Next Article in Journal
A Simple Test for Causality in Volatility
Previous Article in Journal
Goodness-of-Fit Tests for Copulas of Multivariate Time Series
Previous Article in Special Issue
Subset-Continuous-Updating GMM Estimators for Dynamic Panel Data Models
Article Menu

Export Article

Open AccessArticle
Econometrics 2017, 5(1), 14; doi:10.3390/econometrics5010014

Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models

1
Amsterdam School of Economics, University of Amsterdam, P.O. Box 15867, 1001 NJ Amsterdam,The Netherlands
2
IKZ, Newtonlaan 1-41, 3584 BX Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 28 December 2016 / Revised: 6 March 2017 / Accepted: 10 March 2017 / Published: 20 March 2017
(This article belongs to the Special Issue Recent Developments in Panel Data Methods)
View Full-Text   |   Download PDF [1134 KB, uploaded 22 March 2017]

Abstract

Studies employing Arellano-Bond and Blundell-Bond generalized method of moments (GMM) estimation for linear dynamic panel data models are growing exponentially in number. However, for researchers it is hard to make a reasoned choice between many different possible implementations of these estimators and associated tests. By simulation, the effects are examined in terms of many options regarding: (i) reducing, extending or modifying the set of instruments; (ii) specifying the weighting matrix in relation to the type of heteroskedasticity; (iii) using (robustified) 1-step or (corrected) 2-step variance estimators; (iv) employing 1-step or 2-step residuals in Sargan-Hansen overall or incremental overidentification restrictions tests. This is all done for models in which some regressors may be either strictly exogenous, predetermined or endogenous. 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 and coefficient restrictions show serious deficiencies. The variance of the individual effects is shown to be a major determinant of the poor quality of most asymptotic approximations; therefore, the accurate estimation of this nuisance parameter is investigated. A modification of GMM is found to have some potential when the cross-sectional heteroskedasticity is pronounced and the time-series dimension of the sample is not too small. Finally, all techniques are employed to actual data and lead to insights which differ considerably from those published earlier. View Full-Text
Keywords: cross-sectional heteroskedasticity; model specification strategy; Sargan-Hansen (incremental) tests; variants of t-tests; weighting matrices; Windmeijer-correction cross-sectional heteroskedasticity; model specification strategy; Sargan-Hansen (incremental) tests; variants of t-tests; weighting matrices; Windmeijer-correction
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Kiviet, J.; Pleus, M.; Poldermans, R. Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models. Econometrics 2017, 5, 14.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Econometrics EISSN 2225-1146 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top