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Lag selection in subset VAR models with an application to a US monetary system

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  • Brüggemann, Ralf
  • Lütkepohl, Helmut

Abstract

Alternative modeling strategies for specifying subset VAR models are considered. It is shown that under certain conditions a testing procedure based on t-ratios is equivalent to sequentially eliminating lags that lead to the largest improvement in a prespecified model selection criterion. A Monte Carlo study is used to illustrate the properties of different procedures. It is found that the differences between alternative strategies are small. In small samples, the strategies often fail to discover the true model. Nevertheless, using subset strategies results in models with improved forecast precision. To illustrate how these subset strategies can improve results from impulse response analysis, a VAR model is used to analyze the effects of monetary policy shocks for the U.S. economy. While the response patterns from full and subset VARs are qualitatively identical, confidence bands from the unrestricted model are considerably wider. We conclude that subset strategies can be useful modeling tools when forecasting or impulse response analysis is the main objective.

Suggested Citation

  • Brüggemann, Ralf & Lütkepohl, Helmut, 2000. "Lag selection in subset VAR models with an application to a US monetary system," SFB 373 Discussion Papers 2000,37, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:200037
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    References listed on IDEAS

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    1. Christiano, Lawrence J & Eichenbaum, Martin & Evans, Charles, 1996. "The Effects of Monetary Policy Shocks: Evidence from the Flow of Funds," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 16-34, February.
    2. 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.
    3. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    4. Alexander Benkwitz & Michael Neumann & Helmut Lutekpohl, 2000. "Problems related to confidence intervals for impulse responses of autoregressive processes," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 69-103.
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    Cited by:

    1. Hulsewig, Oliver & Mayer, Eric & Wollmershauser, Timo, 2006. "Bank loan supply and monetary policy transmission in Germany: An assessment based on matching impulse responses," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2893-2910, October.
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    3. Soo Khoon Goh, 2005. "New empirical evidence on the effects of capital controls on composition of capital flows in Malaysia," Applied Economics, Taylor & Francis Journals, vol. 37(13), pages 1491-1503.
    4. Avner Offer, 2000. "Economic Welfare Measurements and Human Well-Being, [revised version, March 2000]," Economics Series Working Papers 2000-W34, University of Oxford, Department of Economics.
    5. Melisso Boschi & Alessandro Girardi, 2005. "Euro Area inflation: long-run determinants and short-run dynamics," ISAE Working Papers 60, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    6. Boriss Siliverstovs & Olena Bilan, 2006. "Modeling Inflation Dynamics in Transition Economies: The Case of Ukraine," Eastern European Economics, Taylor & Francis Journals, vol. 43(6), pages 66-81, December.
    7. Philip Rothman & Dick van Dijk & Philip Hans Franses, 1999. "A Multivariate STAR Analysis of the Relationship Between Money and Output," Working Papers 9913, East Carolina University, Department of Economics.
    8. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, October.
    9. Krätzig, Markus, 2005. "A software framework for data based analysis," SFB 649 Discussion Papers 2005-044, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    10. Ubilava, David & holt, Matt, 2013. "El Ni~no southern oscillation and its effects on world vegetable oil prices: assessing asymmetries using smooth transition models," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(2), pages 1-25.
    11. Derek W. Bunn & Carlo Fezzi, 2007. "Interaction of European Carbon Trading and Energy Prices," Working Papers 2007.63, Fondazione Eni Enrico Mattei.
    12. Holtemöller, Oliver, 2002. "Structural vector autoregressive models and monetary policy analysis," SFB 373 Discussion Papers 2002,7, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    13. Martina Alexová, 2012. "What determines inflation?," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 5(4), pages 345-369.
    14. Hans-Martin Krolzig, 2001. "General--to--Specific Reductions of Vector Autoregressive Processes," Computing in Economics and Finance 2001 164, Society for Computational Economics.
    15. Victor Bystrov, 2014. "A factor-augmented model of markup on mortgage loans in Poland," Bank i Kredyt, Narodowy Bank Polski, vol. 45(6), pages 491-512.
    16. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.
    17. Brüggemann, Ralf, 2002. "On the small sample properties of weak exogeneity tests in cointegrated VAR models," SFB 373 Discussion Papers 2002,2, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    18. Benner Joachim & Meier Carsten-Patrick, 2004. "Prognosegüte alternativer Früh Indikatoren für die Konjunktur in Deutschland / Forecasting Performance of Alternative Indicators for the German Economy," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(6), pages 639-652, December.
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    More about this item

    Keywords

    Lag selection; model selection; monetary policy shocks; subset models; vector autoregressions;
    All these keywords.

    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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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