In this paper a new class of Instrumental Variables estimators for linear processes and in particular ARMA models is developed. Previously, IV estimators based on lagged observations as instruments have been used to account for unmodelled MA(q) errors in the estimation of the AR parameters. Here it is shown that these IV methods can be used to improve efficiency of linear time series estimators...

Recent time series methods are applied to the problem of forecasting New Zealand’s real GDP. Model selection is conducted within autoregressive (AR) and vector autoregressive (VAR) classes, allowing for evolution in the form of the models over time. The selections are performed using the Schwarz (1978) BIC and the Phillips-Ploberger (1996) PIC criteria. The forecasts generated by the data-deter...

This article proposes a Bayesian infinite mixture model for the estimation of the conditional density of an ergodic time series. A nonparametric prior on the conditional density is described through the Dirichlet process. In the mixture model, a kernel is used leading to a dynamic nonlinear autoregressivemodel. This model can approximate any linear autoregressivemodel arbitrarily closely while ...

A moment bound for the normalized conditional-sum-of-squares (CSS) estimate of a general autoregressive fractionally integrated moving average (ARFIMA) model with an arbitrary unknown memory parameter is derived in this paper. To achieve this goal, a uniform moment bound for the inverse of the normalized objective function is established. An important application of these results is to establis...