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We consider estimation of the linear component of a partial linear model when errors and regressors have long-range dependence. Assuming that errors and the stochastic component of regressors are linear processes with i.i.d. innovations, we closely examine the asymptotic properties of the OLS...
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We consider nonparametric estimation of the conditional qth quantile for stationary time series. We deal with stationary time series with strong time dependence and heavy tails under the setting of random design. We estimate the conditional qth quantile by local linear regression and investigate...
Persistent link: https://www.econbiz.de/10010634437
We consider nonparametric estimation of the conditional qth quantile for stationary time series. We deal with stationary time series with strong time dependence and heavy tails under the setting of random design. We estimate the conditional qth quantile by local linear regression and investigate...
Persistent link: https://www.econbiz.de/10008838432
We derive noncentral limit theorems for the partial sum processes of K(Xi)‐E{K(Xi)}, where K(x) is a bounded function and {Xi } is a linear process. We assume the innovations of {Xi } are independent and identically distributed and that the distribution of the innovations is an α-stable law...
Persistent link: https://www.econbiz.de/10004992591
We analyze the applicability of standard normal asymptotic theory for linear process models near the boundary of stationarity. Limit results are given for estimation of the mean, autocovariance and autocorrelation functions within the broad region of stationarity that includes near boundary...
Persistent link: https://www.econbiz.de/10010664694
This paper considers a partially linear model of the form y = x beta + g(t) + e, where beta is an unknown parameter vector, g(.) is an unknown function, and e is an error term. Based on a nonparametric estimate of g(.), the parameter beta is estimated by a semiparametric weighted least squares...
Persistent link: https://www.econbiz.de/10011112439
We analyze the applicability of standard normal asymptotic theory for linear process models near the boundary of stationarity. The concept of stationarity is refined, allowing for sample size dependence in the array and paying special attention to the rate at which the boundary unit root case is...
Persistent link: https://www.econbiz.de/10005593612
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