Computes the log-likelihood function (LLF) of the estimated ARMA model.
ARMA_LLF(X, Order, mean, sigma, phi, ARRAY)
- = 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 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_LLF() function is deprecated as of version 1.63: use ARMA_GOF function instead.
- The underlying model is described here.
- The Log-Likelihood Function (LLF) 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 residuals/innovations standard deviation (i.e. $\sigma$) should be greater than zero.
- ARMA model has independent and normally distributed residuals with constant variance. The ARMA log-likelihood function becomes:
$$\ln L^* = -T\left(\ln 2\pi \hat \sigma^2+1\right)/2 $$
- $\hat \sigma$ is the standard deviation of the residuals.
- The maximum likelihood estimation (MLE) is a statistical method for fitting a model to the data and provides estimates for the model's parameters.
- The number of parameters in the input argument - phi - determines the order of the AR component.
- The number of parameters in the input argument - theta - determines the order of the MA component.
|=ARMA_LLF($B$2:$B$30,1,$D$3,$D$4,$D$5,$D$6)||Log-Likelihood Function (-2660.88)|
|=ARMA_CHECK($D$3,$D$4,$D$5,$D$6)||Is the ARMA model stable? (1)|
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