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Bootstrapping Smooth Functions of Slope Parameters and Innovation Variances in VAR (∞) Models

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  • Atsushi Inoue

    (North Carolina State University, U.S.A.)

  • Lutz Kilian

    (University of Michigan, U.S.A., and CEPR, U.K.)

Abstract

It is common to conduct bootstrap inference in vector autoregressive (VAR) models based on the assumption that the underlying data-generating process is of finite-lag order. This assumption is implausible in practice. We establish the asymptotic validity of the residual-based bootstrap method for smooth functions of VAR slope parameters and innovation variances under the alternative assumption that a sequence of finite-lag order VAR models is fitted to data generated by a VAR process of possibly infinite order. This class of statistics includes measures of predictability and orthogonalized impulse responses and variance decompositions. Our approach provides an alternative to the use of the asymptotic normal approximation and can be used even in the absence of closed-form solutions for the variance of the estimator. We illustrate the practical relevance of our findings for applied work, including the evaluation of macroeconomic models. Copyright Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association

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Bibliographic Info

Article provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.

Volume (Year): 43 (2002)
Issue (Month): 2 (May)
Pages: 309-332

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Handle: RePEc:ier:iecrev:v:43:y:2002:i:2:p:309-332

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Cited by:
  1. Francis X. Diebold & Lutz Kilian, 1997. "Measuring Predictability: Theory and Macroeconomic Applications," NBER Technical Working Papers 0213, National Bureau of Economic Research, Inc.
  2. Jean-Marie Dufour & Tarek Jouini, 2005. "Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing," CIRANO Working Papers 2005s-26, CIRANO.
  3. Jeremy Berkowitz & Ionel Birgean & Lutz Kilian, 1999. "On the finite-sample accuracy of nonparametric resampling algorithms for economic time series," Finance and Economics Discussion Series 1999-04, Board of Governors of the Federal Reserve System (U.S.).
  4. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
  5. Trenkler, Carsten & Weber, Enzo, 2012. "Identifying the Shocks behind Business Cycle Asynchrony in Euroland," University of Regensburg Working Papers in Business, Economics and Management Information Systems 466, University of Regensburg, Department of Economics.
  6. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
  7. Andrés Alonso & Daniel Peña & Juan Romo, 2006. "Introducing model uncertainty by moving blocks bootstrap," Statistical Papers, Springer, vol. 47(2), pages 167-179, March.
  8. Dufour, Jean-Marie & Taamouti, Abderrahim, 2010. "Short and long run causality measures: Theory and inference," Journal of Econometrics, Elsevier, vol. 154(1), pages 42-58, January.
  9. Helmut Luetkepohl, 2011. "Vector Autoregressive Models," Economics Working Papers ECO2011/30, European University Institute.
  10. Kilian, Lutz, 2011. "Structural Vector Autoregressions," CEPR Discussion Papers 8515, C.E.P.R. Discussion Papers.
  11. Pesavento, Elena & Rossi, Barbara, 2006. "Impulse Response Confidence Intervals for Persistent Data: What Have We Learned?," Working Papers 06-03, Duke University, Department of Economics.
  12. Lamb, John D. & Tee, Kai-Hong, 2012. "Resampling DEA estimates of investment fund performance," European Journal of Operational Research, Elsevier, vol. 223(3), pages 834-841.
  13. Luca Sala, 2004. "The Fiscal Theory of the Price Level: Identifying Restrictions and Empirical Evidence," Working Papers 257, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

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