DOI QR코드

DOI QR Code

Semiparametric Seasonal Cointegrating Rank Selection

Seong, Byeong-Chan;Ahn, Sung-K.;Ch, Sin-Sup

  • Received : 20110700
  • Accepted : 20110800
  • Published : 2011.10.31

Abstract

This paper considers the issue of seasonal cointegrating rank selection by information criteria as the extension of Cheng and Phillips (2009). The method does not require the specification of lag length in vector autoregression, is convenient in empirical work, and is in a semiparametric context because it allows for a general short memory error component in the model with only lags related to error correction terms. Some limit properties of usual information criteria are given for the rank selection and small Monte Carlo simulations are conducted to evaluate the performances of the criteria.

Keywords

Seasonal cointegration;information criteria;nonparametric model selection

References

  1. Ahn, S. K., Cho, S. and Seong, B. C. (2004). Inference of seasonal cointegration: Gaussian reduced rank estimation and tests for various types of cointegration, Oxford Bulletin of Economics and Statistics, 66, 261-284. https://doi.org/10.1111/j.0305-9049.2003.00100.x
  2. Ahn, S. K. and Reinsel, G. C. (1994). Estimation of partially nonstationary vector autoregressive models with seasonal behavior, Journal of Econometrics, 62, 317-350. https://doi.org/10.1016/0304-4076(94)90027-2
  3. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov and F. Csaki (Eds.), Second International Symposium on Information Theory, Akademiai Kiado, Budapest.
  4. Cheng, X. and Phillips, P. C. B. (2008). Cointegrating rank selection in models with time-varying variance, Cowles Foundation Discussion Paper, No. 1688.
  5. Cheng, X. and Phillips, P. C. B. (2009). Semiparametric cointegrating rank selection, The Econometrics Journal, 12, S83-S104. https://doi.org/10.1111/j.1368-423X.2008.00270.x
  6. Cubadda, G. (2001). Complex reduced rank models for seasonally cointegrated time series, Oxford Bulletin of Economics and Statistics, 63, 497-511. https://doi.org/10.1111/1468-0084.00231
  7. Hannan, E. J. and Quinn, B. G. (1979). The determination of the order of an autoregression, Journal of the Royal Statistical Society, Series B, 41, 190-195.
  8. Johansen, S. (1996). Likelihood-Based Inference in Cointegrated Vector Autoregressive Models, 2nd, Oxford University Press, Oxford.
  9. Johansen, S. and Schaumburg, E. (1999). Likelihood analysis of seasonal cointegration, Journal of Econometrics, 88, 301-339. https://doi.org/10.1016/S0304-4076(98)00035-9
  10. Phillips, P. C. B. (2008). Unit root model selection, Journal of the Japan Statistical Society, 38, 65-74. https://doi.org/10.14490/jjss.38.65
  11. Schwarz, G. (1978). Estimating the dimension of a model, Annals of Statistics, 6, 461-464. https://doi.org/10.1214/aos/1176344136
  12. Seong, B. (2009). Bonferroni correction for seasonal cointegrating ranks, Economics Letters, 103, 42-44. https://doi.org/10.1016/j.econlet.2009.01.017
  13. Seong, B., Cho, S. and Ahn, S. K. (2006). Maximum eigenvalue test for seasonal cointegrating ranks, Oxford Bulletin of Economics and Statistics, 68, 497-514. https://doi.org/10.1111/j.1468-0084.2006.00174.x
  14. Seong, B. and Yi, Y. J. (2008). Joint test for seasonal cointegrating ranks, Communications of the Korean Statistical Society, 15, 719-726. https://doi.org/10.5351/CKSS.2008.15.5.719