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Inference for Impulse Responses

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Author Info
Jorda, Oscar (U of California, Davis)

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Abstract

Poor identification of individual impulse response coefficients does not necessarily mean that an impulse response is imprecisely estimated. This paper introduces a three-pronged approach on how to 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 charts based on the percentiles of the joint Wald statistics. The paper also shows how to anchor the impulse response analysis with a priori economic restrictions that can be formally tested and used to tighten structural identification. These methods are universal and do not depend on how the impulse responses are estimated. An empirical application illustrates the techniques in practice.

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Paper provided by University of California at Davis, Department of Economics in its series Working Papers with number 07-7.

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Date of creation: Jul 2007
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Handle: RePEc:ecl:ucdeco:07-7

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C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

References listed on IDEAS
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  1. Horowitz, Joel L., 2001. "The Bootstrap," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 52, pages 3159-3228 Elsevier. [Downloadable!] (restricted)
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  3. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March. [Downloadable!]
  4. Christopher A. Sims & Tao Zha, 1999. "Error Bands for Impulse Responses," Econometrica, Econometric Society, vol. 67(5), pages 1113-1156, September.
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  5. Hoover, Kevin & Demiralp, Selva, 2003. "Searching for the Causal Structure of a Vector Autoregression," Working Papers 03-3, University of California at Davis, Department of Economics. [Downloadable!]
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  6. Jorda, Oscar & Kozicki, Sharon, 2006. "Projection Minimum Distance: An Estimator for Dynamic Macroeconomic Models," Working Papers 06-23, University of California at Davis, Department of Economics. [Downloadable!]
  7. Eric M. Leeper & Tao Zha, 2003. "Modest policy interventions," Working Paper 2003-24, Federal Reserve Bank of Atlanta. [Downloadable!]
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  8. 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 655-73, September. [Downloadable!] (restricted)
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  9. 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. [Downloadable!] (restricted)
  10. 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. [Downloadable!] (restricted)
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  11. Kuersteiner, Guido M., 2001. "Optimal instrumental variables estimation for ARMA models," Journal of Econometrics, Elsevier, vol. 104(2), pages 359-405, September. [Downloadable!] (restricted)
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  1. Hall, Alastair & Inoue, Atsushi & Nason M, James & Rossi, Barbara, 2007. "Information Criteria for Impulse Response Function Matching Estimation of DSGE Models," Working Papers 07-04, Duke University, Department of Economics. [Downloadable!]
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