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Using Samples of Unequal Length in Generalized Method of Moments Estimation

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  • Anthony W. Lynch
  • Jessica A. Wachter

Abstract

Many applications in financial economics use data series with different starting or ending dates. This paper describes estimation methods, based on the generalized method of moments (GMM), which make use of all available data for each moment condition. We introduce two asymptotically equivalent estimators that are consistent, asymptotically normal, and more efficient asymptotically than standard GMM. We apply these methods to estimating predictive regressions in international data and show that the use of the full sample affects point estimates and standard errors for both assets with data available for the full period and assets with data available for a subset of the period. Monte Carlo experiments demonstrate that reductions hold for small-sample standard errors as well as asymptotic ones.

Suggested Citation

  • Anthony W. Lynch & Jessica A. Wachter, 2008. "Using Samples of Unequal Length in Generalized Method of Moments Estimation," NBER Working Papers 14411, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:14411
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    Cited by:

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    2. Sönksen, Jantje & Grammig, Joachim, 2021. "Empirical asset pricing with multi-period disaster risk: A simulation-based approach," Journal of Econometrics, Elsevier, vol. 222(1), pages 805-832.
    3. Martin M. Andreasen & Tom Engsted & Stig V. Møller & Magnus Sander, 2016. "Bond Market Asymmetries across Recessions and Expansions: New Evidence on Risk Premia," CREATES Research Papers 2016-26, Department of Economics and Business Economics, Aarhus University.
    4. Efstathios Avdis & Jessica A. Wachter, 2013. "Maximum likelihood estimation of the equity premium," NBER Working Papers 19684, National Bureau of Economic Research, Inc.
    5. Barras, Laurent & Malkhozov, Aytek, 2016. "Does variance risk have two prices? Evidence from the equity and option markets," Journal of Financial Economics, Elsevier, vol. 121(1), pages 79-92.
    6. Jonathan Fletcher & Patricia Ntozi-Obwale, 2009. "Exploring the Conditional Performance of U.K. Unit Trusts," Journal of Financial Services Research, Springer;Western Finance Association, vol. 36(1), pages 21-44, August.
    7. Yiying Cheng & Yaozhong Hu & Hongwei Long, 2020. "Generalized moment estimators for $$\alpha $$α-stable Ornstein–Uhlenbeck motions from discrete observations," Statistical Inference for Stochastic Processes, Springer, vol. 23(1), pages 53-81, April.
    8. Anthony W. Lynch & Oliver Randall, 2011. "Why Surplus Consumption in the Habit Model May be Less Persistent than You Think," NBER Working Papers 16950, National Bureau of Economic Research, Inc.
    9. Laurent Barras & Aytek Malkhozov, 2015. "Does variance risk have two prices? Evidence from the equity and option markets," BIS Working Papers 521, Bank for International Settlements.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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