes data-driven model selection and model averaging procedures that address this issue for the propensity score weighting estimation of the average treatment effects for treated (ATT).Building on the focussed information criterion (FIC), the proposed selection and averaging procedures aim to minimize the estimated mean squared error (MSE) of the ATT estimator in a local asymptotic framework. We formulate model averaging as a statistical decision problem in a limit experiment, and derive an averaging scheme that is Bayes optimal with respect to a given prior for the localisation parameters in the local asymptotic framework. In our Monte Carlo studies, the averaging estimator outperforms the post-covariate-selection estimator in terms of MSE, and shows a substantial reduction in MSE compared to conventional ATT estimators. We apply the procedures to evaluate the effect of the labor market program described in LaLonde (1986).Keywords: Treatment effects, Propensity score, Model selection, Focussed information criterion, Model averaging. * We thank Debopam Bhattacharya, Irene Botosaru, Xiaohong Chen, Christian Hansen, Yuichi Kitamura, Frank Kleibergen, Michael Lechner, Simon Lee, Richard Smith, and Frank Windmeijer for valuable comments and discussions. We also thank the seminar participants at AMES 2013,

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“…Two additions to the treatment e ects literature are Lu (2015) and Kitagawa and Muris (2016). DiTraglia (2016) provides results for generalized method of moments estimation. Liu and Kuo (2016) consider predictive regressions.…”

Section: Introductionmentioning

confidence: 99%

Focused information criterion for locally misspecified vector autoregressive models

Lohmeyer

Palm

Reuvers

et al. 2018

Econometric Reviews

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This paper investigates the focused information criterion and plug-in average for vector autoregressive models with local-to-zero misspeci cation. These methods have the advantage of focusing on a quantity of interest rather than aiming at overall model t. Any (su ciently regular) function of the parameters can be used as a quantity of interest. We determine the asymptotic properties and elaborate on the role of the locally misspeci ed parameters. In particular, we show that the inability to consistently estimate locally misspeci ed parameters translates into suboptimal selection and averaging. We apply this framework to impulse response analysis. A Monte Carlo simulation study supports our claims.

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

Cited by 16 publications

References 64 publications

Model averaging in semiparametric estimation of treatment effects

Model averaging in semiparametric estimation of treatment effects

Covariate selection and model averaging in semiparametric estimation of treatment effects

Focused information criterion for locally misspecified vector autoregressive models

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