Forecasting a long memory process subject to structural breaks

Forecasting Long Memory Processes Subject to Structural Breaks

We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an autoregressive (AR) model and suggest using an information criterion (AIC o...

Long-memory versus structural breaks: An overview

We discuss the increasing literature on misspecifying structural breaks or more general trends as long range dependence We consider tests on structural breaks in the long memory regression model as well as the behaviour of estimators of the memory parameter when structural breaks or trends are in the data but long memory is not It can be seen that it is hard to distinguish deterministic trends ...

Learning, Forecasting and Structural Breaks

The literature on structural breaks focuses on ex post identification of break points that may have occurred in the past. While this question is important, a more challenging problem facing econometricians is to provide forecasts when the data generating process is unstable. The purpose of this paper is to provide a general methodology for forecasting in the presence of model instability. We ma...

Forecasting in Dynamic Factor Models Subject to Structural Instability

Intelligent forecasting for financial time series subject to structural changes

This paper is mainly concerned about intelligent forecasting for financial time series subject to structural changes. For example, it is well known that interest rates are subject to structural changes due to external shocks such as government monetary policy change. Such structural changes usually make prediction harder if they are not properly taken care of. Recently, Oh and Kim (2002a, 2002b...