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Empirical Estimation Of Non-Linear Input--Output Models: An Entropy Econometrics Approach

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  • Esteban Fernandez-Vazquez

Abstract

Despite theoretical advances, non-linear input--output models have been empirically applied only to a limited extent. This is mainly due to the fact that the number of parameters to be estimated is much higher than the number of available data points. Taking advantage of the recent proliferation of input--output databases and by applying an estimation strategy that relies on entropy econometrics, this paper suggests a way to estimate the parameters that characterize non-linear relationships between inputs and output. This non-linear modelling allows for considering time-specific input coefficients, instead of fixed ones. Several types of multipliers can be derived from this non-linear model, and the proposed generalized maximum entropy (GME) estimator allows estimating them from time series or cross-sectional datasets of input--output tables. The proposed GME technique is illustrated by means of an empirical application that estimates the parameters that characterize a non-linear input--output model for the Spanish economy over the period 1995--2011.

Suggested Citation

  • Esteban Fernandez-Vazquez, 2015. "Empirical Estimation Of Non-Linear Input--Output Models: An Entropy Econometrics Approach," Economic Systems Research, Taylor & Francis Journals, vol. 27(4), pages 508-524, December.
    • Handle: RePEc:taf:ecsysr:v:27:y:2015:i:4:p:508-524
    DOI: 10.1080/09535314.2015.1102715
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