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How to Advance Theory with Structural VARs: Use the Sims-Cogley-Nason Approach

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  • Patrick J. Kehoe

Abstract

The common approach to evaluating a model in the structural VAR literature is to compare the impulse responses from structural VARs run on the data to the theoretical impulse responses from the model. The Sims-Cogley-Nason approach instead compares the structural VARs run on the data to identical structural VARs run on data from the model of the same length as the actual data. Chari, Kehoe, and McGrattan (2006) argue that the inappropriate comparison made by the common approach is the root of the problems in the SVAR literature. In practice, the problems can be solved simply. Switching from the common approach to the Sims-Cogley-Nason approach basically involves changing a few lines of computer code and a few lines of text. This switch will vastly increase the value of the structural VAR literature for economic theory.

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  • Patrick J. Kehoe, 2006. "How to Advance Theory with Structural VARs: Use the Sims-Cogley-Nason Approach," NBER Working Papers 12575, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:12575
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    2. Karel Mertens & Morten Overgaard Ravn, 2011. "Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy Shocks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 27-54, January.
    3. Hall, Alastair R. & Inoue, Atsushi & Nason, James M. & Rossi, Barbara, 2012. "Information criteria for impulse response function matching estimation of DSGE models," Journal of Econometrics, Elsevier, vol. 170(2), pages 499-518.
    4. Das, Pranab Kumar & Kar, Saibal, 2016. "Public Expenditure, Demography and Growth: Theory and Evidence from India," IZA Discussion Papers 9721, Institute of Labor Economics (IZA).
    5. Kuester, Keith & Müller, Gernot J. & Stölting, Sarah, 2009. "Is the New Keynesian Phillips curve flat?," Economics Letters, Elsevier, vol. 103(1), pages 39-41, April.
    6. Martial Dupaigne & Patrick Feve & Julien Matheron, 2007. "Technology Shocks, Non-stationary Hours and DSVAR," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(2), pages 238-255, April.
    7. Morten O. Ravn & Karel Mertens, 2008. "The Aggregate Effects of Anticipated and Unanticipated U.S. Tax Policy Shocks: Theory and Empirical Evidence," 2008 Meeting Papers 575, Society for Economic Dynamics.
    8. 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.
    9. Toshihiro Okada, 2017. "Time to Innovate and Aggregate Fluctuations: a New Keynesian Model with Endogenous Technology," Discussion Paper Series 154, School of Economics, Kwansei Gakuin University, revised Dec 2018.
    10. Paul Levine & Joseph Pearlman & Stephen Wright & Bo Yang, 2019. "Information, VARs and DSGE Models," School of Economics Discussion Papers 1619, School of Economics, University of Surrey.
    11. Elmar Mertens, 2008. "Are Spectral Estimators Useful for Implementing Long-Run Restrictions in SVARs?," Working Papers 08.01, Swiss National Bank, Study Center Gerzensee.
    12. Karel Mertens & Morten Overgaard Ravn, 2011. "Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy Shocks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 27-54, January.
    13. Stiassny, Alfred & Uhl, Christina, 2014. "Does Elderly Employment have an Impact on Youth Employment? A General Equilibrium Approach," Department of Economics Working Paper Series 178, WU Vienna University of Economics and Business.
    14. Alfred Stiassny & Christina Uhl, 2014. "Does Elderly Employment have an Impact on Youth Employment? A General Equilibrium Approach," Department of Economics Working Papers wuwp178, Vienna University of Economics and Business, Department of Economics.
    15. 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.
    16. 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.
    17. Paul Levine & Joseph Pearlman & Stephen Wright & Bo Yang, 2023. "Imperfect Information and Hidden Dynamics," School of Economics Discussion Papers 1223, School of Economics, University of Surrey.
    18. Pranab Kumar Das & Saibal Kar, 2015. "A Study of Demographic and Financial Changes in India," Palgrave Macmillan Books, in: José María Fanelli (ed.), Asymmetric Demography and the Global Economy, chapter 0, pages 213-241, Palgrave Macmillan.

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    More about this item

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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