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Modified lag augmented vector autoregressions

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Author Info

  • Eiji Kurozumi
  • Taku Yamamoto

Abstract

This paper proposes an inference procedure for a possibly integrated vector autoregression (VAR) model. We modify the lag augmented VAR (LA-VAR) estimator to exclude the quasiasymptotic bias, which is associated with the term Op(T-1), using the jackknife method. The new estimator has an asymptotic normal distribution and then the Wald statistic to test for the parameter restrictions has an asymptotic chi-square distribut,ion. We investigate the finite sample properties of this approach by comparing with the LA-VAR approach. We find t,hat our modified LA-VAR (MLA-VAR) approach excels the LA-VAR approach in view of an accuracy of the empirical size and the robustness to the tnisspecification of the lag length. The MLA-VAR approach may be used when the researchers place importance on an accuracy of the size, and also be used to complement other testing procedures that may suffer from serious size distortion.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 19 (2000)
Issue (Month): 2 ()
Pages: 207-231

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Handle: RePEc:taf:emetrv:v:19:y:2000:i:2:p:207-231

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Related research

Keywords: Vector autoregressions; Integration; Cointegration; Bias correction; Hypothesis testing;

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Citations

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Cited by:
  1. 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.
  2. Dufour, Jean-Marie & Jouini, Tarek, 2006. "Finite-sample simulation-based inference in VAR models with application to Granger causality testing," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 229-254.
  3. Wolde-Rufael, Yemane, 2006. "Electricity consumption and economic growth: a time series experience for 17 African countries," Energy Policy, Elsevier, vol. 34(10), pages 1106-1114, July.
  4. Jean-Marie Dufour & Denis Pelletier & Éric Renault, 2003. "Short Run and Long Run Causality in Time Series: Inference," CIRANO Working Papers 2003s-61, CIRANO.
  5. Wolde-Rufael, Yemane, 2005. "Energy demand and economic growth: The African experience," Journal of Policy Modeling, Elsevier, vol. 27(8), pages 891-903, November.
  6. Jain, Anshul & Ghosh, Sajal, 2013. "Dynamics of global oil prices, exchange rate and precious metal prices in India," Resources Policy, Elsevier, vol. 38(1), pages 88-93.
  7. Eiji Kurozumi & Kohei Aono, 2011. "Estimation and Inference in Predictive Regressions," Global COE Hi-Stat Discussion Paper Series gd11-192, Institute of Economic Research, Hitotsubashi University.
  8. Alam, Mohammad Jahangir & Begum, Ismat Ara & Buysse, Jeroen & Rahman, Sanzidur & Van Huylenbroeck, Guido, 2011. "Dynamic modeling of causal relationship between energy consumption, CO2 emissions and economic growth in India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 3243-3251, August.
  9. Amiri, Arshia & Ventelou, Bruno, 2012. "Granger causality between total expenditure on health and GDP in OECD: Evidence from the Toda–Yamamoto approach," Economics Letters, Elsevier, vol. 116(3), pages 541-544.
  10. Tsani, Stela Z., 2010. "Energy consumption and economic growth: A causality analysis for Greece," Energy Economics, Elsevier, vol. 32(3), pages 582-590, May.
  11. Wolde-Rufael, Yemane, 2004. "Disaggregated industrial energy consumption and GDP: the case of Shanghai, 1952-1999," Energy Economics, Elsevier, vol. 26(1), pages 69-75, January.
  12. Squalli, Jay, 2007. "Electricity consumption and economic growth: Bounds and causality analyses of OPEC members," Energy Economics, Elsevier, vol. 29(6), pages 1192-1205, November.

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