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A Practitioner's Guide to Lag-Order Selection for Vector Autoregressions

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  • Ivanov, Ventzislav
  • Kilian, Lutz

Abstract

An important preliminary step in impulse response analysis is to select the vector autoregressive (VAR) lag order from the data, yet little is known about the implications of alternative lag order selection criteria for the accuracy of the impulse response estimates. In this Paper, we compare the criteria most commonly used in applied work in terms of the mean-squared error of the implied impulse response estimates. We conclude that for monthly VAR models, the Akaike Information Criterion (AIC) produces the most accurate structural and semi-structural impulse response estimates for realistic sample sizes. For quarterly VAR models, the Hannan-Quinn Criterion (HQC) appears to be the most accurate criterion with the exception of sample sizes smaller than 120, for which the Schwarz Information Criterion (SIC) is more accurate. For persistence profiles based on quarterly vector error correction (VEC) models, the SIC is the most accurate criterion for all realistic sample sizes. Sequential Lagrange-multiplier and likelihood ratio tests cannot be recommended.

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Bibliographic Info

Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 2685.

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Date of creation: Jan 2001
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Handle: RePEc:cpr:ceprdp:2685

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Keywords: Impulse Responses; Lag Orders; Model Selection; VAR; VEC;

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References

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Cited by:
  1. Massimo Guidolin & Allan Timmerman, 2005. "Term structure of risk under alternative econometric specifications," Working Papers 2005-001, Federal Reserve Bank of St. Louis.
  2. Canova, Fabio, 2002. "G-7 Inflation Forecasts," CEPR Discussion Papers 3283, C.E.P.R. Discussion Papers.
  3. Giovanni Razzu & Carl Singleton, 2013. "Are Business Cycles Gender Neutral?," Economics & Management Discussion Papers em-dp2013-07, Henley Business School, Reading University.
  4. Jonathan McCarthy & Egon Zakrajsek, 2002. "Inventory dynamics and business cycles: what has changed?," Staff Reports 156, Federal Reserve Bank of New York.

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