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General--to--Specific Reductions of Vector Autoregressive Processes

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  • Hans-Martin Krolzig

Abstract

Unrestricted reduced form vector autoregressive (VAR) models have become a dominant research strategy in empirical macroeconomics since Sims (1980) critique of traditional macroeconometric modeling. They are however subjected to the curse of dimensionality. In this paper we propose general-to-specific reductions of VAR models and consider computer-automated model selection algorithms embodied in PcGets (see Krolzig and Hendry, 2000) for doing so. Starting from the unrestricted VAR, standard testing procedures eliminate statistically-insignificant variables, with diagnostic tests checking the validity of reductions, ensuring a congruent final selection. Since jointly selecting and diagnostic testing eludes theoretical analysis, we evaluate the proposed strategy by simulation. The Monte Carlo experiments show that PcGets recovers the DGP specification from a large unrestricted VAR model with size and power close to commencing from the DGP itself. The application of the proposed reduction strategy to a US monetary system demonstrates the feasibility of PcGets for the analysis of large macroeconomic data sets.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 164.

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Date of creation: 01 Apr 2001
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Handle: RePEc:sce:scecf1:164

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Keywords: Econometric methodology; Model selection; Vector autoregression; Data mining.;

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  1. Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Econometric Society World Congress 2000 Contributed Papers 0411, Econometric Society.
  2. David F. Hendry & Hans-Martin Krolzig, 1999. "Improving on 'Data mining reconsidered' by K.D. Hoover and S.J. Perez," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 202-219.
  3. Ralf Brueggemann & Helmut Leutkepohl, 2000. "Lag Selection in Subset VAR Models with an Application to a U.S. Monetary System," Econometric Society World Congress 2000 Contributed Papers 0821, Econometric Society.
  4. Kevin D. Hoover & Stephen J. Perez, . "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Department of Economics 97-27, California Davis - Department of Economics.
  5. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-44, January.
  6. David Hendry & Hans-Martin Krolzig, 2000. "Computer Automation of General-to-Specific Model Selection Procedures," Economics Series Working Papers 3, University of Oxford, Department of Economics.
  7. Holly,Sean & Weale,Martin (ed.), 2000. "Econometric Modelling," Cambridge Books, Cambridge University Press, number 9780521650694.
  8. Hendry, David F., 1984. "Monte carlo experimentation in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 16, pages 937-976 Elsevier.
  9. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 1994. "The effects of monetary policy shocks: evidence from the flow of funds," Proceedings, Federal Reserve Bank of Dallas, issue Apr.
  10. Hendry, David F, 1980. "Econometrics-Alchemy or Science?," Economica, London School of Economics and Political Science, vol. 47(188), pages 387-406, November.
  11. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
  12. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, September.
  13. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164, September.
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Cited by:
  1. David Hendry & Hans-Martin Krolzig, 2003. "The Properties of Automatic Gets Modelling," Economics Series Working Papers 2003-W14, University of Oxford, Department of Economics.
  2. 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.
  3. Hendry, David F. & Mizon, Grayham E., 2001. "Reformulating empirical macro-econometric modelling," Discussion Paper Series In Economics And Econometrics 0104, Economics Division, School of Social Sciences, University of Southampton.
  4. Ralf BRUEGGEMANN & Hans-Martin KROLZIG & Helmut LUETKEPOHL, 2002. "Comparison of Model Reduction Methods for VAR Processes," Economics Working Papers ECO2002/19, European University Institute.
  5. Julia Campos & Neil R. Ericsson & David F. Hendry, 2005. "General-to-specific modeling: an overview and selected bibliography," International Finance Discussion Papers 838, Board of Governors of the Federal Reserve System (U.S.).
  6. Carmine Pappalardo & Gianfranco Piras, 2004. "Vector-Autoregression Approach to Forecast Italian Imports," ISAE Working Papers 42, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
  7. Valpy FitzGerald & Derya Krolzig, 2004. "Modelling the demand for emerging market assets," Money Macro and Finance (MMF) Research Group Conference 2003 29, Money Macro and Finance Research Group.
  8. Hans-Martin Krolzig & Peter Flaschel, 2003. "Wage and Price Phillips Curves An empirical analysis of destabilizing wage-price spirals," Economics Series Working Papers 2003-W16, University of Oxford, Department of Economics.
  9. Chris Bloor & Troy Matheson, 2010. "Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand," Empirical Economics, Springer, vol. 39(2), pages 537-558, October.
  10. 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.

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