The asymptotic behaviour of nonparametric estimators of the stationary density and of the spectral density function of a stationary process have been studied in some detail in the last 50-60years. Nevertheless, less is ...

Given stationary time series data, we study the problem of finding the best linear combination of a set of lag window spectral density estimators with respect to the mean squared risk. We present an aggregation procedure ...

The multivariate time-varying volatility models have recently attracted a lot of attention in the statistics/econometrics community. We apply two bivariate ARCH-GARCH models and a bivariate unobserved ARCH model to a series ...

The problem of spectral estimation on the basis of observations from a finite stretch of a stationary time series is considered, in connection with knowledge of a prior estimate of the spectral density. In general, the ...

It is shown that the limiting distribution of the augmented Dickey–Fuller (ADF) test under the null hypothesis of a unit root is valid under a very general set of assumptions that goes far beyond the linear AR(∞) process ...

Bagging and subagging procedures are put forth with the purpose of improving the discovery power in the context of large-scale simultaneous hypothesis testing. Bagging and subagging significantly improve discovery power ...

A central problem in time series analysis is prediction of a future observation. The theory of optimal linear prediction has been well understood since the seminal work of A. Kolmogorov and N. Wiener during World War II. ...

Abstract. The theory of nonparametric spectral density estimation based on an observed stretch X1,…, XN from a stationary time series has been studied extensively in recent years. However, the most popular spectral estimators, ...

We develop some asymptotic theory for applications of block bootstrap resampling schemes to multivariate integrated and cointegrated time series. It is proved that a multivariate, continuous-path block bootstrap scheme ...