Nonparametric Estimation and Identification of Nonlinear ARCH Time Series Strong Convergence and Asymptotic Normality: Strong Convergence and Asymptotic Normality

We consider the estimation and identification of the functional structures of nonlinear econometric systems of the ARCH type. We employ nonparametric kernel estimates for the nonlinear functions characterizing the systems, and we establish strong consistency along with sharp rates of convergence under mild regularity conditions. We also prove the asymptotic normality of the estimates.

[1]  T. Broadbent Measure and Integral , 1957, Nature.

[2]  V. Volkonskii,et al.  Some Limit Theorems for Random Functions. II , 1959 .

[3]  R. Tweedie Sufficient conditions for ergodicity and recurrence of Markov chains on a general state space , 1975 .

[4]  A. Zygmund,et al.  Measure and integral : an introduction to real analysis , 1977 .

[5]  P. Hall,et al.  Martingale Limit Theory and Its Application , 1980 .

[6]  Raymond J. Carroll,et al.  Adapting for Heteroscedasticity in Linear Models , 1982 .

[7]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[8]  C. J. Stone,et al.  Optimal Global Rates of Convergence for Nonparametric Regression , 1982 .

[9]  B. Silverman,et al.  Weak and strong uniform consistency of kernel regression estimates , 1982 .

[10]  P. Robinson NONPARAMETRIC ESTIMATORS FOR TIME SERIES , 1983 .

[11]  R. C. Bradley Approximation theorems for strongly mixing random variables. , 1983 .

[12]  H. Tong,et al.  On the use of the deterministic Lyapunov function for the ergodicity of stochastic difference equations , 1985, Advances in Applied Probability.

[13]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[14]  Timo Teräsvirta,et al.  Aspects of modelling nonlinear time series , 1986 .

[15]  P. Tuan The mixing property of bilinear and generalised random coefficient autoregressive models , 1986 .

[16]  Wolfgang Härdle,et al.  Strong Uniform Convergence Rates in Robust Nonparametric Time Series Analysis and Prediction: Kernel , 1986 .

[17]  P. Robinson ROOT-N-CONSISTENT SEMIPARAMETRIC REGRESSION , 1988 .

[18]  Adrian Pagan,et al.  The Econometric Analysis of Models with Risk Terms , 1988 .

[19]  R. Tweedie Invariant measures for Markov chains with no irreducibility assumptions , 1988 .

[20]  M. B. Priestley,et al.  Non-linear and non-stationary time series analysis , 1990 .

[21]  L. Tran Kernel density estimation on random fields , 1990 .

[22]  D. Tjøstheim Non-linear time series and Markov chains , 1990, Advances in Applied Probability.

[23]  G. Roussas Nonparametric regression estimation under mixing conditions , 1990 .

[24]  Ruey S. Tsay,et al.  On the Ergodicity of Tar(1) Processes , 1991 .

[25]  B. LeBaron,et al.  Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence , 1991 .

[26]  Jianqing Fan,et al.  Multivariate regression estimation with errors-in-variables: asymptotic normality for mixing processes , 1992 .

[27]  E. Masry Multivariate regression estimation with errors-in-variables for stationary processes , 1993 .

[28]  Dag Tjøstheim,et al.  Nonparametric Identification of Nonlinear Time Series: Selecting Significant Lags , 1994 .