Abstract
The present paper investigates the effects of aggregation on the Perron vector, i.e. the eigenvector corresponding to the dominant eigenvalue. First, necessary and sufficient conditions are given for the aggregation to be consistent with respect to the Perron vector. That is, the Perron vector of the aggregated matrix is equal to the aggregated Perron vector of the original matrix, while the dominant eigenvalue remains the same. Secondly, upper and lower bounds are derived for the dominant eigenvalue and for the elements, of the Perron vector, which correspond to the sectors that are not aggregated. Thirdly, the results are applied to the traditional aggregation problem in input–output analysis, using the Perron vector as an artificial tool. It is indicated how the effects of aggregation on the input–output multipliers may be analyzed in a similar way.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
