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Inference for Impulse Responses

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  • Oscar Jorda

    (Department of Economics, University of California Davis)

Abstract

Poor identification of individual impulse response coefficients does not necessarily mean that animpulse response is imprecisely estimated. This paper introduces a three-pronged approach on howto communicate uncertainty of impulse response estimates: (1) withWald tests of joint significance;(2) with conditional t-tests of individual marginal coefficient significance; and (3) with fan chartsbased on the percentiles of the joint Wald statistics. The paper also shows how to anchor theimpulse response analysis with a priori economic restrictions that can be formally tested and usedto tighten structural identification. These methods are universal and do not depend on how theimpulse responses are estimated. An empirical application illustrates the techniques in practice.

Suggested Citation

  • Oscar Jorda, 2007. "Inference for Impulse Responses," Working Papers 201, University of California, Davis, Department of Economics.
    • Handle: RePEc:cda:wpaper:201
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    References listed on IDEAS

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    1. Kuersteiner, Guido M., 2002. "Efficient Iv Estimation For Autoregressive Models With Conditional Heteroskedasticity," Econometric Theory, Cambridge University Press, vol. 18(3), pages 547-583, June.
    2. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    3. 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.
    4. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    5. Lütkepohl, Helmut & Poskitt, D.S., 1991. "Estimating Orthogonal Impulse Responses via Vector Autoregressive Models," Econometric Theory, Cambridge University Press, vol. 7(4), pages 487-496, December.
    6. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
    7. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    8. Christopher A. Sims & Tao Zha, 1999. "Error Bands for Impulse Responses," Econometrica, Econometric Society, vol. 67(5), pages 1113-1156, September.
    9. Kuersteiner, Guido M., 2001. "Optimal instrumental variables estimation for ARMA models," Journal of Econometrics, Elsevier, vol. 104(2), pages 359-405, September.
    10. 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.
    11. Lutz Kilian, 1999. "Finite-Sample Properties of Percentile and Percentile-t Bootstrap Confidence Intervals for Impulse Responses," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 652-660, November.
    12. Oscar Jorda & Sharon Kozicki, 2006. "Projection Minimum Distance: An Estimator for Dynamic Macroeconomic Models," Working Papers 623, University of California, Davis, Department of Economics.
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    Cited by:

    1. Uluc Aysun & Ryan Brady & Adam Honig, 2009. "Financial Frictions and Monetary Transmission Strength: A Cross-Country Analysis," Working papers 2009-24, University of Connecticut, Department of Economics, revised Jun 2010.
    2. Ryan R. Brady, 2011. "Measuring the diffusion of housing prices across space and over time," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 213-231, March.

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    More about this item

    Keywords

    impulse response function; local projections; vector autoregressions;
    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
    • 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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