by
Serletis, Apostolos and
Hossain, A K M NurulWe 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.
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