Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Remarks on correlated error tests

  • Received : 2016.02.19
  • Accepted : 2016.03.22
  • Published : 2016.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

The Durbin-Watson (DW) test in regression model and the Ljung-Box (LB) test in ARMA (autoregressive moving average) model are typical examples of correlated error tests. The DW test is used for detecting autocorrelation of errors using the residuals from a regression analysis. The LB test is used for specifying the correct ARMA model using the first some sample autocorrelations based on the residuals of a tted ARMA model. In this article, simulations with four data generating processes have been carried out to evaluate their performances as correlated error tests. Our simulations show that the DW test is severely dependent on the assumed AR(1) model but isn't sensitive enough to reject the misspecified model and that the LB test reports lackluster performance in general.

Keywords

References

  1. Box, G. E. P and Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models Journal of the American Statistical Association, 65, 1509-1526. https://doi.org/10.1080/01621459.1970.10481180
  2. Durbin, J. and Watson, G. S. (1950). Testing for serial correlation in least square regression: I. Biometrika, 37, 409-428.
  3. Durbin, J. and Watson, G. S. (1951). Testing for serial correlation in least square regression: II. Biometrika, 38, 159-177. https://doi.org/10.1093/biomet/38.1-2.159
  4. Ha, J. and Jung, J. M. (2015). A study on the slope sign test for explosive autoregressive models. Journal of the Korean Data & Information Science Society, 26, 791-799. https://doi.org/10.7465/jkdi.2015.26.4.791
  5. Hwang, H. (2014). Support vector quantile regression for autoregressive data. Journal of the Korean Data & Information Science Society, 25, 1539-1547. https://doi.org/10.7465/jkdi.2014.25.6.1539
  6. Ljung, G. M. and Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65, 297-303. https://doi.org/10.1093/biomet/65.2.297
  7. Pena, D. and Rodriguez, J. (2002). A Powerful Portmanteau Test of Lack of Fit for Time Series. Journal of the American Statistical Association, 97, 601-610. https://doi.org/10.1198/016214502760047122