Central limit theorem for linear processes (Q1356349)

The authors study the CLT for standardized partial sums of a generalized linear process \(X_n=\sum^n_1 a_{ni} Y_i\), where \(\sup_n\sum^n_1 a^2_{ni} <\infty\), \(\max|a_{ni}|\to 0\) as \(n\to \infty\) and \(Y_i\)'s are random variables satisfying one of dependence conditions: pairwise mixing martingale differences, \(\Phi\)-mixing, \(\varphi\)-mixing, strong mixing sequences or associated sequences. The results are useful in analyzing the asymptotical properties of some well-known estimators like kernel estimator in the nonlinear regression model as well as of linear processes.