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A strategy to reduce the count of moment conditions in panel data GMM

Author

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  • M. E. Bontempi
  • I. Mammi

Abstract

The problem of instrument proliferation and its consequences (overfitting of endogenous variables, bias of estimates, weakening of Sargan/Hansen test) are well known. The literature provides little guidance on how many instruments is too many. It is common practice to report the instrument count and to test the sensitivity of results to the use of more or fewer instruments. Strategies to alleviate the instrument proliferation problem are the lag-depth truncation and/or the collapse of the instrument set (the latter being an horizontal squeezing of the instrument matrix). However, such strategies involve either a certain degree of arbitrariness (based on the ability and the experience of the researcher) or of trust in the restrictions implicitly imposed (and hence untestable) on the instrument matrix. The aim of the paper is to introduce a new strategy to reduce the instrument count. The technique we propose is statistically founded and purely datadriven and, as such, it can be considered a sort of benchmark solution to the problem of instrument proliferation. We apply the principal component analysis (PCA) on the instrument matrix and exploit the PCA scores as the instrument set for the panel generalized method-of-moments (GMM)estimation. Through extensive Monte Carlo simulations, under alternative characteristics of persistence of the endogenous variables, we compare the performance of the Difference GMM, Level and System GMM estimators when lag truncation, collapsing and our principal component-based IV reduction (PCIVR henceforth) are applied to the instrument set. The same comparison has been carried out with two empirical applications on real data: the first replicates the estimates of Blundell and Bond [1998]; the second exploits a new and large panel data-set in order to assess the role of tangible and intangible capital on productivity. Results show that PCIVR is a promising strategy of instrument reduction.

Suggested Citation

  • M. E. Bontempi & I. Mammi, 2012. "A strategy to reduce the count of moment conditions in panel data GMM," Working Papers wp843, Dipartimento Scienze Economiche, Universita' di Bologna.
  • Handle: RePEc:bol:bodewp:wp843
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    References listed on IDEAS

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    1. Maria Elena Bontempi & Jacques Mairesse, 2008. "Intangible Capital and Productivity: An Exploration on a Panel of Italian Manufacturing Firms," NBER Working Papers 14108, National Bureau of Economic Research, Inc.
    2. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
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    6. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2004. "The generalized dynamic factor model consistency and rates," Journal of Econometrics, Elsevier, vol. 119(2), pages 231-255, April.
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    Cited by:

    1. Ferreira, Francisco H. G. & Lakner, Christoph & Lugo, Maria Ana & Ozler, Berk, 2014. "Inequality of opportunity and economic growth : a cross-country analysis," Policy Research Working Paper Series 6915, The World Bank.
    2. Fendel Tanja, 2016. "Migration and Regional Wage Disparities in Germany," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(1), pages 3-35, February.
    3. I. Mammi, 2015. "GMM estimation of fiscal rules: Monte Carlo experiments and empirical tests," Working Papers wp1028, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. repec:eee:streco:v:42:y:2017:i:c:p:56-66 is not listed on IDEAS
    5. Bergman, U. Michael & Hutchison, Michael, 2015. "Economic stabilization in the post-crisis world: Are fiscal rules the answer?," Journal of International Money and Finance, Elsevier, vol. 52(C), pages 82-101.
    6. Michal Brzezinski, 2013. "Income Polarization and Economic Growth," LIS Working papers 587, LIS Cross-National Data Center in Luxembourg.
    7. repec:eee:quaeco:v:64:y:2017:i:c:p:44-56 is not listed on IDEAS
    8. Maria Elena Bontempi, 2013. "The Istat MeMo-It Macroeconometric Model: comments and suggestions for possible extensions," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(1), pages 47-56.
    9. Bergman, U. Michael & Hutchison, Michael M. & Jensen, Svend E. Hougaard, 2016. "Promoting sustainable public finances in the European Union: The role of fiscal rules and government efficiency," European Journal of Political Economy, Elsevier, vol. 44(C), pages 1-19.

    More about this item

    JEL classification:

    • 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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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