Abstract
In time series analysis, autoregressive integrated moving average (ARIMA) models have found extensive use since the publication of Box and Jenkins (1976). For an introduction to the standard ARIMA modeling in S-PLUS, see S-PLUS Guide to Statistics. Regression models are also frequently used in finance and econometrics research and applications. For example, as “factor” models for empirical asset pricing research and for parsimonious covariance matrix estimation in portfolio risk models. Often ARIMA models and regression models are combined by using an ARIMA model to account for serially correlated residuals in a regression model, resulting in REGARIMA models.
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17.7 References
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(2006). Robust Change Detection. In: Modeling Financial Time Series with S-PLUS®. Springer, New York, NY. https://doi.org/10.1007/978-0-387-32348-0_17
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DOI: https://doi.org/10.1007/978-0-387-32348-0_17
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