Modelling stochastic and cyclical components of technical change: An application of the Kalman filter

摘要: Abstract In this paper, the Kalman filter is applied to task of estimating rate and direction change in technology production at a micro level. The framework familiar system factor-demand equations derived from cost function. state technology, latent variable, modeled as stochastic trend. addition, estimates total-factor productivity are corrected for measurement error that induces procyclibal bias. As result decoupling trend cyclical components using state-space estimation techniques, significant changes uncovered fail be detected when more traditional methods employed. application U.S. primary-metals industry appear consistent with stylized facts sector.