Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.