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Comparison of Model Reduction Methods for VAR Processes

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  • Ralf BRUEGGEMANN
  • Hans-Martin KROLZIG
  • Helmut LUETKEPOHL

Abstract

The objective of this study is to compare alternative computerized model-selection strategies in the context of the vector autoregressive (VAR) modeling framework. The focus is on a comparison of subset modeling strategies with the general-to-specific reduction approach automated by PcGets. Different measures of the possible gains of model selection are considered: (i) the chances of finding the correct model, that is, a model which contains all necessary right-hand side variables and is as parsimonious as possible, (ii) the accuracy of the implied impulse-responses and (iii) the forecast performance of the models obtained with different specification algorithms. In the Monte Carlo experiments, the procedures recover the DGP specification from a large VAR with anticipated size and power close to commencing from the DGP itself when evaluated at the empirical size. We find that subset strategies and PcGets are close competitors in many respects, with the forecast comparison indicating a clear advantage of the PcGets algorithm.
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Suggested Citation

  • Ralf BRUEGGEMANN & Hans-Martin KROLZIG & Helmut LUETKEPOHL, 2002. "Comparison of Model Reduction Methods for VAR Processes," Economics Working Papers ECO2002/19, European University Institute.
  • Handle: RePEc:eui:euiwps:eco2002/19
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    References listed on IDEAS

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    1. Krolzig, Hans-Martin & Hendry, David F., 2001. "Computer automation of general-to-specific model selection procedures," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 831-866, June.
    2. Hans-Martin Krolzig, 2001. "General--to--Specific Reductions of Vector Autoregressive Processes," Computing in Economics and Finance 2001 164, Society for Computational Economics.
    3. 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.
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    Citations

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    Cited by:

    1. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
    2. Jose Sanchez-Fung, 2008. "Measuring inflation targeting's impact on the macroeconomy," Applied Economics Letters, Taylor & Francis Journals, vol. 15(13), pages 1027-1035.
    3. Hans-Martin Krolzig, 2003. "General-to-Specific Model Selection Procedures for Structural Vector Autoregressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 769-801, December.
    4. Fengler, Matthias R. & Gisler, Katja I.M., 2015. "A variance spillover analysis without covariances: What do we miss?," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 174-195.
    5. Alejandro Gaytán & Jesús González-García, 2007. "Cambios estructurales en el mecanismo de transmisión de la política monetaria en México: un enfoque VAR no lineal," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 367-404, octubre-d.
    6. Sylvia Beatriz Guillermo Peón & Martín Alberto Rodríguez Brindis, 2014. "Analyzing the Exchange Rate Pass-through in Mexico: Evidence Post Inflation Targeting Implementation," Revista Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 32(74), pages 18-35, June.
    7. Barrera, Carlos, 2013. "El sistema de predicción desagregada: Una evaluación de las proyecciones de inflación 2006-2011," Working Papers 2013-009, Banco Central de Reserva del Perú.
    8. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
    9. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2009. "Studying co-movements in large multivariate data prior to multivariate modelling," Journal of Econometrics, Elsevier, vol. 148(1), pages 25-35, January.
    10. David F. Hendry & Hans-Martin Krolzig, 2003. "Sub-sample Model Selection Procedures in Gets Modelling," Economics Papers 2003-W17, Economics Group, Nuffield College, University of Oxford.
    11. Alejandro Gaytán González & Jesús R. González García, 2006. "Structural Changes in the Transmission Mechanism of Monetary Policy in Mexico: A Non-linear VAR Approach," Working Papers 2006-06, Banco de México.
    12. Cheong, Chongcheul & Lee, Hyunchul, 2014. "Forecasting with a parsimonious subset VAR model," Economics Letters, Elsevier, vol. 125(2), pages 167-170.
    13. Oscar Díaz Q. & Marco Laguna V., 2007. "Factores que explican la reducción de las tasas pasivas de interés en el sistema bancario boliviano," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 331-366, octubre-d.
    14. Dietmar Maringer & Peter Winker, 2004. "Optimal Lag Structure Selection in VEC-Models," Computing in Economics and Finance 2004 155, Society for Computational Economics.
    15. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.

    More about this item

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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