# Disaggregating Input--Output Tables by the Multidimensional RAS Method: A Case Study of the Czech Republic

## TL;DR

This paper applies the multidimensional RAS method to disaggregate input-output tables for the Czech Republic, improving accuracy and consistency across multiple dimensions such as region and time.

## Contribution

It demonstrates the effectiveness of the multidimensional RAS method in maintaining consistency and accuracy in disaggregated input-output tables compared to traditional methods.

## Key findings

- Enhanced accuracy of input-output table estimation.
- Improved precision of the Leontief inverse and quarterly value added.
- Better regional modeling with the multidimensional approach.

## Abstract

The RAS method is an iterative procedure that bi-proportionally scales an input-output table to be consistent with given row and column sums. It can be used to disaggregate an annual national table to more detailed tables, such as regional, quarterly, and domestic/imported tables. However, the regular two-dimensional RAS method does not ensure the consistency of the disaggregated tables with the original table. For this problem, we use the multidimensional RAS method, which besides input and output totals, also ensures regional, quarterly, and domestic/imported totals. Our analysis of Czech industries shows that the multidimensional RAS method increases the accuracy of table estimation as well as the accuracy of the Leontief inverse, the quarterly value added, and (to some degree) the regional Isard's model. We also rigorously demonstrate its relation to the cross-entropy model.