The purpose of this book is to establish a connection between the traditional field of empirical economic research and the emerging area of empirical financial research, and to build a bridge between theoretical developments in these areas and their application in practice. Accordingly, it...

We investigate the behavior of nonparametric kernel M-estimators in the presence of long-memory errors. The optimal bandwidth and a central limit theorem are obtained. It turns out that in the Gaussian case all kernel M-estimators have the same limiting normal distribution. The motivation behind...

Prediction in time series models with a trend requires reliable estimation of the trend function at the right end of the observed series. Local polynomial smoothing is a suitable tool because boundary corrections are included implicitly. However, outliers may lead to unreliable estimates, if...

Recent results on so-called SEMIFAR models introduced by Beran (1997) are discussed. The nonparametric deterministic trend is estimated by a kernel method. The differencing and fractional differencing parameters as well as the autoregressive coefficients are estimated by an approximate maximum...

We consider M-estimation of a location parameter for processes with zero autocorrelations but long-range dependence in volatility. The observed process is the product of i.i.d. Gaussian observations and a long-memory Gaussian process. For nonlinear estimators, the rate of convergence depends on...

We consider dependence structures in multivariate time series that are characterized by deterministic trends. Results from spectral analysis for stationary processes are extended to deterministic trend functions. A regression cross covariance and spectrum are defined. Estimation of these...