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To difference or not to difference: a Monte Carlo investigation of inference in vector autoregression models

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  • Richard A. Ashley
  • Randal J. Verbrugge

Abstract

It is often unclear whether time series displaying substantial persistence should be modelled as a vector autoregression in levels (perhaps with a trend term) or in differences. The impact of this decision on inference is examined here using Monte Carlo simulation. In particular, the size and power of variable inclusion (Granger causality) tests and the coverage of impulse response function confidence intervals are examined for simulated vector autoregression models using a variety of estimation techniques. We conclude that testing should be done using differenced regressors, but that overdifferencing a model yields poor impulse response function confidence interval coverage; modelling in Hodrick-Prescott filtered levels yields poor results in any case. We find that the lag-augmented vector autoregression method suggested by Toda and Yamamoto (1995) -- which models the level of the series but allows for variable inclusion testing on changes in the series -- performs well for both Granger causality testing and impulse response function estimation.

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Bibliographic Info

Article provided by Inderscience Enterprises Ltd in its journal International Journal of Data Analysis Techniques and Strategies.

Volume (Year): 1 (2009)
Issue (Month): 3 (January)
Pages: 242-274

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Handle: RePEc:ids:injdan:v:1:y:2009:i:3:p:242-274

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Web page: http://inderscience.metapress.com/link.asp?target=journal&id=121168

Related research

Keywords: vector autoregressions; VAR models; differencing; impulse response function; unit root; Monte Carlo simulation;

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Cited by:
  1. Elena Pesavento & Barbara Rossi, 2006. "Impulse Responses Confidence Intervals for Persistent Data: What Have We Learned?," Emory Economics 0603, Department of Economics, Emory University (Atlanta).
  2. Johann Burgstaller, 2009. "When and why do Austrian companies issue shares?," Empirica, Springer, vol. 36(3), pages 229-244, August.
  3. Johann Burgstaller, 2006. "The cyclicality of interest rate spreads in Austria: Evidence for a financial decelerator?," Economics working papers 2006-02, Department of Economics, Johannes Kepler University Linz, Austria.
  4. John Bluedorn & Christopher Bowdler, 2006. "The Open Economy Consequences of U.S. Monetary Policy," Economics Papers 2006-W04, Economics Group, Nuffield College, University of Oxford.
  5. Johann Burgstaller, 2006. "Bank income and profits over the business and interest rate cycle," Economics working papers 2006-11, Department of Economics, Johannes Kepler University Linz, Austria.
  6. Ryan R. Brady & Derek Stimel & Steven Sumner, 2012. "A Time Series Test of the Direct Wealth Effect," Departmental Working Papers 40, United States Naval Academy Department of Economics.
  7. Johann Burgstaller, 2005. "Interest rate pass-through estimates from vector autoregressive models," Economics working papers 2005-10, Department of Economics, Johannes Kepler University Linz, Austria.
  8. Thesia I. Garner & Randal Verbrugge, 2007. "Puzzling Divergence of U.S. Rents and User Costs, 1980-2004: Summary and Extensions," Working Papers 409, U.S. Bureau of Labor Statistics.

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