IDEAS home Printed from https://ideas.repec.org/p/nzb/nzbdps/2015-02.html
   My bibliography  Save this paper

Structural VARs, deterministic and stochastic trends: Does detrending matter?

Author

Listed:

Abstract

We highlight how detrending within Structural Vector Autoregressions (SVAR) is directly linked to the shock identification. Consequences of trend misspecification are investigated using a prototypical Real Business Cycle model as the Data Generating Process. Decomposing the different sources of biases in the estimated impulse response functions, we find the biases arising directly from trend misspecification are not trivial when compared to other widely studied misspecifications. Misspecifying the trend can also distort impulse response functions of even the correctly detrended variable within the SVAR system. A possible solution hinted by our analysis is that increasing the lag order when estimating the SVAR may mitigate some of the biases associated with trend misspecification.

Suggested Citation

  • Benjamin Wong & Varang Wiriyawit, 2015. "Structural VARs, deterministic and stochastic trends: Does detrending matter?," Reserve Bank of New Zealand Discussion Paper Series DP2015/02, Reserve Bank of New Zealand.
    • Handle: RePEc:nzb:nzbdps:2015/02
    as

    Download full text from publisher

    File URL: http://rbnz.govt.nz/-/media/ReserveBank/Files/Publications/Discussion%20papers/2015/dp15-02.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Fabio Canova & Filippo Ferroni, 2011. "Multiple filtering devices for the estimation of cyclical DSGE models," Quantitative Economics, Econometric Society, vol. 2(1), pages 73-98, March.
    3. Hansen, Gary D., 1985. "Indivisible labor and the business cycle," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 309-327, November.
    4. Efrem Castelnuovo, 2012. "Monetary Policy Neutrality: Sign Restrictions Go to Monte Carlo," "Marco Fanno" Working Papers 0151, Dipartimento di Scienze Economiche "Marco Fanno".
    5. Ravenna, Federico, 2007. "Vector autoregressions and reduced form representations of DSGE models," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2048-2064, October.
    6. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    7. D.S. Poskitt & Wenying Yao, 2012. "VAR Modeling and Business Cycle Analysis: A Taxonomy of Errors," Monash Econometrics and Business Statistics Working Papers 11/12, Monash University, Department of Econometrics and Business Statistics.
    8. Renee Fry & Adrian Pagan, 2005. "Some Issues In Using Vars For Macroeconometric Research," CAMA Working Papers 2005-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Pagan, A.R. & Pesaran, M. Hashem, 2008. "Econometric analysis of structural systems with permanent and transitory shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3376-3395, October.
    10. Martin Fukač & Adrian Pagan, 2010. "Limited information estimation and evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 55-70, January.
    11. 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.
    12. Arabinda Basistha, 2009. "Hours per capita and productivity: evidence from correlated unobserved components models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 187-206.
    13. Peersman, Gert & Van Robays, Ine, 2012. "Cross-country differences in the effects of oil shocks," Energy Economics, Elsevier, vol. 34(5), pages 1532-1547.
    14. 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.
    15. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
    16. Leu, Shawn Chen-Yu, 2011. "A New Keynesian SVAR model of the Australian economy," Economic Modelling, Elsevier, vol. 28(1), pages 157-168.
    17. 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.
    18. Varang Wiriyawit, 2014. "Trend Mis-specifications and Estimated Policy Implications in DSGE Models," ANU Working Papers in Economics and Econometrics 2014-615, Australian National University, College of Business and Economics, School of Economics.
    19. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    20. James C. Morley & Charles R. Nelson & Eric Zivot, 2003. "Why Are the Beveridge-Nelson and Unobserved-Components Decompositions of GDP So Different?," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 235-243, May.
    21. Paustian Matthias, 2007. "Assessing Sign Restrictions," The B.E. Journal of Macroeconomics, De Gruyter, vol. 7(1), pages 1-33, August.
    22. Kato, Ryuta Ray & Miyamoto, Hiroaki, 2013. "Fiscal stimulus and labor market dynamics in Japan," Journal of the Japanese and International Economies, Elsevier, vol. 30(C), pages 33-58.
    23. Cogley, Timothy & Nason, James M., 1995. "Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research," Journal of Economic Dynamics and Control, Elsevier, vol. 19(1-2), pages 253-278.
    24. Cover, James P. & Mallick, Sushanta K., 2012. "Identifying sources of macroeconomic and exchange rate fluctuations in the UK," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1627-1648.
    25. Finlay, Richard & Jääskelä, Jarkko P., 2014. "Credit supply shocks and the global financial crisis in three small open economies," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 270-276.
    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. Giovanni Caggiano & Efrem Castelnuovo & Gabriela Nodari, 2020. "Uncertainty and monetary policy in good and bad times: A Replication of the VAR investigation by Bloom (2009)," CAMA Working Papers 2020-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Giovanni Caggiano & Efrem Castelnuovo & Gabriela Nodari, 2022. "Uncertainty and monetary policy in good and bad times: A replication of the vector autoregressive investigation by Bloom (2009)," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 210-217, January.
    3. Elekdag, Selim & Han, Fei, 2015. "What drives credit growth in emerging Asia?," Journal of Asian Economics, Elsevier, vol. 38(C), pages 1-13.

    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. Laura Bisio & Andrea Faccini, 2010. "Does Cointegration Matter? An Analysis in a RBC Perspective," Working Papers in Public Economics 133, Department of Economics and Law, Sapienza University of Roma.
    2. Peter N. Ireland, 2009. "On the Welfare Cost of Inflation and the Recent Behavior of Money Demand," American Economic Review, American Economic Association, vol. 99(3), pages 1040-1052, June.
    3. Rujin, Svetlana, 2024. "Labor market institutions and technology-induced labor adjustment along the extensive and intensive margins," Journal of Macroeconomics, Elsevier, vol. 79(C).
    4. 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.
    5. Charles, Amélie & Darné, Olivier & Tripier, Fabien, 2015. "Are Unit Root Tests Useful In The Debate Over The (Non)Stationarity Of Hours Worked?," Macroeconomic Dynamics, Cambridge University Press, vol. 19(1), pages 167-188, January.
    6. Peter Ireland & Scott Schuh, 2008. "Productivity and U.S. Macroeconomic Performance: Interpreting the Past and Predicting the Future with a Two-Sector Real Business Cycle Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 11(3), pages 473-492, July.
    7. 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.
    8. 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.
    9. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
    10. 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.
    11. Francesco Giuli & Massimiliano Tancioni, 2010. "Contractionary Effects of Supply Shocks: Evidence and Theoretical Interpretation," Working Papers in Public Economics 131, Department of Economics and Law, Sapienza University of Roma.
    12. Dedola, Luca & Neri, Stefano, 2007. "What does a technology shock do? A VAR analysis with model-based sign restrictions," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 512-549, March.
    13. Yao, Wenying & Kam, Timothy & Vahid, Farshid, 2017. "On weak identification in structural VARMA models," Economics Letters, Elsevier, vol. 156(C), pages 1-6.
    14. Marcos Sanso-Navarro, 2012. "Broken trend stationarity of hours worked," Applied Economics, Taylor & Francis Journals, vol. 44(30), pages 3955-3964, October.
    15. Cantore, Cristiano & Ferroni, Filippo & León-Ledesma, Miguel A., 2017. "The dynamics of hours worked and technology," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 67-82.
    16. Ríos-Rull, José-Víctor & Santaeulàlia-Llopis, Raül, 2010. "Redistributive shocks and productivity shocks," Journal of Monetary Economics, Elsevier, vol. 57(8), pages 931-948, November.
    17. Lindé, Jesper, 2009. "The effects of permanent technology shocks on hours: Can the RBC-model fit the VAR evidence?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 597-613, March.
    18. Sean Holly & Ivan Petrella, 2008. "Factor demand linkages and the business cycle: interpreting aggregate fluctuations as sectoral fluctuations," CDMA Conference Paper Series 0809, Centre for Dynamic Macroeconomic Analysis.
    19. Hikaru Saijo, 2019. "Technology Shocks and Hours Revisited: Evidence from Household Data," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 31, pages 347-362, January.
    20. 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.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nzb:nzbdps:2015/02. 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: Reserve Bank of New Zealand Knowledge Centre (email available below). General contact details of provider: https://edirc.repec.org/data/rbngvnz.html .

    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.