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Finite Sample Properties of Impulse Response Intervals in SVECMs with Long-Run Identifying Restrictions

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  • Ralf Brüggemann

Abstract

This paper investigates the finite sample properties of confidence intervals for structural vector error correction models (SVECMs) with long-run identifying restrictions on the impulse response functions. The simulation study compares methods that are frequently used in applied SVECM studies including an interval based on the asymptotic distribution of impulse responses, a standard percentile (Efron) bootstrap interval, Hall’s percentile and Hall’s studentized bootstrap interval. Data generating processes are based on empirical SVECM studies and evaluation criteria include the empirical coverage, the average length and the sign implied by the interval. Our Monte Carlo evidence suggests that applied researchers have little to choose between the asymptotic and the Hall bootstrap intervals in SVECMs. In contrast, the Efron bootstrap interval may be less suitable for applied work as it is less informative about the sign of the underlying impulse response function and the computationally demanding studentized Hall interval is often outperformed by the other methods. Differences between methods are illustrated empirically by using a data set from King, Plosser, Stock & Watson (1991).

Suggested Citation

  • Ralf Brüggemann, 2006. "Finite Sample Properties of Impulse Response Intervals in SVECMs with Long-Run Identifying Restrictions," SFB 649 Discussion Papers SFB649DP2006-021, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2006-021
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    File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2006-021.pdf
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    References listed on IDEAS

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    Cited by:

    1. Antonio Ribba, 2011. "On some neglected implications of the Fisher effect," Empirical Economics, Springer, vol. 40(2), pages 451-470, April.

    More about this item

    Keywords

    Structural vector error correction model; impulse response intervals; cointegration; long-run restrictions; bootstrap;

    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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