The seasonal structure of quarterly UK and Japanese consumption and income is examined by means of fractionally-based tests proposed by Robinson (1994). These series were analysed from an autoregressive unit root viewpoint by Hylleberg, Engle, Granger and Yoo (HEGY, 1990) and Hylleberg, Engle,...

The seasonal structure of quarterly UK and Japanese consumption and income is examined by means of fractionally-based tests proposed by Robinson (1994). These series were analysed from an autoregressive unit root viewpoint by Hylleberg, Engle, Granger and Yoo (HEGY, 1990) and Hylleberg, Engle,...

Much time series data are recorded on economic and financial variables. Statistical modelling of such data is now very well developed, and has applications in forecasting. We review a variety of statistical models from the viewpoint of ‘memory’, or strength of dependence across time, which...

This paper develops methods of investigating the existence and extent of cointegration in fractionally integrated systems. We focus on stationary series, with some discussion of extension to nonstationarity. The setting is semiparametric, so that modelling is effectively confined to a...

Employing recent results of Robinson (2005) we consider the asymptotic properties of conditional-sum-of-squares (CSS) estimates of parametric models for stationary time series with long memory. CSS estimation has been considered as a rival to Gaussian maximum likelihood and Whittle estimation of...

The semiparametric local Whittle or Gaussian estimate of the long memory parameter is known to have especially nice limiting distributional properties, being asymptotically normal with a limiting variance that is completely known. However in moderate samples the normal approximation may not be...

We consider the long memory and leverage properties of a model for the conditional variance of an observable stationary sequence, where the conditional variance is the square of an inhomogeneous linear combination of past values of the observable sequence, with square summable weights. This...

Smoothed nonparametric estimates of the spectral density matrix at zero frequency have been widely used in econometric inference, because they can consistently estimate the covariance matrix of a partial sum of a possibly dependent vector process. When elements of the vector process exhibit long...

We show that it is possible to adapt to nonparametric disturbance autocorrelation in time series regression in the presence of long memory in both regressors and disturbances by using a smoothed nonparametric spectrum estimate in frequency-domain generalized least squares. When the collective...