Difference between revisions of "Aggregation Matrix"

Revision as of 15:14, 16 November 2023

Aggregation Matrix in the context of Input-Output Analysis is a Boolean Matrix (composed of zeros and ones) 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:

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

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

@@ Line 6: / Line 6: @@
 Vectors and Matrices can be aggregated by multiplying with the aggregation matrix:
-$$\mathbf{y}\_{s} = \mathbf{S} \mathbf{y}$$
+:<math>\mathbf{y}\_{s} = \mathbf{S} \mathbf{y}</math>
-$$\mathbf{A}\_{s} = \mathbf{S} \mathbf{A} \mathbf{S}^T $$
+:<math>\mathbf{A}\_{s} = \mathbf{S} \mathbf{A} \mathbf{S}^T</math>
 == See Also ==
 * [[Aggregation Bias]]
+== Further Resources ==
+* [https://www.openriskacademy.com/course/view.php?id=70 Crash Course on Input-Output Model Mathematics]
+* [https://www.openriskacademy.com/course/view.php?id=64 Introduction to Input-Output Models using Python]
 [[Category:EEIO]]