Computes the maximum likelihood estimate (MLE) of the model's parameters.
ARMA_CALIBRATE(X, Order, mean, sigma, phi, theta, maxIter)
- is the univariate time series data (a one-dimensional array of cells (e.g. rows or columns)).
- is 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)
- is the ARMA model long-run mean (i.e. mu).
- is the standard deviation of the model's residuals/innovations.
- are the parameters of the AR(p) component model (starting with the lowest lag).
- are the parameters of the MA(q) component model (starting with the lowest lag).
- is the maximum number of iterations used to calibrate the model. If missing, the default maximum of 100 is assumed.
ARMA_CALIBRATE() 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.
- The maximum likelihood estimation (MLE) is a statistical method for fitting a model to the data and provides estimates for the model's parameters.
- 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