Estimating parameters and structural change in CGE models using a Bayesian cross-entropy estimation approach

Delfin Go, Hans Lofgren, Fabian Mendez Ramos and Sherman Robinson

Economic Modelling, 2016, vol. 52, issue PB, 790-811

Abstract: This paper uses a three-step Bayesian cross-entropy estimation approach in an environment of noisy and scarce data to estimate behavioral parameters for a computable general equilibrium (CGE) model and to measure how labor augmenting productivity and other structural parameters in the model may have shifted over time to contribute to the generation of historically observed changes in the economic arrangement. In this approach, the parameters in a CGE model are treated as fixed but unobserved, which we represent as prior mean values with prior error mass functions. Estimation of the parameters involves using an informationtheoretic Bayesian approach to exploit additional information in the form of new data from a series of Social Accounting Matrices (SAMs), which we assume were measured with error. The estimation procedure is "efficient" in the sense that it uses all available information and makes no assumptions about unavailable information. As illustration, we apply the methodology to estimate the parameters of a CGE model using alternative data sets for South Korea and for Sub-Saharan Africa.

Keywords: Computable general equilibrium (CGE) models; Bayesian estimation; cross-entropy estimation; Kullback-Leibler objective function; total factor productivity; labor augmenting productivity (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (10)

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Related works:
Working Paper: Estimating parameters and structural change in CGE models using a Bayesian cross-entropy estimation approach (2015)
Working Paper: Estimating Parameters and Structural Change in CGE Models Using a Bayesian Cross-Entropy Estimation Approach (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:52:y:2016:i:pb:p:790-811

DOI: 10.1016/j.econmod.2015.10.017

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