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Residual Diagnostic Plots for Checking for model Mis-Specification in Time Series Regression


Author Info

  • Fraccaro, R.
  • Hyndman, R.
  • Veevers, A.


This paper considers residuals for time series regression. Despite much literature on visual diagnostics for uncorrelated data, there is little on the autocorrelated case. In order to examine various aspects of the fitted time series regression model, three residuals are considered. The fitted regression model can be checked using orthogonal residuals; the time series error model can be analysed using marginal residuals; and the white noise error component can be tested using conditional residuals. When used together, these residuals allow identification of outliers, model mis-specification and mean shifts.

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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 12/98.

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Length: 21 pages
Date of creation: 1998
Date of revision:
Publication status: Published in Australian and New Zealand Journal of Statistics, 42(4), 463-477.
Handle: RePEc:msh:ebswps:1998-12

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Cited by:
  1. E. Andres Houseman & Brent Coull & Louise Ryan, 2004. "A Functional-Based Distribution Diagnostic for a Linear Model with Correlated Outcomes: Technical Report," Harvard University Biostatistics Working Paper Series 1018, Berkeley Electronic Press.
  2. Xie, Feng-Chang & Lin, Jin-Guan & Wei, Bo-Cheng, 2009. "Diagnostics for skew-normal nonlinear regression models with AR(1) errors," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4403-4416, October.
  3. E. Andres Houseman & Louise Ryan & Brent Coull, 2004. "Cholesky Residuals for Assessing Normal Errors in a Linear Model with Correlated Outcomes: Technical Report," Harvard University Biostatistics Working Paper Series 1019, Berkeley Electronic Press.


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