Bayesian input–output table update using a benchmark LASSO prior

Tsionas, Mike G. (2020) Bayesian input–output table update using a benchmark LASSO prior. Economic Systems Research, 32 (3). pp. 413-427.

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Abstract

We propose updating a multiplier matrix subject to final demand and total output constraints, where the prior multiplier matrix is weighted against a LASSO prior. We update elements of the Leontief inverse, from which we can derive posterior densities of the entries in input-output tables. As the parameter estimates required by far exceed the available observations, many zero entries deliver a sparse tabulation. We address that problem with a new statistical model wherein we adopt a LASSO prior. We develop novel numerical techniques and perform a detailed Monte Carlo study to examine the performance of the new approach under different configurations of the input-output table. The new techniques are applied to a 196 ×196 U.S. input-output table for 2012.

Item Type:

Journal Article

Journal or Publication Title:

Economic Systems Research

Additional Information:

This is an Accepted Manuscript of an article published by Taylor & Francis in Economic Systems Research on 09/01/2020, available online: https://www.tandfonline.com/doi/abs/10.1080/09535314.2019.1707170

Subjects:

?? input–output tablesbayesian inferencemarkov chain monte carlolasso priors ??

Departments:

Lancaster University Management School > Economics

ID Code:

149578

Deposited By:

ep_importer_pure

Deposited On:

03 Dec 2020 11:10

Refereed?:

Yes