The Components of Output Growth: A Stochastic Frontier Analysis
Abstract
This paper uses Bayesian stochastic frontier methods to decompose output change into technical, efficiency and input changes. In the context of macroeconomic growth exercises, which typically involve small and noisy data sets, we argue that stochastic frontier methods are useful since they incorporate measurement error and assume a (flexible) parametric form for the production relationship. These properties enable us to calculate measures of uncertainty associated with the decomposition and minimize the risk of overfitting the noise in the data. Tools for Bayesian inference in such models are developed. An empirical investigation using data from 17 OECD countries for 10 years illustrates the practicality and usefulness of our approach. Copyright 1999 by Blackwell Publishing LtdDownload Info
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Bibliographic Info
Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics & Statistics.
Volume (Year): 61 (1999)
Issue (Month): 4 (November)
Pages: 455-87
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
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