The ARIMA Procedure

Akaike, H. (1974), “A New Look at the Statistical Model Identification,” IEEE Transactions on Automatic Control, AC-19, 716–723.

Anderson, T. W. (1971), The Statistical Analysis of Time Series, New York: John Wiley & Sons.

Andrews, D. F. and Herzberg, A. M. (1985), A Collection of Problems from Many Fields for the Student and Research Worker, New York: Springer-Verlag.

Ansley, C. F. (1979), “An Algorithm for the Exact Likelihood of a Mixed Autoregressive–Moving Average Process,” Biometrika, 66, 59–65.

Ansley, C. F. and Newbold, P. (1980), “Finite Sample Properties of Estimators for Autoregressive Moving-Average Models,” Journal of Econometrics, 13, 159–183.

Bhansali, R. J. (1980), “Autoregressive and Window Estimates of the Inverse Correlation Function,” Biometrika, 67, 551–566.

Box, G. E. P. and Jenkins, G. M. (1976), Time Series Analysis: Forecasting and Control, Rev. Edition, San Francisco: Holden-Day.

Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994), Time Series Analysis: Forecasting and Control, 3rd Edition, Englewood Cliffs, NJ: Prentice-Hall.

Box, G. E. P. and Tiao, G. C. (1975), “Intervention Analysis with Applications to Economic and Environmental Problems,” Journal of the American Statistical Association, 70, 70–79.

Brocklebank, J. C. and Dickey, D. A. (2003), SAS for Forecasting Time Series, 2nd Edition, Cary, NC: SAS Institute Inc.

Brockwell, P. J. and Davis, R. A. (1991), Time Series: Theory and Methods, 2nd Edition, New York: Springer-Verlag.

Chatfield, C. (1980), “Inverse Autocorrelations,” Journal of the Royal Statistical Society, Series A, 142, 363–377.

Choi, B. (1992), ARMA Model Identification, New York: Springer-Verlag.

Cleveland, W. S. (1972), “The Inverse Autocorrelations of a Time Series and Their Applications,” Technometrics, 14, 277.

Cobb, G. W. (1978), “The Problem of the Nile: Conditional Solution to a Change Point Problem,” Biometrika, 65, 243–251.

Davidson, J. (1981), “Problems with the Estimation of Moving Average Models,” Journal of Econometrics, 16, 295.

Davies, N., Triggs, C. M., and Newbold, P. (1977), “Significance Levels of the Box-Pierce Portmanteau Statistic in Finite Samples,” Biometrika, 64, 517–522.

De Jong, P. and Penzer, J. (1998), “Diagnosing Shocks in Time Series,” Journal of the American Statistical Association, 93, 796–806.

Dickey, D. A. (1976), Estimation and Testing of Nonstationary Time Series, Ph.D. diss., Iowa State University.

Dickey, D. A. and Fuller, W. A. (1979), “Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association, 74, 427–431.

Dickey, D. A., Hasza, D. P., and Fuller, W. A. (1984), “Testing for Unit Roots in Seasonal Time Series,” Journal of the American Statistical Association, 79, 355–367.

Dunsmuir, W. (1984), “Large Sample Properties of Estimation in Time Series Observed at Unequally Spaced Times,” in E. Parzen, ed., Time Series Analysis of Irregularly Observed Data, New York: Springer-Verlag.

Findley, D. F., Monsell, B. C., Bell, W. R., Otto, M. C., and Chen, B. C. (1998), “New Capabilities and Methods of the X-12-ARIMA Seasonal Adjustment Program,” Journal of Business and Economic Statistics, 16, 127–176.

Fuller, W. A. (1976), Introduction to Statistical Time Series, New York: John Wiley & Sons.

Hamilton, J. D. (1994), Time Series Analysis, Princeton, NJ: Princeton University Press.

Hannan, E. J. and Rissanen, J. (1982), “Recursive Estimation of Mixed Autoregressive Moving Average Order,” Biometrika, 69, 81–94.

Harvey, A. C. (1981), Time Series Models, New York: John Wiley & Sons.

Jones, R. H. (1980), “Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations,” Technometrics, 22, 389–396.

Kohn, R. and Ansley, C. F. (1985), “Efficient Estimation and Prediction in Time Series Regression Models,” Biometrika, 72, 694–697.

Ljung, G. M. and Box, G. E. P. (1978), “On a Measure of Lack of Fit in Time Series Models,” Biometrika, 65, 297–303.

Montgomery, D. C. and Johnson, L. A. (1976), Forecasting and Time Series Analysis, New York: McGraw-Hill.

Morf, M., Sidhu, G. S., and Kailath, T. (1974), “Some New Algorithms for Recursive Estimation on Constant Linear Discrete Time Systems,” IEEE Transactions on Automatic Control, 19, 315–323.

Nelson, C. R. (1973), Applied Time Series for Managerial Forecasting, San Francisco: Holden-Day.

Newbold, P. (1981), “Some Recent Developments in Time Series Analysis,” International Statistical Review, 49, 53–66.

Newton, H. J. and Pagano, M. (1983), “The Finite Memory Prediction of Covariance Stationary Time Series,” SIAM Journal on Scientific and Statistical Computing, 4, 330–339.

Pankratz, A. (1983), Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, New York: John Wiley & Sons.

Pankratz, A. (1991), Forecasting with Dynamic Regression Models, New York: John Wiley & Sons.

Pearlman, J. G. (1980), “An Algorithm for the Exact Likelihood of a High-Order Autoregressive–Moving Average Process,” Biometrika, 67, 232–233.

Priestley, M. B. (1981), Spectral Analysis and Time Series, London: Academic Press.

Schwarz, G. (1978), “Estimating the Dimension of a Model,” Annals of Statistics, 6, 461–464.

Stoffer, D. S. and Toloi, C. M. C. (1992), “A Note on the Ljung-Box-Pierce Portmanteau Statistic with Missing Data,” Statistics and Probability Letters, 13, 391–396.

Tsay, R. S. and Tiao, G. C. (1984), “Consistent Estimates of Autoregressive Parameters and Extended Sample Autocorrelation Function for Stationary and Nonstationary ARMA Models,” Journal of the American Statistical Association, 79, 84–96.

Tsay, R. S. and Tiao, G. C. (1985), “Use of Canonical Analysis in Time Series Model Identification,” Biometrika, 72, 299–315.

Woodfield, T. J. (1987), “Time Series Intervention Analysis Using SAS Software,” in Proceedings of the Twelfth Annual SAS Users Group International Conference, Cary, NC: SAS Institute Inc.