An improved portmanteau test for autocorrelated errors in interrupted time-series regression models

Behavior Research Methods

Volumn 39, Issue 3, 2007, Pages 343-349

  Huitema, Bradley E ^a   McKean, Joseph W ^a  

^a WESTERN MICHIGAN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34948889247     PISSN: 1554351X     EISSN: None     Source Type: Journal    
DOI: 10.3758/BF03193002     Document Type: Article

References (28)

1. ANDREWS, D. W. K., & PLOBERGER, W. (1996). Testing for serial correlation against an ARMA(1,1) process. Journal of the American Statistical Association, 91, 1331-1342.
2. BOWERMAN, B. L., O'CONNELL, R. T., & KOEHLER, A. B. (2005). Forecasting, time series, and regression: An applied approach (4th ed.). Belmont, CA: Thomson, Brooks/Cole.
3. BOX, G. E. P., & 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.
4. BRADLEY, J. V. (1978). Robustness? British Journal of Mathematical & Statistical Psychology, 31, 144-152.
5. DURBIN, J., & WATSON, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37, 409-428.
6. DURBIN, J., & WATSON, G. S. (1951). Testing for serial correlation in least squares regression: II. Biometrika, 38, 159-178.
7. GREENE, W. H. (2000). Econometric analysis (4th ed.). Upper Saddle River, NJ: Prentice Hall.
8. HUITEMA, B. E. (2004), Analysis of interrupted time-series experiments using ITSE: A critique. Understanding Statistics: Statistical Issues in Psychology, Education, & the Social Sciences, 3, 27-46.
9. HUITEMA, B. E., & MCKEAN, J. W. (1991). Autocorrelation estimation and inference with small samples. Psychological Bulletin, 110, 291-304.
10. HUITEMA, B. E., & MCKEAN, J. W. (1998). Irrelevant autocorrelation in least-squares intervention models. Psychological Methods, 3, 104-116.
11. HUITEMA, B. E., & MCKEAN, J. W. (2000a). Design specification issues in time-series intervention models. Educational & Psychological Measurement, 60, 38-58.
12. HUITEMA, B. E., & MCKEAN, J. W. (2000b). A simple and powerful test for autocorrelated errors in OLS intervention models. Psychological Reports, 87, 3-20.
13. HUITEMA, B. E., & MCKEAN, J. W. (2007). Identifying autocorrelation generated by various error processes in interrupted time-series regression designs: A comparison of AR1 and portmanteau tests. Educational & Psychological Measurement, 67, 447-459.
14. HUITEMA, B. E., MCKEAN, J. W., & LARAWAY, S. (in press). Time-series intervention analysis using ITSACORR: Fatal flaws. Journal of Modern Applied Statistical Methods.
15. HUITEMA, B. E., MCKEAN, J. W., & MCKNIGHT, S. D. (1994, August). Small-sample time-series intervention analysis: Problems and solutions. Paper presented at the meeting of the American Psychological Association, Los Angeles.
16. JOHNSTON, J. (1984). Econometric methods (3rd ed.). New York: McGraw-Hill.
17. KAHANER, D., MOLER, C., & NASH, S. (1989). Numerical methods and software. Englewood Cliffs, NJ: Prentice Hall.
18. KUTNER, M. H., NACHTSHEIM, C. J., & NETER, J. (2004). Applied linear regression models (4th ed.). New York: McGraw-Hill Irwin.
19. LJUNG, G. M. (1986). Diagnostic testing of univariate time series models. Biometrika, 73, 725-730.
20. LJUNG, G. M., & BOX, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65, 297-303.
21. MARSAGLIA, G., & BRAY, T. A. (1964). A convenient method for generating normal variables. SIAM Review, 6, 260-264.
22. MCKNIGHT, S., MCKEAN, J. W., & HUITEMA, B. E. (2000). A double bootstrap method to analyze linear models with autoregressive error terms. Psychological Methods, 5, 87-101.
23. MILLS, T. C. (1990). lime series techniques for economists. Cambridge: Cambridge University Press.
24. NEWTON, H. J. (1988). TIMESLAB: A Time Series Analysis Laboratory. Pacific Grove, CA: Wadsworth & Brooks/Cole.
25. SCHWARZ, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
26. SHUMWAY, R. H. (1988). Applied statistical time series analysis. Englewood Cliffs, NJ: Prentice Hall.
27. WHITE, K. (1993). SHAZAM(Version 7) [Computer software]. Vancouver: University of British Columbia, Department of Economics.
28. YAFFEE, R. A., & MCGEE, M. (2000). Introduction to time series analysis and forecasting: With applications of SAS and SPSS. San Diego: Academic Press.