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Comparison Of Bootstrap Confidence Intervals For Impulse Responses Of German Monetary Systems

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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 sometimes are not provided. If confidence intervals are given, they often are based on bootstrap methods with dubious 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, 2001. "Comparison Of Bootstrap Confidence Intervals For Impulse Responses Of German Monetary Systems," Macroeconomic Dynamics, Cambridge University Press, vol. 5(1), pages 81-100, February.
    • Handle: RePEc:cup:macdyn:v:5:y:2001:i:01:p:81-100_01
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    References listed on IDEAS

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    1. 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.
    2. JØrgen Wolters & Helmut LØtkepohl, 1998. "A money demand system for German M3," Empirical Economics, Springer, vol. 23(3), pages 371-386.
    3. 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.
    4. Brüggemann, I. & Wolters, J., 1996. "Money and Prices in Germany. Empirical Results for 1962 to 1994," SFB 373 Discussion Papers 1996,34, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    5. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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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

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