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Instrumental Variables Estimation of Heteroskedastic Linear Models Using All Lags of Instruments

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  • Kenneth D. West
  • Ka-fu Wong
  • Stanislav Anatolyev

Abstract

We propose and evaluate a technique for instrumental variables estimation of linear models with conditional heteroskedasticity. The technique uses approximating parametric models for the projection of right hand side variables onto the instrument space, and for conditional heteroskedasticity and serial correlation of the disturbance. Use of parametric models allows one to exploit information in all lags of instruments, unconstrained by degrees of freedom limitations. Analytical calculations and simulations indicate that there sometimes are large asymptotic and finite sample efficiency gains relative to conventional estimators (Hansen (1982)), and modest gains or losses depending on data generating process and sample size relative to quasi-maximum likelihood. These results are robust to minor misspecification of the parametric models used by our estimator.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0338.

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Date of creation: May 2007
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Publication status: published as "Instrumental Variables Estimation of Heteroskedastic Linear Models Using All Lags of Instruments,” (with Ka-fu Wong and Stanislav Anatolyev), Econometric Reviews 28 (5) (2009), 441-467.
Handle: RePEc:nbr:nberte:0338

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Cited by:
  1. Nikolay Gospodinov & Taisuke Otsu, 2008. "Local GMM Estimation of Time Series Models with Conditional Moment Restrictions," Working Papers, Concordia University, Department of Economics 08010, Concordia University, Department of Economics.
  2. David Laibson & Andrea Repetto & Jeremy Tobacman, 2007. "Estimating Discount Functions with Consumption Choices over the Lifecycle," NBER Working Papers 13314, National Bureau of Economic Research, Inc.
  3. Stanislav Anatolyev, 2007. "Optimal Instruments In Time Series: A Survey," Journal of Economic Surveys, Wiley Blackwell, Wiley Blackwell, vol. 21(1), pages 143-173, 02.
  4. Kuersteiner, Guido M., 2012. "Kernel-weighted GMM estimators for linear time series models," Journal of Econometrics, Elsevier, Elsevier, vol. 170(2), pages 399-421.
  5. Lars Peter Hansen, 2014. "Uncertainty Outside and Inside Economic Models," NBER Working Papers 20394, National Bureau of Economic Research, Inc.
  6. West, Kenneth D., 2002. "Efficient GMM estimation of weak AR processes," Economics Letters, Elsevier, Elsevier, vol. 75(3), pages 415-418, May.
  7. Elena Corallo, 2005. "The effect of the war risk: a comparison of the consequences of the two Iraq wars on some financial variables," LIUC Papers in Economics, Cattaneo University (LIUC) 171, Cattaneo University (LIUC).
  8. Lars Peter Hansen, 2014. "Uncertainty Outside and Inside Economic Models," Working Papers, Becker Friedman Institute for Research In Economics 2014-06, Becker Friedman Institute for Research In Economics.

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