Difference between revisions of "Aggregation Matrix"
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Vectors and Matrices can be aggregated by multiplying with the aggregation matrix: | Vectors and Matrices can be aggregated by multiplying with the aggregation matrix: | ||
− | + | :<math>\mathbf{y}\_{s} = \mathbf{S} \mathbf{y}</math> | |
− | + | :<math>\mathbf{A}\_{s} = \mathbf{S} \mathbf{A} \mathbf{S}^T</math> | |
== See Also == | == See Also == | ||
* [[Aggregation Bias]] | * [[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]] | [[Category:EEIO]] |
Revision as of 15:14, 16 November 2023
Definition
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: