IDEAS home Printed from https://ideas.repec.org/a/eee/dyncon/v41y2014icp154-172.html
   My bibliography  Save this article

Understanding the effect of technology shocks in SVARs with long-run restrictions

Author

Listed:
  • Chaudourne, Jeremy
  • Fève, Patrick
  • Guay, Alain

Abstract

This paper studies the statistical properties of impulse response functions in structural vector autoregressions (SVARs) with a highly persistent variable as hours worked and long-run identifying restrictions. The highly persistent variable is specified as a nearly stationary persistent process. Such a process appears to be particularly well suited to characterize the dynamics of hours worked because it implies a unit root in a finite sample but is asymptotically stationary and persistent. This is typically the case for per capita hours worked which are included in SVARs. Theoretical results derived from this specification allow us to explain most of the empirical findings from SVARs which include US hours worked.

Suggested Citation

  • Chaudourne, Jeremy & Fève, Patrick & Guay, Alain, 2014. "Understanding the effect of technology shocks in SVARs with long-run restrictions," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 154-172.
    • Handle: RePEc:eee:dyncon:v:41:y:2014:i:c:p:154-172
    DOI: 10.1016/j.jedc.2014.01.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165188914000232
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jedc.2014.01.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    2. Miles S. Kimball & John G. Fernald & Susanto Basu, 2006. "Are Technology Improvements Contractionary?," American Economic Review, American Economic Association, vol. 96(5), pages 1418-1448, December.
    3. Pierre Perron & Serena Ng, 1996. "Useful Modifications to some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties," Review of Economic Studies, Oxford University Press, vol. 63(3), pages 435-463.
    4. Hansen, Gary D., 1985. "Indivisible labor and the business cycle," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 309-327, November.
    5. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    6. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2007. "Assessing Structural VARs," NBER Chapters, in: NBER Macroeconomics Annual 2006, Volume 21, pages 1-106, National Bureau of Economic Research, Inc.
    7. Atsushi Inoue & Lutz Kilian, 2002. "Bootstrapping Smooth Functions of Slope Parameters and Innovation Variances in VAR (∞) Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(2), pages 309-332, May.
    8. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
    9. Francis, Neville & Ramey, Valerie A., 2005. "Is the technology-driven real business cycle hypothesis dead? Shocks and aggregate fluctuations revisited," Journal of Monetary Economics, Elsevier, vol. 52(8), pages 1379-1399, November.
    10. Matthew D. Shapiro & Mark W. Watson, 1988. "Sources of Business Cycle Fluctuations," NBER Chapters, in: NBER Macroeconomics Annual 1988, Volume 3, pages 111-156, National Bureau of Economic Research, Inc.
    11. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
    12. Yongsung Chang & Taeyoung Doh & Frank Schorfheide, 2007. "Non‐stationary Hours in a DSGE Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(6), pages 1357-1373, September.
    13. Ireland, Peter N., 2004. "A method for taking models to the data," Journal of Economic Dynamics and Control, Elsevier, vol. 28(6), pages 1205-1226, March.
    14. Gospodinov, Nikolay, 2010. "Inference in Nearly Nonstationary SVAR Models With Long-Run Identifying Restrictions," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 1-12.
    15. Lewis, Richard & Reinsel, Gregory C., 1985. "Prediction of multivariate time series by autoregressive model fitting," Journal of Multivariate Analysis, Elsevier, vol. 16(3), pages 393-411, June.
    16. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    17. Neville Francis & Valerie A. Ramey, 2009. "Measures of per Capita Hours and Their Implications for the Technology-Hours Debate," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(6), pages 1071-1097, September.
    18. Nikolay Gospodinov & Alex Maynard & Elena Pesavento, 2011. "Sensitivity of Impulse Responses to Small Low-Frequency Comovements: Reconciling the Evidence on the Effects of Technology Shocks," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(4), pages 455-467, October.
    19. Galí, Jordi & Rabanal, Pau, 2004. "Technology Shocks and Aggregate Fluctuations: How Well Does the RBC Model Fit Post-War US Data?," CEPR Discussion Papers 4522, C.E.P.R. Discussion Papers.
    20. Pantula, Sastry G, 1991. "Asymptotic Distributions of Unit-Root Tests When the Process Is Nearly Stationary," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(1), pages 63-71, January.
    21. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    22. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    23. John G. Fernald, 2012. "A quarterly, utilization-adjusted series on total factor productivity," Working Paper Series 2012-19, Federal Reserve Bank of San Francisco.
    24. Mark Gertler & Kenneth Rogoff (ed.), 2006. "NBER Macroeconomics Annual 2005," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262572346, December.
    25. Beaudry, Paul & Guay, Alain, 1996. "What do interest rates reveal about the functioning of real business cycle models?," Journal of Economic Dynamics and Control, Elsevier, vol. 20(9-10), pages 1661-1682.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ivashchenko, S., 2020. "Long-term growth sources for sectors of Russian economy," Journal of the New Economic Association, New Economic Association, vol. 48(4), pages 86-112.
    2. Benchimol, Jonathan & Ivashchenko, Sergey, 2021. "Switching volatility in a nonlinear open economy," Journal of International Money and Finance, Elsevier, vol. 110(C).
    3. Sergey Ivashchenko, 2022. "Dynamic Stochastic General Equilibrium Model with Multiple Trends and Structural Breaks," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 46-72, March.
    4. Chevillon, Guillaume & Mavroeidis, Sophocles & Zhan, Zhaoguo, 2016. "Robust inference in structural VARs with long-run restrictions," ESSEC Working Papers WP1702, ESSEC Research Center, ESSEC Business School.
    5. Bertinelli, Luisito & Cardi, Olivier & Restout, Romain, 2022. "Labor market effects of technology shocks biased toward the traded sector," Journal of International Economics, Elsevier, vol. 138(C).
    6. Thomet, Jacqueline & Wegmueller, Philipp, 2021. "Technology Shocks And Hours Worked: A Cross-Country Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 25(4), pages 1020-1052, June.
    7. Sevgi Coskun, 2020. "Technology Shocks and Non-stationary Hours in Emerging Countries and DSVAR," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 14(2), pages 129-163, May.
    8. Rujin, Svetlana, 2019. "What are the effects of technology shocks on international labor markets?," Ruhr Economic Papers 806, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Mathew Ekundayo Rotimi & Harold Ngalawa, 2017. "Oil Price Shocks and Economic Performance in Africa’s Oil Exporting Countries," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 13(5), pages 169-188, OCTOBER.
    10. Sergey M. Ivashchenko, 2019. "DSGE Models: Problem of Trends," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 2, pages 81-95, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    2. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
    3. Gubler, Matthias & Hertweck, Matthias S., 2013. "Commodity price shocks and the business cycle: Structural evidence for the U.S," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 324-352.
    4. Chari, V.V. & Kehoe, Patrick J. & McGrattan, Ellen R., 2008. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1337-1352, November.
    5. Holly, S. & Petrella, I., 2008. "Factor demand linkages and the business cycle: Interpreting aggregate fluctuations as sectoral fluctuations," Cambridge Working Papers in Economics 0827, Faculty of Economics, University of Cambridge.
    6. Patrick Fève & Alain Guay, 2009. "The Response of Hours to a Technology Shock: A Two‐Step Structural VAR Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(5), pages 987-1013, August.
    7. Giancarlo Corsetti & Luca Dedola & Sylvain Leduc, 2008. "Productivity, External Balance, and Exchange Rates: Evidence on the Transmission Mechanism among G7 Countries," NBER Chapters, in: NBER International Seminar on Macroeconomics 2006, pages 117-194, National Bureau of Economic Research, Inc.
    8. Danthine, Jean-Pierre & Kurmann, André, 2010. "The business cycle implications of reciprocity in labor relations," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 837-850, October.
    9. Ambler, Steve & Guay, Alain & Phaneuf, Louis, 2012. "Endogenous business cycle propagation and the persistence problem: The role of labor-market frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 47-62.
    10. Ghent, Andra C., 2009. "Comparing DSGE-VAR forecasting models: How big are the differences?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 864-882, April.
    11. Liu, Zheng & Phaneuf, Louis, 2007. "Technology shocks and labor market dynamics: Some evidence and theory," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2534-2553, November.
    12. Andrei Polbin & Sergey Drobyshevsky, 2014. "Developing a Dynamic Stochastic Model of General Equilibrium for the Russian Economy," Research Paper Series, Gaidar Institute for Economic Policy, issue 166P, pages 156-156.
    13. Justiniano, Alejandro & Primiceri, Giorgio E. & Tambalotti, Andrea, 2010. "Investment shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 132-145, March.
    14. Gospodinov, Nikolay & Maynard, Alex & Pesavento, Elena, 2011. "Sensitivity of Impulse Responses to Small Low-Frequency Comovements: Reconciling the Evidence on the Effects of Technology Shocks," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 455-467.
    15. Francesco Giuli & Massimiliano Tancioni, 2009. "Firm-Specific Capital, Productivity Shocks and Investment Dynamics," Working Papers in Public Economics 120, University of Rome La Sapienza, Department of Economics and Law.
    16. Fernald, John G., 2007. "Trend breaks, long-run restrictions, and contractionary technology improvements," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2467-2485, November.
    17. Shingo Watanabe, 2012. "The Role Of Technology And Nontechnology Shocks In Business Cycles," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(4), pages 1287-1321, November.
    18. Kascha, Christian & Mertens, Karel, 2009. "Business cycle analysis and VARMA models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 267-282, February.
    19. Mertens, Elmar, 2010. "Structural shocks and the comovements between output and interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1171-1186, June.
    20. Riggi, Marianna & Tancioni, Massimiliano, 2010. "Nominal vs real wage rigidities in New Keynesian models with hiring costs: A Bayesian evaluation," Journal of Economic Dynamics and Control, Elsevier, vol. 34(7), pages 1305-1324, July.

    More about this item

    Keywords

    SVARs; Long-run restrictions; Locally nonstationary process; Technology shocks; Hours worked;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:dyncon:v:41:y:2014:i:c:p:154-172. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jedc .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.