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Testing for Coefficient Stability of AR(1) Model When the Null is an Integrated or a Stationary Process

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  • Daisuke Nagakura

    (Institute for Monetary and Economic Studies, Bank of Japan (E-mail: daisuke.nagakura@boj.or.jp))

Abstract

In this paper, we propose a test for coefficient stability of an AR(1) model against the random coefficient autoregressive model of order 1 or RCA(1) model without assuming a stationary nor a non- stationary process under the null hypothesis of constant coefficient. The proposed test is obtained as a modification of the locally best invariant (LBI) test by Lee (1998). We examine finite sample properties of the proposed test by Monte Carlo experiments comparing with other existing tests including the LBI test by McCabe and Tremayne (1995), which is for the null of unit root against the alternative of stochastic unit root.

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Bibliographic Info

Paper provided by Institute for Monetary and Economic Studies, Bank of Japan in its series IMES Discussion Paper Series with number 07-E-20.

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Date of creation: Nov 2007
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Handle: RePEc:ime:imedps:07-e-20

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Keywords: Random Coefficient Autoregressive Model; Stability; Constancy;

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References

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  1. Granger, Clive W. J. & Swanson, Norman R., 1997. "An introduction to stochastic unit-root processes," Journal of Econometrics, Elsevier, vol. 80(1), pages 35-62, September.
  2. Pierre Perron & Tomoyoshi Yabu, 2007. "Estimating Deterministic Trend with an Integrated or Stationary Noise Component," Boston University - Department of Economics - Working Papers Series WP2007-020, Boston University - Department of Economics.
  3. Michael F. Bleaney & Stephen J. Leybourne & Paul Mizen, 1999. "Mean Reversion of Real Exchange Rates in High-Inflation Countries," Southern Economic Journal, Southern Economic Association, vol. 65(4), pages 839-854, April.
  4. Charemza W.W. & M. Lifshits & S. Makarova, 2002. "Conditional testing for unit-root bilinearity in financial time series: some theoretical and empirical results," Computing in Economics and Finance 2002 251, Society for Computational Economics.
  5. Robert Sollis & Paul Newbold & Stephen Leybourne, 2000. "Stochastic unit roots modelling of stock price indices," Applied Financial Economics, Taylor & Francis Journals, vol. 10(3), pages 311-315.
  6. Michael Bleaney & Stephen J. Leybourne, 2003. "Real Exchange Rate Dynamics Under The Current Float: A Re-Examination," Manchester School, University of Manchester, vol. 71(2), pages 156-171, 03.
  7. Alexander Aue & Lajos Horváth & Josef Steinebach, 2006. "Estimation in Random Coefficient Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(1), pages 61-76, 01.
  8. Hansen, Bruce E., 1992. "Convergence to Stochastic Integrals for Dependent Heterogeneous Processes," Econometric Theory, Cambridge University Press, vol. 8(04), pages 489-500, December.
  9. Leybourne, S J & McCabe, B P M & Tremayne, A R, 1996. "Can Economic Time Series Be Differenced to Stationarity?," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 435-46, October.
  10. Marc Hallin & Abdelhadi Akharif, 2003. "Efficient detection of random coefficients in AR(p) models," ULB Institutional Repository 2013/2121, ULB -- Universite Libre de Bruxelles.
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Cited by:
  1. Daisuke Nagakura, 2009. "Inconsistency of a Unit Root Test against Stochastic Unit Root Processes," IMES Discussion Paper Series 09-E-23, Institute for Monetary and Economic Studies, Bank of Japan.
  2. Nagakura, Daisuke, 2009. "Asymptotic theory for explosive random coefficient autoregressive models and inconsistency of a unit root test against a stochastic unit root process," Statistics & Probability Letters, Elsevier, vol. 79(24), pages 2476-2483, December.

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