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Original Articles

Local M-estimation for Conditional Variance in Heteroscedastic Regression Models

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Pages 48-62 | Received 17 Nov 2011, Accepted 28 Sep 2012, Published online: 22 Oct 2014

References

  • Bandi, F., Phillips, P. (2003). Fully nonparametric estimation of scalar diffusion models. Econometrica 71:241283.
  • Borkovec, M. (2001). Asymptotic behavior of the sample auto covariance and auto correlation function of the AR(1) process with ARCH(1). Bernoulli 6:847872.
  • Borkovec, M., Klüppelberg, C. (2001). The tail of the stationary distribution of an autoregressive process with ARCH(1) errors. Ann. Appl. Probab. 11:12201241.
  • Cai, Z., Ould-Said, E. (2003). Local M-estimator for nonparametric time series. Statist. Probab. Lett. 65:433449.
  • Chen, L., Cheng, M., Peng, L. (2009). Conditional variance estimation in heteroscedastic regression models. J. Statist. Plann. Infer. 139:236245.
  • Davis, R.A., Knight, K., Liuc, J. (1992). M-estimation for autoregressions with infinite variance. Stoch. Process. Their Applic. 40:145180.
  • Delecroix, M., Hristache, M., Patilea, V. (2006). On semiparametric M-estimation in single-index regression. J. Statist. Plann. Infer. 136:730769.
  • Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of U.K. inflation. Econometrica 50:9871008.
  • Fan, J., Gijbels, I. (1996). Local Polynomial Modelling and its Applications. London: Chapman & Hall.
  • Fan, J., Jiang, J. (2000). Variable bandwidth and one-step local M-estimator. Sci. China Ser. A 43:6581.
  • Fan, J., Yao, Q. (1998). Efficient estimation of conditional variance functions in stochastic regression. Biometrika 85:645–660.
  • Franses, P.H. (1998). Time Series Models for Business and Economic Forecasting. New York: Cambridge University Press.
  • Hall, P., Jones, M.C. (1990). Adaptive M-estimation in nonparametric regression. Ann. Statist. 18:17121728.
  • Härdle, W. (1984). Robust regression function estimation. J. Multivariate Anal. 14:169180.
  • Härdle, W. (1989). Asymptotic maximal derivation of M-smoothes. J. Multivariate Anal. 29:163179.
  • Huber, P.J. (1973). Robust regression. Ann. Statist. 1:799821.
  • Jiang, J.C., Mack, Y.P. (2001). Robust local polynomial regression for dependent data. Statistica Sinica 11:705722.
  • Øksendal, B. (2005). Stochastic Differential Equations: An Introduction with Applications 6th ed. New York: Springer.
  • Ruppert, D., Wand, M.P., Holst, U., Hössjer, O. (1997). Local polynomial variance function estimation. Technometrics 39:262273.
  • Stigler, S.M. (1973). Simon Newcomb, Percy Daniell, and the history of robust estimation 1885-1920. J. Amer. Statist. Assoc. 68:872879.
  • Tjøtheim, D. (1994). Nonlinear time series: a selective review. Scand. J. Statist. 21:97130.
  • Wang, Y.Y., Zhang, L.X., Tang, M.T. (2012). Re-weighted functional estimation of second-order diffusion processes. Metrika 75:11291151.
  • Xu, K.L., Phillips, P. C.B. (2011). Tilted nonparametric estimation of volatility functions with empirical applications. J. Busi. Econo. Statist. 29:518528.
  • Yu, K., Jones, M.C. (2004). Likelihood-based local linear estimation of the conditional variance function. J. Amer. Statist. Assoc. 99:139144.
  • Ziegelmann, F.A. (2002). Nonparametric estimation of volatility functions: the local exponential estimator. Econometric Theory 18:985991.

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