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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.

Suggested Citation

  • Hans-Martin Krolzig, 2001. "General--to--Specific Reductions of Vector Autoregressive Processes," Computing in Economics and Finance 2001 164, Society for Computational Economics.
  • Handle: RePEc:sce:scecf1:164
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    Cited by:

    1. David F. Hendry & Hans-Martin Krolzig, 2005. "The Properties of Automatic "GETS" Modelling," Economic Journal, Royal Economic Society, vol. 115(502), pages 32-61, March.
    2. 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.
    3. 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.
    4. Peter Flaschel & Hans-Martin Krolzig, 2003. "Wage and Price Phillips Curves An empirical analysis of destabilizing wage-price spirals," Economics Papers 2003-W16, Economics Group, Nuffield College, University of Oxford.
    5. Valpy FitzGerald & Derya Krolzig, 2003. "Modeling the Demand for Emerging Market Assets," OFRC Working Papers Series 2003fe10, Oxford Financial Research Centre.
    6. Ralf BRUEGGEMANN & Hans-Martin KROLZIG & Helmut LUETKEPOHL, 2002. "Comparison of Model Reduction Methods for VAR Processes," Economics Working Papers ECO2002/19, European University Institute.
    7. Mark Pabatang Doblas & Maria Cecilia Lagaras, 2023. "The Granger Causality of Bahrain Stocks, Bitcoin, and Other Commodity Asset Returns: Evidence of Short-Term Return Spillover Before and During the COVID-19 Pandemic," International Journal of Business Analytics (IJBAN), IGI Global, vol. 10(1), pages 1-20, January.
    8. 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.
    9. 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.
    10. Krolzig, Hans-Martin & Sserwanja, Isaac, 2015. "Fiscal Policy, Interest Rates, and Output: Equilibrium-Correction Dynamics in the US Economy," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112813, Verein für Socialpolitik / German Economic Association.
    11. R. Scott Hacker & Abdulnasser Hatemi-J, 2021. "Model selection in time series analysis: using information criteria as an alternative to hypothesis testing," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 49(6), pages 1055-1075, September.
    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. Carmine Pappalardo & Gianfranco Piras, 2004. "Vector-Autoregression Approach to Forecast Italian Imports," ISAE Working Papers 42, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    14. Dalheimer, Bernhard & Brümmer, Bernhard & Jaghdani, Tinoush Jamali, 2017. "Impacts of Export Restrictions on Food Price Volatility: Evidence from VAR-X and EGARCH-X Models," 57th Annual Conference, Weihenstephan, Germany, September 13-15, 2017 262151, German Association of Agricultural Economists (GEWISOLA).
    15. Sudarshan Kumar & Tiziana Di Matteo & Anindya S. Chakrabarti, 2020. "Disentangling shock diffusion on complex networks: Identification through graph planarity," Papers 2001.01518, arXiv.org.
    16. 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.).

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    More about this item

    Keywords

    Econometric methodology; Model selection; Vector autoregression; Data mining.;
    All these keywords.

    JEL classification:

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