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Total Factor Productivity: An Unobserved Components Approach

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  • Raul Crespo

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    Abstract

    This work examines the presence of unobserved components in the time series of Total Factor Productivity, which is an idea central to modern Macroeconomics. The main approaches in both the study of economic growth and the study of business cycles rely on certain properties of the different components of the time series of Total Factor Productivity. In the study of economic growth, the Neoclassical growth model explains growth in terms of technical progress as measured by the secular component of Total Factor Productivity. While in the study of business cycles, the Real Business Cycle approach explains short-run fluctuations in the economy as determined by temporary movements in the production function, which are reflected by the cyclical component of the time series of the same variable. The econometric methodology employed in the estimation of these different components is the structural time series approach developed by Harvey (1989), Harvey and Shephard (1993), and others. An application to the time series of Total Factor Productivity for the 1948-2002 U.S. private non-farm business sector is presented. The pattern described by technical progress in this economy is characterised by important growth for the period immediately after War World II, which reaches its peak at the beginning of the 1960s to decline until the earliest 1980s where it shows a modest rebound. On the other hand, the cyclical component of the series seems to be better described by two cycles with periodicity of six and twelve years, respectively.

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    File URL: http://www.efm.bris.ac.uk/economics/working_papers/pdffiles/dp05579.pdf
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    Bibliographic Info

    Paper provided by Department of Economics, University of Bristol, UK in its series Bristol Economics Discussion Papers with number 05/579.

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    Length: 31 pages
    Date of creation: Dec 2005
    Date of revision:
    Handle: RePEc:bri:uobdis:05/579

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    Keywords: Productivity; Business Cycles; Structural Time Series Models; Unobserved Components.;

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