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Selection of the Truncation Lag in Structural VARs (or VECMs) with Long-Run Restrictions

  • Alain DeSerres
  • Alain Guay

The authors examine the issue of lag-length selection in the context of a structural vector autoregression (VAR) and a vector error-correction model with long-run restrictions. First, they show that imposing long- run restrictions implies, in general, a moving-average (MA) component in the stationary multivariate representation. Then they examine the sensitivity of estimates of the permanent and transitory components to the selection of the lag length required in a VAR system to approximate this MA component. In summary, they find that using a lag structure that is too short can lead to a significant estimation bias of the permanent and transitory components. In addition, in comparing four different lag- selection criteria, they find that the Schwarz information criterion systematically underperforms relative to the other tests. More generally, as the order of the VAR that best approximates the data- generating process increases, the sequence-based tests (Wald, likelihood ratio) tend to provide more reliable results than the information-based tests (Akaike, Schwarz).

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Paper provided by EconWPA in its series Econometrics with number 9510001.

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Length: 39 pages
Date of creation: 13 Oct 1995
Date of revision:
Handle: RePEc:wpa:wuwpem:9510001
Note: 39 printed pages, compressed PostScript file. Other recent Bank of Canada working papers are listed on the last page of this report.
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  1. Lippi, Marco & Reichlin, Lucrezia, 1993. "The Dynamic Effects of Aggregate Demand and Supply Disturbances: Comment," American Economic Review, American Economic Association, vol. 83(3), pages 644-52, June.
  2. Ng, S. & Perron, P., 1994. "Unit Root Tests ARMA Models with Data Dependent Methods for the Selection of the Truncation Lag," Cahiers de recherche 9423, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  3. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : I. The basic neoclassical model," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 195-232.
  4. Lewis, Richard & Reinsel, Gregory C., 1985. "Prediction of multivariate time series by autoregressive model fitting," Journal of Multivariate Analysis, Elsevier, vol. 16(3), pages 393-411, June.
  5. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  6. Bernanke, Ben S., 1986. "Alternative explanations of the money-income correlation," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 25(1), pages 49-99, January.
  7. Robert G. King & Charles I. Plosser & James H. Stock & Mark W. Watson, 1987. "Stochastic Trends and Economic Fluctuations," NBER Working Papers 2229, National Bureau of Economic Research, Inc.
  8. Jon Faust & Eric M. Leeper, 1994. "When do long-run identifying restrictions give reliable results?," FRB Atlanta Working Paper No. 94-2, Federal Reserve Bank of Atlanta.
  9. Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
  10. Olivier J. Blanchard & Mark W. Watson, 1986. "Are Business Cycles All Alike?," NBER Chapters, in: The American Business Cycle: Continuity and Change, pages 123-180 National Bureau of Economic Research, Inc.
  11. Gamber, Edward N & Joutz, Frederick L, 1993. "The Dynamic Effects of Aggregate Demand and Supply Disturbances: Comment," American Economic Review, American Economic Association, vol. 83(5), pages 1387-93, December.
  12. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-73, September.
  13. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-16.
  14. Hall, Alastair R, 1994. "Testing for a Unit Root in Time Series with Pretest Data-Based Model Selection," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 461-70, October.
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