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Simulating small-sample properties of the maximum likelihood cointegration method : estimation and testing

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  • Tor Jacobson

    (Department of Statistics, Uppsala University, Sweden)

Abstract

This paper analyzes - using Monte Carlo simulation - small-sample properties of the maximum likelihood cointegration method for estimation and inference in cointegrated systems. The simulations of a bivariate system concentrate on the following; the estimator of the cointegrating vector; the trace test for determining cointegrating rank, and the likelihood ratio and Wald tests for linear restrictions on the cointegrating vector: Furthermore, we introduce autoregressive conditional heteroscedasticity, as well as multivariate non-normality in the form of excess skewness and kurtosis, in the error process. All in all, the results suggest that the maximum likelihood method displays desirable features as long as the samples are of reasonable sizes.

Suggested Citation

  • Tor Jacobson, 1995. "Simulating small-sample properties of the maximum likelihood cointegration method : estimation and testing," Finnish Economic Papers, Finnish Economic Association, vol. 8(2), pages 96-107, Autumn.
    • Handle: RePEc:fep:journl:v:8:y:1995:i:2:p:96-107
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    5. Gregory C. Reinsel & Sung K. Ahn, 1992. "Vector Autoregressive Models With Unit Roots And Reduced Rank Structure:Estimation. Likelihood Ratio Test, And Forecasting," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(4), pages 353-375, July.
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    8. Engle, Robert F. & Granger, C. W. J. & Kraft, Dennis, 1984. "Combining competing forecasts of inflation using a bivariate arch model," Journal of Economic Dynamics and Control, Elsevier, vol. 8(2), pages 151-165, November.
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    Cited by:

    1. Larsson, Rolf & Villani, Mattias, 2001. "A distance measure between cointegration spaces," Economics Letters, Elsevier, vol. 70(1), pages 21-27, January.
    2. Hjelm, Goran & Johansson, Martin W., 2005. "A Monte Carlo study on the pitfalls in determining deterministic components in cointegrating models," Journal of Macroeconomics, Elsevier, vol. 27(4), pages 691-703, December.
    3. Johansen, Soren, 2002. "A small sample correction for tests of hypotheses on the cointegrating vectors," Journal of Econometrics, Elsevier, vol. 111(2), pages 195-221, December.
    4. Richard G. Anderson & Hailong Qian & Robert H. Rasche, 2006. "Analysis of panel vector error correction models using maximum likelihood, the bootstrap, and canonical-correlation estimators," Working Papers 2006-050, Federal Reserve Bank of St. Louis.
    5. Annika Alexius, 1997. "Import prices and nominal exchange rates in Sweden," Finnish Economic Papers, Finnish Economic Association, vol. 10(2), pages 99-107, Autumn.
    6. Eriksson , Åsa, 2004. "Testing Structural Hypotheses on Cointegration Vectors: A Monte Carlo Study," Working Papers 2004:29, Lund University, Department of Economics.
    7. Murat Duran & Doruk Kucuksarac, 2012. "Are Swap and Bond Markets Alternatives to Each Other in Turkey?," Working Papers 1223, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    8. Jacobson, Tor & Larsson, Rolf, 1999. "Bartlett corrections in cointegration testing," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 203-225, August.
    9. Anton Bekkerman & Thomas Gumbley & Gary W. Brester, 2021. "The Impacts of Biofuel Policies on Spatial and Vertical Price Relationships in the US Fertilizer Industry," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(2), pages 802-822, June.
    10. Hjelm, Göran & Johansson, Martin W, 2002. "A Monte Carlo Study on the Pitfalls in Determining Deterministic Components in Cointegrating Models," Working Papers 2002:3, Lund University, Department of Economics.

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    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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

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