• Title/Summary/Keyword: Seasonal cointegration

Search Result 17, Processing Time 0.018 seconds

Seasonal Cointegration Rank Tests for Daily Data

  • Song, Dae-Gun;Park, Suk-Kyung;Cho, Sin-Sup
    • Journal of the Korean Data and Information Science Society
    • /
    • v.16 no.3
    • /
    • pp.695-703
    • /
    • 2005
  • This paper extends the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. The finite sample distribution of the associated rank test for dally data is also presented.

  • PDF

A Feasible Two-Step Estimator for Seasonal Cointegration

  • Seong, Byeong-Chan
    • Communications for Statistical Applications and Methods
    • /
    • v.15 no.3
    • /
    • pp.411-420
    • /
    • 2008
  • This paper considers a feasible two-step estimator for seasonal cointegration as the extension of $Br{\ddot{u}}ggeman$ and $L{\ddot{u}}tkepohl$ (2005). It is shown that the reducedrank maximum likelihood(ML) estimator for seasonal cointegration can still produce occasional outliers as that for non-seasonal cointegration even though the sizes of them are not extreme as those in non-seasonal cointegration. The ML estimator(MLE) is compared with the two-step estimator in a small Monte Carlo simulation study and we find that the two-step estimator can be an attractive alternative to the MLE, especially, in a small sample.

Comparison of Forecasting Performance in Multivariate Nonstationary Seasonal Time Series Models (다변량 비정상 계절형 시계열모형의 예측력 비교)

  • Seong, Byeong-Chan
    • Communications for Statistical Applications and Methods
    • /
    • v.18 no.1
    • /
    • pp.13-21
    • /
    • 2011
  • This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.

Seasonal cointegration for daily data

  • Song, Dae-Gun;Cho, Sin-Sup;Park, Suk-Kyung
    • Proceedings of the Korean Statistical Society Conference
    • /
    • 2005.05a
    • /
    • pp.13-15
    • /
    • 2005
  • In this paper, we propose an extension of the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. We presented the finite sample distribution of the associated rank test statistics for daily data.

  • PDF

Joint Test for Seasonal Cointegrating Ranks

  • Seong, Byeong-Chan;Yi, Yoon-Ju
    • Communications for Statistical Applications and Methods
    • /
    • v.15 no.5
    • /
    • pp.719-726
    • /
    • 2008
  • In this paper we consider a joint test for seasonal cointegrating(CI) ranks that enables us to simultaneously model cointegrated structures across seasonal unit roots in seasonal cointegration. A CI rank test for a single seasonal unit root is constructed and extended to a joint test for multiple seasonal unit roots. Their asymptotic distributions and selected critical values for the joint test are obtained. Through a small Monte Carlo simulation study, we evaluate performances of the tests.

Tests for Seasonal Cointegrating Vectors

  • Seong, Byeong-C.;Cho, Sin-S.;Ahn, Sung-K.
    • Proceedings of the Korean Statistical Society Conference
    • /
    • 2003.10a
    • /
    • pp.275-279
    • /
    • 2003
  • We obtain the asymptotic distributions of tests statistics for various types of seasonal cointegration based on GRR estimators of Ahn and Cho (2003). These tests are useful in testing for restrictions about cointegrating vectors after Chi-square tests for CCI and common PCIV in Ahn and Cho (2003) or tests for the known CCI and the known PCIVs have been performed.

  • PDF

INFERENCE ON THE SEASONALLY COINTEGRATED MODEL WITH STRUCTURAL CHANGES

  • Song, Dae-Gun;Cho, Sin-Sup
    • Journal of the Korean Statistical Society
    • /
    • v.36 no.4
    • /
    • pp.501-522
    • /
    • 2007
  • We propose an estimation procedure that can be used for detecting structural changes in the seasonal cointegrated vector autoregressive model. The asymptotic properties of the estimates and the test statistics for the parameter change are provided. A simulation example is presented to illustrate this method and its concept.

Effects of the Misspecification of Cointegrating Ranks in Seasonal Models

  • Seong, Byeong-Chan;Cho, Sin-Sup;Ahn, Sung-K.;Hwang, S.Y.
    • The Korean Journal of Applied Statistics
    • /
    • v.21 no.5
    • /
    • pp.783-789
    • /
    • 2008
  • We investigate the effects of the misspecification of cointegrating(CI) ranks at other frequencies on the inference of seasonal models at the frequency of interest; our study includes tests for CI ranks and estimation of CI vectors. Earlier studies focused mostly on a single frequency corresponding to one seasonal root at a time, ignoring possible cointegration at the remaining frequencies. We investigate the effects of the mis-specification, especially in finite samples, by adopting Gaussian reduced rank(GRR) estimation by Ahn and Reinsel (1994) that considers cointegration at all frequencies of seasonal unit roots simultaneously. It is observed that the identification of the seasonal CI rank at the frequency of interest is sensitive to the mis-prespecification of the CI ranks at other frequencies, mainly when the CI ranks at the remaining frequencies are underspecified.

Estimation of Seasonal Cointegration under Conditional Heteroskedasticity

  • Seong, Byeongchan
    • Communications for Statistical Applications and Methods
    • /
    • v.22 no.6
    • /
    • pp.615-624
    • /
    • 2015
  • We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.

GMM Estimation for Seasonal Cointegration

  • Park, Suk-Kyung;Cho, Sin-Sup;Seon, Byeong-Chan
    • The Korean Journal of Applied Statistics
    • /
    • v.24 no.2
    • /
    • pp.227-237
    • /
    • 2011
  • This paper considers a generalized method of moments(GMM) estimation for seasonal cointegration as the extension of Kleibergen (1999). We propose two iterative methods for the estimation according to whether parameters in the model are simultaneously estimated or not. It is shown that the GMM estimator coincides in form to a maximum likelihood estimator or a feasible two-step estimator. In addition, we derive its asymptotic distribution that takes the same form as that in Ahn and Reinsel (1994).