The STATESPACE Procedure

Forecasting

Given estimates of ${\mb {F}}$, ${\mb {G}}$, and ${\bSigma _{\mb {ee}}}$, forecasts of ${\mb {x}_{t}}$ are computed from the conditional expectation of ${\mb {z}_{t}}$.

In forecasting, the parameters F, G, and ${\bSigma _{\mb {ee}}}$ are replaced with the estimates or by values specified in the RESTRICT statement. One-step-ahead forecasting is performed for the observation ${\mb {x}_{t}}$, where ${t{\leq }n-b}$. Here ${n}$ is the number of observations and b is the value of the BACK= option. For the observation ${\mb {x}_{t}}$, where ${t > n-b}$, m-step-ahead forecasting is performed for ${m = t-n + b}$. The forecasts are generated recursively with the initial condition ${\mb {z}_{0} = 0}$.

The m-step-ahead forecast of ${\mb {z}_{t+m}}$ is ${\mb {z}_{t+m|t}}$, where ${\mb {z}_{t+m|t}}$ denotes the conditional expectation of ${\mb {z}_{t+m}}$ given the information available at time t. The m-step-ahead forecast of ${\mb {x}_{t+m}}$ is ${\mb {x}_{t+m|t} = \mb {H} \mb {z}_{t+m|t}}$, where the matrix ${\mb {H} = [\mb {I}_{r} \mb {0} ]}$.

Let ${{\Psi }_{i} = \mb {F} ^{i} \mb {G}}$. Note that the last ${s-r}$ elements of ${\mb {z}_{t}}$ consist of the elements of ${\mb {x}_{u|t}}$ for ${u>t}$.

The state vector ${\mb {z}_{t+m}}$ can be represented as

\[  \mb {z}_{t+m} = \mb {F} ^{m} \mb {z}_{t} + \sum _{i=0}^{m-1}{\bPsi _{i} \mb {e}_{t+m-i}}  \]

Since ${\mb {e}_{t+i|t} = \mb {0}}$ for ${i>0}$, the m-step-ahead forecast ${\mb {z}_{t+m|t}}$ is

\[  \mb {z}_{t+m|t} = \mb {F} ^{m} \mb {z}_{t} = \mb {F} \mb {z}_{t+m-1|t}  \]

Therefore, the m-step-ahead forecast of ${\mb {x}_{t+m}}$ is

\[  \mb {x}_{t+m|t} = \mb {H} \mb {z}_{t+m|t}  \]

The m-step-ahead forecast error is

\[  \mb {z}_{t+m}-\mb {z}_{t+m|t} = \sum _{i=0}^{m-1}{\bPsi _{i} \mb {e}_{t+m-i}}  \]

The variance of the m-step-ahead forecast error is

\[  \mb {V}_{z,m} = \sum _{i=0}^{m-1}{\bPsi _{i} \bSigma _{\mb {ee}} {\Psi }_{i}’}  \]

Letting ${\mb {V}_{z,0} = \mb {0}}$, the variance of the m-step-ahead forecast error of ${\mb {z}_{t+m}}$, ${\mb {V}_{z,m}}$, can be computed recursively as follows:

\[  \mb {V}_{z,m} = \mb {V}_{z,m-1} + \bPsi _{m-1} \bSigma _{\mb {ee}} \bPsi ^{}_{m-1}  \]

The variance of the m-step-ahead forecast error of ${\mb {x}_{t+m}}$ is the ${r \times r}$ left upper submatrix of ${\mb {V}_{z,m}}$; that is,

\[  \mb {V}_{x,m} = \mb {H} \mb {V}_{z,m}\mb {H} ’  \]

Unless the NOCENTER option is specified, the sample mean vector is added to the forecast. When differencing is specified, the forecasts x $_{t+m|t}$ plus the sample mean vector are integrated back to produce forecasts for the original series.

Let ${\mb {y}_{t}}$ be the original series specified by the VAR statement, with some 0 values appended that correspond to the unobserved past observations. Let B be the backshift operator, and let ${\bDelta ({B})}$ be the ${s \times s}$ matrix polynomial in the backshift operator that corresponds to the differencing specified by the VAR statement. The off-diagonal elements of ${\bDelta _{i}}$ are 0. Note that ${\bDelta _{0} = \mb {I}_{s}}$, where ${\mb {I}_{s}}$ is the ${s \times s}$ identity matrix. Then ${\mb {z}_{t} = \bDelta ({B})\mb {y}_{t}}$.

This gives the relationship

\[  \mb {y}_{t} = \bDelta ^{-1}(B) \mb {z}_{t} = \sum _{i=0}^{{\infty }}{\bLambda _{i}\mb {z}_{t-i}}  \]

where ${\bDelta ^{-1}(B) =\sum _{i=0}^{{\infty }}{\bLambda _{i} B^{i}}}$ and ${\bLambda _{0} = \mb {I}_{s}}$.

The m-step-ahead forecast of ${\mb {y}_{t+m}}$ is

\[  \mb {y}_{t+m|t} = \sum _{i=0}^{m-1}{\bLambda _{i} \mb {z}_{t+m-i|t}} + \sum _{i=m}^{{\infty }}{\bLambda _{i} \mb {z}_{t+m-i}}  \]

The m-step-ahead forecast error of ${\mb {y}_{t+m}}$ is

$\displaystyle  \sum _{i=0}^{m-1}{\bLambda _{i} \left(\Strong{z}_{t+m-i} - \Strong{z}_{t+m-i|t}\right)} = \sum _{i=0}^{m-1} \left(\sum _{u=0}^{i}{\bLambda _{u} \bPsi _{i-u}}\right) \Strong{e}_{t+m-i} \nonumber  $

Letting ${\mb {V}_{y,0} = \mb {0}}$, the variance of the m-step-ahead forecast error of ${\mb {y}_{t+m}}$, ${\mb {V}_{y,m}}$, is

$\displaystyle  \Strong{V}_{y,m}  $
$\displaystyle = $
$\displaystyle  \sum _{i=0}^{m-1}{\left(\sum _{u=0}^{i}{\bLambda _{u} \bPsi _{i-u}}\right) \bSigma _{\mb {ee}} \left(\sum _{u=0}^{i}{\bLambda _{u} \bPsi _{i-u}}\right)’}  $
$\displaystyle  $
$\displaystyle = $
$\displaystyle  \Strong{V}_{y,m-1} + \left(\sum _{u=0}^{m-1}{\bLambda _{u} \bPsi _{m-1-u}}\right) \bSigma _{\mb {ee}} \left(\sum _{u=0}^{m-1}{\bLambda _{u} \bPsi _{m-1-u}}\right)’ \nonumber  $