Detecting shocks: Outliers and breaks in time series

Journal of Econometrics

Volumn 80, Issue 2, 1997, Pages 387-422

  Atkinson, A C ^a   Koopman, S J ^c   Shephard, N ^b  

^a LONDON SCHOOL OF ECONOMICS   (United Kingdom)

^b NUFFIELD COLLEGE   (United Kingdom)

^c TILBURG UNIVERSITY   (Netherlands)

Author keywords

Added variable; Deletion methods; Diagnostics; Dynamic linear mode; EM algorithm; Fragility; Kalman filter; Monte Carlo test; Outlier; Score test; Shocks; Simulation envelope; Structural change; Structural time series model; Unobserved components model

Indexed keywords

EID: 0004191672     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-4076(97)00050-X     Document Type: Article

References (58)

1. Anderson, B.D.O., Moore, J.B., 1979. Optimal Filtering. Prentice-Hall, Englewood Cliffs, NJ.
2. Atkinson, A.C., 1981. Two graphical displays for influential and outlying observations in regression. Biometrika 68, 13-20.
3. Atkinson, A.C., 1985. Plots, Transformations, and Regression. Oxford University Press, Oxford.
4. Atkinson, A.C., 1986. Diagnostic tests for transformations. Technometrics 28, 29-37.
5. Atkinson, A.C., Koopman, S.J., Shephard, N., 1994. Outliers and switches in time series. In: Huskova, M. (Ed.), Proceedings of the Fifth Prague Symposium on Asymptotic Statistics, Prague, Artia, pp. 35-48.
6. Atkinson, A.C., Shephard, N., 1996. Deletion diagnostics for transformation of time series. Journal of Forecasting 15, to appear.
7. Balke, N.S., 1993. Detecting level shifts in time series. Journal of Business and Economic Statistics 11, 81-92.
8. Becker, R.A., Chambers, C., Wilks, A.R., 1988. The New S Language. Wadsworth and Brooks/Cole, Pacific Grove, CA.
9. 2. Biometrika 62, 243-50.
10. Box, G.E.P., Tiao, G.C., 1975. Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association 70, 70-79.
11. Bruce, A.G., Martin, R.D., 1989. Leave-k-out diagnostics for time series (with discussion). Journal of the Royal Statistical Society, Series B 51, 363-424.
12. Burman, P.J., 1985. Report on ASA fellowship 1984-5. Technical report, Statistical Research Division, US Bureau of the Census, Washington, DC.
13. Carlstein, E., 1988. Nonparametric change-point estimation. Biometrika 16, 188-197.
14. Chatterjee, S., Hadi, A.S., 1988. Sensitivity Analysis in Linear Regression. Wiley, New York.
15. Chen, C., Liu, L.-M., 1993. Joint estimation of model parameters and outlier effects in time series. Journal of the American Statistical Association 88, 284-297.
16. Christiano, L.J., 1992. Searching for a break in GDP. Journal of Business and Economic Statistics 10, 230-257.
17. Cobb, G.W., 1978. The problem of the Nile: conditional solution to a change point problem. Biometrika 65, 243-251.
18. Cook, R.D., 1977, detection of influential observations in linear regression. Technometrics 19, 15-18.
19. Cook, R.D., Weisberg, S., 1982. Residuals and Influence in Regression. Chapman and Hall, London.
20. Cox, D.R., Hinkley, D.V., 1974. Theoretical Statistics. Chapman Hall, London.
21. Cox, D.R., Reid, N., 1987. Parameter orthogonality and approximate conditional inference (with discussion). Journal of the Royal Statistical Society, Series B 49, 1-39.
22. Davison, A.C., Tsai, C.-L. 1992. Regression model diagnostics. International Statistical Review 60, 337-353.
23. de Jong, P., 1989. Smoothing and interpolation with the state space model. Journal of the American Statistical Association 84, 1085-88.
24. Dempster, A.P., Selwyn, M.R., Patel, C.M., Roth, A.J., 1984. Statistical and computational aspects of mixed model analysis. Applied Statistics 33, 203-214.
25. Flack, V.F., Flores, R.A., 1989. Using simulated envelopes in the evaluation of normal probability plots of regression residuals. Technometrics 31, 219-225.
26. Fox, A.J., 1972. Outliers in time series. Journal of the Royal Statistical Society, Series B 34, 350-363.
27. Gersch, W., Kitagawa, G., 1983. The prediction of time series with trends and seasonalities. Journal of Business and Economic Statistics 1, 253-264.
28. Hall, P., Titterington, D.M., 1989. The effect of simulation order on level accuracy and power of Monte Carlo tests. Journal of the Royal Statistical Society, Series B 51, 459-467.
29. Harrison, P.J., Veerapen, P.P., 1993. Incorporating and deleting information in dynamic models. In: Rao, T.S. (Ed.), Developments in Time Series Analysis: in Honour of Maurice B. Priestley. Chapman and Hall, London, pp. 37-49.
30. Harrison, J., West, M., 1991. Dynamic linear model diagnostics. Biometrika 78, 797-808.
31. Harvey, A.C., 1989. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge.
32. Harvey, A.C., Durbin, J., 1986. The effects of seat belt legislation on British road casualities: a case study in structural time series modelling (with discussion). Journal of the Royal Statistical Society, Series A 149, 187-227.
33. Harvey, A.C., Koopman, S.J., 1992. Diagnostic checking of unobserved components time series models. Journal of Business and Economic Statistics 10, 377-389.
34. Harvey, A.C., Ruiz, E., Shephard, N., 1994. Multivariate stochastic variance models. Review of Economic Studies 61, 247-264.
35. Kitagawa, G., 1981. A nonstationary time series model and its fitting by a recursive filter. Journal of Time Series Analysis 2, 103-116.
36. Kohn, R., Ansley, C.F., 1989. A fast algorithm for signal extraction, influence and cross-validation in state space models. Biometrika 76, 65-79.
37. Koopman, S.J., 1993. Disturbance smoother for state space models. Biometrika 80, 117-126.
38. Koopman, S.J., Shephard, N., 1992. The exact score for time series models in state space form. Biometrika 79, 823-826.
39. Ljung, G.M., 1993. On outlier detection in time series. Journal of the Royal Statistical Society, Series B 55, 559-567.
40. Ljung, G.M., Box, G.E.P., 1978. On a measure of lack of fit in time series models. Biometrika 65, 297-303.
41. MacNeill, I.B., Tang, S.M., Jandhyala, V.K., 1991. A search for the source of the Nile's change-points. Environmetrics 2, 341-375.
42. McCulloch, R.E., Tsay, R.S., 1993. Bayesian inference and prediction for mean and variance shifts in autoregressive time series. Journal of the American Statistical Association 88, 968-978.
43. Muirhead, C.R., 1986. Distinguishing outlier types in time series. Journal of the Royal Statistical Society, Series B 48, 39-47.
44. Ng, C.N., Young, P.C., 1990. Recursive estimation and forecasting of non-stationary time series. Journal of Forecasting 9, 173-204.
45. O'Hara Hines, R.J., Carter, E.M., 1993. Improved added variable and partial residual plots for the detection of influential observations in generalized linear models. Applied Statistics 42, 3-20.
46. Peña, D., 1990. Influential observations in time series. Journal of Economic and Business Statistics 8, 235-241.
47. Peña, D., 1991. Measuring influence in dynamic regression models. Technometrics 33, 93-101.
48. Perron, P., 1989. The great crash, the oil price shock and the unit root hypothesis. Econometrica 57, 1361-1401.
49. Rappoport, P., Reichlin, L., 1989. Segmented trends and non-stationary time series. Economic Journal 99, 168-177.
50. Reed, D., 1987. Whistlestop: a community alternative for crime prevention. Ph.D. Thesis, Department of Sociology, Northwestern University, Chicago.
51. Shephard, N., 1993a. Distribution of the ML estimator of a MA(1) and a local level model. Econometric Theory 9, 377-401.
52. Shephard, N., 1993b. Maximum likelihood estimation of regression models with stochastic trend components. Journal of the American Statistical Association 88, 590-595.
53. Theil, H., Wage, S., 1964. Some observations on adaptive forecasting. Management Science 10, 198-206.
54. Tsay, R.S., 1986. Time series model specification in the presence of outliers. Journal of the American Statistical Association 81, 132-141.
55. Tunnicliffe Wilson, G., 1989. On the use of marginal likelihood in time series model estimation. Journal of the Royal Statistical Society, Series B 51, 15-27.
56. Wecker, W.E., Ansley, C.F. 1983. The signal extraction approach to nonlinear regression and spline smoothing. Journal of the American Statistical Association 78, 81-89.
57. West, M., Harrison, P.J., 1989. Bayesian Forecasting and Dynamic Models. Springer, New York.
58. Wu, L.S.-Y., Hosking, J.R.M., Ravishanker, 1993. Reallocation outliers in time series. Applied Statistics 42, 301-313.