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Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models

Author

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  • Jan F. Kiviet

    (Division of Economics, School of Humanities and Social Sciences, Nanyang Technological University, 14 Nanyang Drive, Singapore 637332;)

  • Milan Pleus

    (Amsterdam School of Economics & Tinbergen Institute, University of Amsterdam, Valckenierstraat 65, 1018 XE Amsterdam, The Netherlands)

  • Rutger Poldermans

    (Amsterdam School of Economics & Tinbergen Institute, University of Amsterdam, Valckenierstraat 65, 1018 XE Amsterdam, The Netherlands)

Abstract
The performance in finite samples is examined of inference obtained by variants of the Arellano-Bond and the Blundell-Bond GMM estimation techniques for single dynamic panel data models with possibly endogenous regressors and cross-sectional heteroskedasticity. By simulation the effects are examined of using particular instrument strength enhancing reductions and transformations of the matrix of instrumental variables, of less robust implementations of the GMM weighting matrix, and also of corrections to the standard asymptotic variance estimates. We compare the root mean squared errors of the coefficient estimators and also the size of tests on coefficient values and of different implementations of overidentification restriction tests. Also the size and power of tests on the validity of the additional orthogonality conditions exploited by the Blundell-Bond technique are assessed over a pretty wide grid of relevant cases. Surprisingly, particular asymptotically optimal and relatively robust weighting matrices are found to be superior in finite samples to ostensibly more appropriate versions. Most of the variants of tests for overidentification restrictions show serious deficiencies. A recently developed modification of GMM is found to have great potential when the cross-sectional heteroskedasticity is pronounced and the time-series dimension of the sample not too small. Finally all techniques are employed to actual data and lead to some profound insights.

Suggested Citation

  • Jan F. Kiviet & Milan Pleus & Rutger Poldermans, 2014. "Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models," Economic Growth Centre Working Paper Series 1415, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    • Handle: RePEc:nan:wpaper:1415
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    More about this item

    Keywords

    cross-sectional heteroskedasticity; Sargan-Hansen (incremental) tests; variants of t-tests; weighting matrices; Windmeijer-correction;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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