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Comparison of Bootstrap Confidence Intervals for Impulse Responses of German Monetary Systems

Author

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  • Benkwitz, Alexander
  • Lütkepohl, Helmut
  • Wolters, Jürgen

Abstract

It is argued that standard impulse response analysis based on vector autoregressive models has a number of shortcomings. Although the impulse responses are estimated quantities, measures for sampling variability such as confidence intervals are often not provided. If confidence intervals are given they are often based on bootstrap methods with poor theoretical properties. These problems are illustrated using two German monetary systems. Proposals are made for improving current practice. Special emphasis is placed on systems with cointegrated variables.

Suggested Citation

  • Benkwitz, Alexander & Lütkepohl, Helmut & Wolters, Jürgen, 1999. "Comparison of Bootstrap Confidence Intervals for Impulse Responses of German Monetary Systems," CEPR Discussion Papers 2208, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:2208
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    References listed on IDEAS

    as
    1. JØrgen Wolters & Helmut LØtkepohl, 1998. "A money demand system for German M3," Empirical Economics, Springer, vol. 23(3), pages 371-386.
    2. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    3. Lutkepohl, Helmut, 1990. "Asymptotic Distributions of Impulse Response Functions and Forecast Error Variance Decompositions of Vector Autoregressive Models," The Review of Economics and Statistics, MIT Press, vol. 72(1), pages 116-125, February.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Bootstrap; Impulse Response; Monetary Policy; Money Demand System;

    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

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