The possibility of confusing long memory behavior with structural changes need to specify what kind of long memory behavior is concerned in literature and applications. One attraction of long memory models is that they imply different long run predictions and effects of shocks to conventional...

This paper supplements Dark (2003c) where bivariate error correction GARCH and FIGARCH models between the All Ordinaries Index and its Share Price Index (SPI) futures are used to estimate dynamic minimum variance hedge ratios (MVHRs). Dark (2003c) documents the importance of allowing for long...

This paper examines the long-run dynamics and the cyclical structure of the US stock market using fractional integration techniques. We implement a version of the tests of Robinson (1994a), which enables one to consider unit (or fractional) roots both at the zero (long-run) and at the cyclical...

The paper considers tests for structural change in time series regression models where both regressors and residuals may exhibit long range dependence. The limiting distribution of the test statistic depends on unknown parameters and is approximated by a bootstrap procedure. The asymptotic...

In this paper we consider different periodic extensions of regression models with autoregressive fractionally integrated moving average disturbances for the analysis of daily spot prices of electricity. We show that day-of-the-week periodicity and long memory are important determinants for the...

Long memory models have been successfully used to investigate the dynamic time-series behavior of inflation rates based on the CPI and WPI. However, almost no attention has been paid to import and export price inflation, nor to the terms of trade which they make up. This article investigates the...

This paper presents a rigurous framework for evaluating alternative forecasting methods for Chilean industrial production and sales. While nonlinear features appear to be important for forecasting the very short term, simple univariate linear models perform about as well for almost every...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors should be serially uncorrelated at the single period horizon with...

A common problem in out-of-sample prediction is that there are potentially many relevant predictors that individually have only weak explanatory power. We propose bootstrap aggregation of pre-test predictors (or bagging for short) as a means of constructing forecasts from multiple regression...

In real time forecasting, the sample is usually split into an estimation period of R observations and a prediction period of P observations, where T=R+P. Parameters are often estimated in a recursive manner, initially using R observations, then R+1 observations and so on until T-1 observations...