Aggregation Matrix

Definition

Aggregation Matrix in the context of Input-Output Analysis is a Boolean Matrix that aims to produce a coarse-grained version of a more granular Input-Output Model.

Aggregation can be for example along sectoral or regional dimensions.

Vectors and Matrices can be aggregated by multiplying with the aggregation matrix. Mathematically a aggregation matrix T is a $K \times N$ matrix, where each value $s_{mn}$ is either zero or one.

$S=\left(\begin{matrix} s_{00} & s_{01} & \dots &s_{0n} & \dots & s_{0N} \\ s_{10} & s_{11} & \dots &s_{1n} & \dots & s_{1N} \\ \vdots & \vdots & \ddots &\vdots & \ddots & \vdots \\ s_{m0} & s_{m1} & \dots &s_{mn} & \dots & s_{mN} \\ \vdots & \vdots & \ddots & \vdots& \ddots & \vdots \\ s_{K0} & s_{K1} & \dots & s_{Kn} & \dots & s_{KN}\\ \end{matrix}\right).$

Vector Quantity Aggregation

A vector of dimension N is aggregated through its pre-multiplication with the aggregation matrix.

$\mathbf{y}_{s} = \mathbf{S} \mathbf{y}$

Matrix Quantity Aggregation

A matrix of dimension N is aggregated through its pre-multiplication with the aggregation matrix and the post-multiplication with the aggregation matrix transpose.

$\mathbf{A}_{s} = \mathbf{S} \mathbf{A} \mathbf{S}^T$

See Also

• Aggregation Bias
• Summation Vector

Further Resources

• Crash Course on Input-Output Model Mathematics
• Introduction to Input-Output Models using Python