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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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Bibliographic Info

Paper provided by European University Institute in its series Economics Working Papers with number ECO2002/19.

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Date of creation: 2002
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Handle: RePEc:eui:euiwps:eco2002/19

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  1. 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.
  2. Hans-Martin Krolzig & David Hendry, 1999. "Computer Automation of General-to-Specific Model Selection Procedures," Computing in Economics and Finance 1999 314, Society for Computational Economics.
  3. Hans-Martin Krolzig, 2001. "General--to--Specific Reductions of Vector Autoregressive Processes," Computing in Economics and Finance 2001 164, Society for Computational Economics.
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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. María Fernanda Hernández & Juan José Valero & María Bernardette Díaz, 2007. "Perfil de riesgos del sistema bancario venezolano: aplicación de la metodología de stress testing," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 405-452, octubre-d.
  6. 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.
  7. Gianluca Cubadda & Alain Hecq & Franz C. Palm, 2008. "Studying Co-Movements in Large Multivariate Data Prior to Multivariate Modelling," CEIS Research Paper 125, Tor Vergata University, CEIS, revised 14 Jul 2008.
  8. 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.
  9. 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.
  10. Dietmar Maringer & Peter Winker, 2004. "Optimal Lag Structure Selection in VEC-Models," Computing in Economics and Finance 2004 155, Society for Computational Economics.
  11. 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.

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