Returns an array of cells for the estimated error/standard deviation of the model's parameters.
ARMA_ERRORS(X, Order, mean, sigma, phi, theta)
- = the univariate time series data (a one-dimensional array of cells (e.g. rows or columns)).
- = the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).
Order Description 1 ascending (the first data point corresponds to the earliest date) (default) 0 descending (the first data point corresponds to the latest date)
- = the ARMA model long-run mean (i.e. mu).
- = the standard deviation of the model's residuals/innovations.
- = the parameters of the AR(p) component model (starting with the lowest lag).
- = the parameters of the MA(q) component model (starting with the lowest lag).
ARMA_ERRORS() function is deprecated as of version 1.63: use ARMA_PARAM function instead.
- The underlying model is described here.
- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g. #N/A) at either end.
- D. S.G. Pollock; Handbook of Time Series Analysis, Signal Processing, and Dynamics; Academic Press; Har/Cdr edition(Nov 17, 1999), ISBN: 125609906
- James Douglas Hamilton; Time Series Analysis; Princeton University Press; 1st edition(Jan 11, 1994), ISBN: 691042896
- Tsay, Ruey S.; Analysis of Financial Time Series; John Wiley & SONS; 2nd edition(Aug 30, 2005), ISBN: 0-471-690740
- Box, Jenkins and Reisel; Time Series Analysis: Forecasting and Control; John Wiley & SONS.; 4th edition(Jun 30, 2008), ISBN: 470272848
- Walter Enders; Applied Econometric Time Series; Wiley; 4th edition(Nov 03, 2014), ISBN: 1118808568
Article is closed for comments.