Instrumental variables estimation of heteroskedastic linear models using all lags of instruments

Citation:

West, Kenneth D., Ka-fu Wong, and Stanislav Anatolyev (2009) Instrumental variables estimation of heteroskedastic linear models using all lags of instruments, Econometric Reviews, Vol. 28, No. 5, pp. 441467

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.

Paper in RePEc:

Econometric Reviews, Vol. 28, No. 5, pp. 441467

Paper in accepted version:

WWA.pdf

Additional appendix:

WWAaa.pdf

Cited by:

Gospodinov, N. & Otsu, T. (2012) "Local GMM estimation of time series models with conditional moment restrictions", Journal of Econometrics, Vol. 170, pp. 476-490.

Kuersteiner, G.M. (2012) "Kernel-weighted GMM estimators for linear time series models", Journal of Econometrics, Vol. 170, pp. 399-421.

Gourinchas, Pierre-Olivier and Jonathan A. Parker (2002) "Consumption over the life cycle", Econometrica, Vol. 70, pp. 47-89.

Grammig, J. and M. Wellner (2002) "Modeling the interdependence of volatility and inter-transaction duration processes", Journal of Econometrics, Vol. 106, pp. 369-400.