Random Level-Shift Time Series Models, Arima Approximations, and Level-Shift Detection
Bibliographic Data
| ID | 19418151 |
|---|---|
| Authors | Chung Chen (Syracuse University), George C Tiao (University of Chicago) |
| Year | 1990 |
| Volume | 8 |
| Issue | 1 |
| Pages | 83-97 |
| Publication date | 1990-01-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.1990.10509779 |
| OpenAlex | W2020944421 |
| Language | EN |
| Citations received | 8 |
| References cited | 22 |
The main purpose of this article is to assess the performance of autoregressive integrated moving average (ARIMA) models when occasional level shifts occur in the time series under study. A random level-shift time series model that allows the level of the process to change occasionally is introduced. Between two consecutive changes, the process behaves like the usual autoregressive moving average (ARMA) process. In practice, a series generated from a random level-shift ARMA (RLARMA) model may be misspecified as an ARIMA process. The efficiency of this ARIMA approximation with respect to estimation of current level and forecasting is investigated. The results of examining a special case of an RLARMA model indicate that the ARIMA approximations are inadequate for estimating the current level, but they are robust for forecasting future observations except when there is a very low frequency of level shifts or when the series are highly negatively correlated. A level-shift detection procedure is presented to handle the low-frequency level-shift phenomena, and its usefulness in building models for forecasting is demonstrated
Autoregressive integrated moving average · Autoregressive model · Autoregressive–moving-average model · Econometrics · Moving average · Moving-average model · Statistics · Time series · Computer Science · Financial Risk and Volatility Modeling · Forecasting Techniques and Applications · Mathematics · Monetary Policy and Economic Impact
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| Citations per year | 0.22 |
|---|---|
| Citation span | 1990 - 2026 (37) |
| Highly cited | No |