 Technical change in U.S. industriesA K M Nurul Hossain and Apostolos Serletis Economic Modelling, 2020, vol. 91, issue C, 579-600 Abstract: We use the normalized quadratic cost function, introduced by Diewert and Wales (1987), to measure and analyze the rate and biases of technical change at the sectoral level in eleven major U.S. industries — manufacturing, construction, mining, agriculture, finance, health, wholesale, transportation, education, hospitality, and utilities — using annual KLEM (capital, labor, energy, and intermediate materials) data from the World KLEMS database, over the period from 1947 to 2010. We extend the work in Feng and Serletis (2008), by taking a new approach to econometric modeling, merging the econometric approach to productivity measurement with recent state-of-the-art advances in financial econometrics. In particular, we relax the homoskedasticity assumption and instead assume that the covariance matrix of the errors of the flexible interrelated factor demand systems is time-varying. We also pay explicit attention to theoretical regularity, treating the curvature property as a maintained hypothesis, thus achieving superior modeling in the context of a parametric nonlinear factor demand system that captures certain important features of the data. Keywords: Total factor productivity; Biases of technical change; Heteroskedasticity; Substitution (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:91:y:2020:i:c:p:579-600 DOI: 10.1016/j.econmod.2019.12.001 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
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