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

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Author Info

  • 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. Diebold, Francis X & Kilian, Lutz, 2000. "Measuring Predictability: Theory And Macroeconomic Applications," CEPR Discussion Papers 2424, C.E.P.R. Discussion Papers.
  2. 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.
  3. DUFOUR, Jean-Marie & JOUINI, Tarek, 2005. "Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing," Cahiers de recherche 2005-12, Universite de Montreal, Departement de sciences economiques.
  4. Kilian, Lutz, 2011. "Structural Vector Autoregressions," CEPR Discussion Papers 8515, C.E.P.R. Discussion Papers.
  5. 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.
  6. Helmut Luetkepohl, 2011. "Vector Autoregressive Models," Economics Working Papers ECO2011/30, European University Institute.
  7. 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.
  8. 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.).
  9. Andrés Alonso & Daniel Peña & Juan Romo, 2006. "Introducing model uncertainty by moving blocks bootstrap," Statistical Papers, Springer, vol. 47(2), pages 167-179, March.
  10. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.

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