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


  • 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.

Suggested Citation

  • Fraccaro, R. & Hyndman, R. & Veevers, A., 1998. "Residual Diagnostic Plots for Checking for model Mis-Specification in Time Series Regression," Monash Econometrics and Business Statistics Working Papers 12/98, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:1998-12

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    Cited by:

    1. 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.
    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 & 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.

    More about this item



    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes


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