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Joint Inference and Counterfactual experimentation for Impulse Response Functions by Local Projections


  • Oscar Jorda

    (Department of Economics, University of California Davis)


This paper provides three measures of the uncertainty associated to an impulse response path: (1) conditional confidence bands which isolate the uncertainty of individual response coefficients given the temporal path experienced up to that point; (2) response percentile bounds} which provide bounds on the universe of permissible paths at a given probability level; and (3) Wald tests of joint significance and joint cumulative significance. These results rely on general assumptions for the joint distribution of the system's impulse responses. Given this distribution, the paper then shows how to construct counterfactual experiments formally; provides a test on the likelihood of observing the counterfactual; and derives the distribution of the system's responses conditional on the counterfactual. The paper then derives the asymptotic joint distribution of structural impulse responses identified by either short- or long-run recursive assumptions and estimated by local projections (Jorda, 2005). An application to a two country system implements all of these new methods.

Suggested Citation

  • Oscar Jorda, 2007. "Joint Inference and Counterfactual experimentation for Impulse Response Functions by Local Projections," Working Papers 624, University of California, Davis, Department of Economics.
  • Handle: RePEc:cda:wpaper:06-24

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    References listed on IDEAS

    1. Leeper, Eric M. & Zha, Tao, 2003. "Modest policy interventions," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1673-1700, November.
    2. Cochrane, John H., 1998. "What do the VARs mean? Measuring the output effects of monetary policy," Journal of Monetary Economics, Elsevier, vol. 41(2), pages 277-300, April.
    3. Christopher A. Sims & Tao Zha, 1999. "Error Bands for Impulse Responses," Econometrica, Econometric Society, vol. 67(5), pages 1113-1156, September.
    4. Sims, Christopher A., 1992. "Interpreting the macroeconomic time series facts : The effects of monetary policy," European Economic Review, Elsevier, vol. 36(5), pages 975-1000, June.
    5. Kuersteiner, Guido M., 2001. "Optimal instrumental variables estimation for ARMA models," Journal of Econometrics, Elsevier, vol. 104(2), pages 359-405, September.
    6. Kuersteiner, Guido M., 2002. "Efficient Iv Estimation For Autoregressive Models With Conditional Heteroskedasticity," Econometric Theory, Cambridge University Press, vol. 18(03), pages 547-583, June.
    7. Bénédicte Vidaillet & V. D'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    8. Silvia Goncalves & Lutz Kilian, 2007. "Asymptotic and Bootstrap Inference for AR(∞) Processes with Conditional Heteroskedasticity," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 609-641.
    9. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
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    More about this item


    impulse response; local projection; conditional confidence bands; counterfactual;

    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
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods


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