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Comparison of model reduction methods for VAR processes

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  • Brüggemann, Ralf
  • Krolzig, Hans-Martin
  • Lütkepohl, Helmut

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.

Suggested Citation

  • Brüggemann, Ralf & Krolzig, Hans-Martin & Lütkepohl, Helmut, 2002. "Comparison of model reduction methods for VAR processes," SFB 373 Discussion Papers 2002,80, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:200280
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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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    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. Jian Zhang, 2018. "Low-dimensional approximation searching strategy for transfer entropy from non-uniform embedding," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-24, March.
    4. 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.
    5. 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.
    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, CEMLA, vol. 0(4), pages 367-404, octubre-d.
    7. Sylvia Beatriz Guillermo Peón & Martín Alberto Rodríguez Brindis, 2014. "Analyzing the Exchange Rate Pass-through in Mexico: Evidence Post Inflation Targeting Implementation," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 32(74), pages 18-35, June.
    8. 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.
    9. 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.
    10. Peter Winker & Dietmar Maringer, 2004. "Optimal Lag Structure Selection in VEC-Models," Contributions to Economic Analysis, in: New Directions in Macromodelling, pages 213-234, Emerald Group Publishing Limited.
    11. 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.
    12. 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.
    13. Gaytán González Alejandro & González García Jesús R., 2006. "Structural Changes in the Transmission Mechanism of Monetary Policy in Mexico: A Non-linear VAR Approach," Working Papers 2006-06, Banco de México.
    14. Cheong, Chongcheul & Lee, Hyunchul, 2014. "Forecasting with a parsimonious subset VAR model," Economics Letters, Elsevier, vol. 125(2), pages 167-170.
    15. Oscar Díaz Q. & Marco Laguna V., 2007. "Factores que explican la reducción de las tasas pasivas de interés en el sistema bancario boliviano," Monetaria, CEMLA, vol. 0(4), pages 331-366, octubre-d.
    16. Eklund, Jana & Kapetanios, George, 2008. "A review of forecasting techniques for large datasets," National Institute Economic Review, National Institute of Economic and Social Research, vol. 203, pages 109-115, January.
    17. Adugna Olani, 2016. "Dynamic Capital Inflow Transmission Of Monetary Policy To Emerging Markets," Working Paper 1358, Economics Department, Queen's University.
    18. Eklund, Jana & Kapetanios, George, 2008. "A review of forecasting techniques for large datasets," National Institute Economic Review, Cambridge University Press, vol. 203, pages 109-115, January.
    19. Christian Hertrich, 2013. "Asset Allocation Considerations for Pension Insurance Funds," Springer Books, Springer, edition 127, number 978-3-658-02167-2, April.

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    More about this item

    Keywords

    vector autoregression; model selection; subset model; lag order determination;
    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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