A Bayesian method to detect structural changes in multivariate dynamic linear model is introduced and it is applied to predicting and dating the turns in business cycle. As many researchers use for business cycle analysis, the composite leading index (CLI) and the composite coincident index...

It is well known that an unexpected level change in time series can cause persistent forecasting errors, depending on the change size and the underlying time series process. This relationship is demonstrated particularly with macroeconomic and financial time series. Forecasting literature...

Most forecasting models often fail to produce appropriate forecasts because they are built on the assumption that data is being generated from only one stochastic process. However, in many real world problems, the time series data are generated from one stochastic process initially and then...

The state space model is widely used to handle time series data driven by related latent processes in many fields. In this article, we suggest a framework to examine the relationship between state space models and ARIMA models by examining the existence and positive-definiteness conditions...

In this study we examine the long term behavior of stock returns. The analysis reveals that negative autocorrelations of the returns exist for a super-long horizon as long as 10 years. This pattern, however, contrasts to predictions of previous stock price models which include random walks. We...

The repeated occurrences of interventions in observations make most forecasting models fail to produce appropriate forecasts. The purpose of this study is to propose the adaptive forecasting procedure based on sequential identifications of interventions and adjusting forecast to them in general...