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Stochastic capital depreciation and the comovement of hours and productivity

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Listed:
  • Robert Dittmar
  • Michael J. Dueker
  • Andreas M. Fischer
Abstract
An unresolved question concerning stochastic depreciation shocks is whether they have to be unrealistically large to have any useful role in a dynamic general equilibrium model economy, as Ambler and Paquet (1994) first suggested. We first consider implied depreciation rates from sectoral data from the Bureau of Economic Analysis. These depreciation rates vary across time solely due to compositional changes within each sector. Hence, they tend to understate the range of fluctuation that would hold if the economic shelf life of capital varied endogenously as in Cooley, Greenwood and Yorukoglu (1997). We find, however, that if depreciation rates follow a Markov switching process, a low variance of the depreciation rate can generate the low correlation between hours worked and productivity in a simple model economy. White noise and autoregressive depreciation shocks, in contrast, require a counterfactually large variance in the depreciation rate to reduce the hours-productivity correlation. We also illustrate the level effects implied by nonlinear decision rules in simulations of dynamic general equilibrium models that include Markov switching parameters. Linear decision rules, in contrast, imply certainty equivalence and ignore the aversion that agents have to the skewed shock distributions that characterize Markov switching.

Suggested Citation

  • Robert Dittmar & Michael J. Dueker & Andreas M. Fischer, 2004. "Stochastic capital depreciation and the comovement of hours and productivity," Working Papers 2002-003, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:2002-003
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    References listed on IDEAS

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    Cited by:

    1. Fabio Canova & Filippo Ferroni & Christian Matthes, 2015. "Approximating Time Varying Structural Models With Time Invariant Structures," Working Paper 15-10, Federal Reserve Bank of Richmond.
    2. Furlanetto, Francesco & Seneca, Martin, 2014. "New Perspectives On Depreciation Shocks As A Source Of Business Cycle Fluctuations," Macroeconomic Dynamics, Cambridge University Press, vol. 18(6), pages 1209-1233, September.
    3. repec:ebl:ecbull:v:3:y:2007:i:50:p:1-8 is not listed on IDEAS
    4. Jasmina Hasanhodzic & Laurence J. Kotlikoff, 2013. "Generational Risk–Is It a Big Deal?: Simulating an 80-Period OLG Model with Aggregate Shocks," BYU Macroeconomics and Computational Laboratory Working Paper Series 2013-01, Brigham Young University, Department of Economics, BYU Macroeconomics and Computational Laboratory.
    5. Inwon Jang & Hyeon-seung Huh & Richard Wong, 2008. "Optimal capital investment under uncertainty: An extension," Economics Bulletin, AccessEcon, vol. 5(4), pages 1-7.
    6. Richard Harrison & George Kapetanios & Alasdair Scott & Jana Eklund, 2008. "Breaks in DSGE models," 2008 Meeting Papers 657, Society for Economic Dynamics.
    7. Anatoliy Belaygorod & Michael J. Dueker, 2007. "The price puzzle and indeterminacy in an estimated DSGE model," Working Papers 2006-025, Federal Reserve Bank of St. Louis.
    8. George Bitros, 2010. "The theorem of proportionality in contemporary capital theory: An assessment of its conceptual foundations," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 23(4), pages 367-401, December.
    9. Ludmila Fadejeva & Aleksejs Melihovs, 2010. "Measuring Total Factor Productivity and Variable Factor Utilization," Eastern European Economics, Taylor & Francis Journals, vol. 48(5), pages 63-101, September.
    10. Eric Kemp-Benedict & Jonathan Lamontagne & Timothy Laing & Crystal Drakes, 2019. "Climate Impacts on Capital Accumulation in the Small Island State of Barbados," Sustainability, MDPI, vol. 11(11), pages 1-23, June.
    11. Paul Pichler, 2007. "On the accuracy of low-order projection methods," Economics Bulletin, AccessEcon, vol. 3(50), pages 1-8.
    12. Bitros, George C., 2009. "The Theorem of Proportionality in Mainstream Capital Theory: An Assessment of its Conceptual Foundations," MPRA Paper 17436, University Library of Munich, Germany.
    13. Karamé, Frédéric, 2018. "A new particle filtering approach to estimate stochastic volatility models with Markov-switching," Econometrics and Statistics, Elsevier, vol. 8(C), pages 204-230.
    14. Tom Holden, 2012. "Medium-frequency cycles and the remarkable near trend-stationarity of output," School of Economics Discussion Papers 1412, School of Economics, University of Surrey.
    15. Deli, Yota D., 2016. "Endogenous capital depreciation and technology shocks," Journal of International Money and Finance, Elsevier, vol. 69(C), pages 318-338.
    16. repec:ebl:ecbull:v:5:y:2008:i:4:p:1-7 is not listed on IDEAS
    17. Pedro P. Alvarez-Lois, 2005. "Production Inflexibilities and the Cost Channel of Monetary Policy," Economic Inquiry, Western Economic Association International, vol. 43(1), pages 170-193, January.
    18. Poudel, Diwakar & Sandal, Leif K. & Kvamsdal, Sturla F. & Steinshamn, Stein I., 2011. "Fisheries Management under Irreversible Investment: Does Stochasticity Matter?," Discussion Papers 2011/20, Norwegian School of Economics, Department of Business and Management Science.
    19. Belaygorod, Anatoliy & Dueker, Michael, 2009. "Indeterminacy, change points and the price puzzle in an estimated DSGE model," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 624-648, March.

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    More about this item

    Keywords

    Econometric models; Productivity;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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