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ARMAX_FORE
Calculates the out-of-sample conditional forecast (i.e. mean, error and confidence interval)
Syntax
ARMAX_FORE(Y, X, Order, Beta, mean, sigma, phi, theta, T, Type, alpha)
Y
is the response or the dependent variable time series data array (one dimensional array of cells (e.g. rows or columns)).
X
is the independent variables (exogenous factors) time series data matrix, such that each column represents one variable.
Order
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) |
Beta
are the coefficients array of the exogenous factors.
mean
is the ARMA long-run mean (i.e. mu).
sigma
is the standard deviation of the model's residuals.
phi
are the parameters of the AR(p) component model (starting with the lowest lagi).
theta
are the parameters of the MA(q) component model (starting with the lowest lag).
T
is the forecast time/horizon (expressed in terms of steps beyond end of the time series).
Type
is an integer switch to select the forecast output type: (1=mean (default), 2=Std. Error, 3=Term Struct, 4=LL, 5=UL)
| Order | Description |
|---|---|
| 1 | Mean forecast value (default) |
| 2 | Forecast standard error (aka local volatility) |
| 3 | Volatility term structure |
| 4 | Lower limit of the forecast confidence interval. |
| 5 | Upper limit of the forecast confidence interval. |
alpha
is the statistical significance level. If missing, a default of 5% is assumed.
Remarks
- The underlying model is described here.
- The Log-Likelihood Function (LLFi) 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 long-run mean can take any value or be omitted, in which case a zero value is assumed.
- The residuals/innovations standard deviation (sigma) must be greater than zero.
- For the input argument (beta):
- The input argument is optional and can be omitted, in which case no regression component is included (i.e. plain ARMA).
- The order of the parameters defines how the exogenous factor input arguments are passed.
- One or more parameters may have missing values or error codes (i.e. #NUM!, #VALUE!, etc.).
- For the input argument (phi):
- The input argument is optional and can be omitted, in which case no AR component is included.
- The order of the parameters starts with the lowest lag.
- One or more parameters may have missing values or error codes (i.e. #NUM!, #VALUE!, etc.).
- The order of the AR component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).
- For the input argument (theta):
- The input argument is optional and can be omitted, in which case no MA component is included.
- The order of the parameters starts with the lowest lag.
- One or more values in the input argument can be missing or an error code (i.e. #NUM!, #VALUE!, etc.).
- The order of the MA component model is solely determined by the order of the last value in the array with a numeric value (vs. missing or error).
- The function was added in version 1.63 SHAMROCK.
Files Examples
References
- Hamilton, J .D.; Time Series Analysis
, Princeton University Press (1994), ISBN 0-691-04289-6 - Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740
