Regularity of Maximum Likelihood Estimation for ARCH Regression Model with Lagged Dependent Variables

  • Hwang, Sun Y. (Department of Statistics, Sookmyung Women)
  • Published : 2000.03.01

Abstract

This article addresses the problem of maximum likelihood estimation in ARCH regression with lagged dependent variables. Some topics in asymptotics of the model such as uniform expansion of likelihood function and construction of a class of MLE are discussed, and the regularity property of MLE is obtained. The error process here is possibly non-Gaussian.

Keywords

References

  1. Statistics and Probability Letters v.31 The geometric erodicity and existencde of moments for z class of non-linear time series models An;H. Z.;Chen;M.;Huang;F. C.
  2. Journal of Econometrics v.52 Stationarity of GARCH processes and some non-negative time series Bougeral;P.;Picard;N.
  3. Journal of Econometrics v.81 Efficient estimation in semiparmetric GARCH models Drost;F. C.;Klaassen;C. A. J.
  4. Annals of statistics v.25 Adaptive estimates in time series models Drost;F. C.;Klaassen;C. A. J.;Werker;B. J. M.
  5. Econometrica v.50 Autoregressive conditonal heteroscedasticty with estimates of the variance of U.K. inflation Engle;R. F.
  6. International Statistics Review v.58 On Large-Sample estimation and testing in parametric models Hall;W. J.;Mathiason;D. J.
  7. Some Applications in Statistics Contiguity of Probability Meausures Roussas;G. G.
  8. Annals of Statistics v.8 Uniform asymptotic normality of the maximum likelihood estimator Sweeting;T. J.
  9. Econometric Theory v.2 Asymptotic Theory for ARCH models: estimation and testing Weiss;A. A.