Multivariate singular spectrum analysis for forecasting revisions to real-time data

Journal of Applied Statistics

Volumn 38, Issue 10, 2011, Pages 2183-2211

  Patterson, Kerry ^a   Hassani, Hossein ^b,c   Heravi, Saeed ^d   Zhigljavsky, Anatoly ^b  

^a UNIVERSITY OF READING   (United Kingdom)

^b CARDIFF UNIVERSITY   (United Kingdom)

^c Statistical Research and Training Center   (Iran)

^d CARDIFF UNIVERSITY   (United Kingdom)

Author keywords

Data revisions; Forecasting; Hankelisation; Non parametric methods; Reconstruction; Recurrence formula; Trajectory matrix

Indexed keywords

EID: 84860407215     PISSN: 02664763     EISSN: 13600532     Source Type: Journal    
DOI: 10.1080/02664763.2010.545371     Document Type: Article

References (73)

1. L. Akritidis, Revisions to quarterly GDP growth and expenditure components, Econ. Trends December (2003), pp. 69-85.
2. C. Aldrich and M. Barkhuizen, Process system identification strategies based on the use of singular spectrum analysis, Miner. Eng. 16 (2003), pp. 815-826.
3. S.B. Aruoba, Data Revisions are not well-behaved, J. Money Credit Bank. 40 (2008), pp. 319-340.
4. J.C.K. Ash, D.J. Smyth, and S. Heravi, The accuracy of OECD forecasts for Japan, Pac. Econ. Rev. 2 (1997), pp. 25-44.
5. J. Ashley, R. Driver, S. Hayes, and C. Jeffery, Dealing with data uncertainty, Bank Engl. Q. Bull. Spring (2005), pp. 23-29.
6. D.S. Broomhead and G.P. King, Extracting qualitative dynamics from experimental data, Phys. D 20 (1986), pp. 217-236.
7. B.W. Brown and S. Maital, What do economists know? An empirical study of exeperts'exepectations, Econometrica 49 (1981), pp. 491-504.
8. F. Busetti, Preliminary data and econometric forecasting: An application with the Bank of Italy quarterly model, J. Forecast. 25 (2006), pp. 1-23.
9. J. Castle and C. Ellis, Building a real-time database for GDP(E), Bank Engl. Q. Bull. Spring (2002), pp. 42-49.
10. M.K. Clements and A.B. Galvão, Real-time forecasting with autoregressive models in the presence of data revisions, ESG,Working Paper, University ofWarwick,Warwick, UK, 2009.
11. M.P. Clements and J. Smith, A Monte Carlo investigation of forecasting performance of empirical SETAR model, J. Appl. Econom. 14 (1999), pp. 123-141.
12. W. Conrad and C. Corrado, Application of the Kalman filter to revisions in monthly retail sales estimates, J. Econ. Dyn. Control 1 (1979), pp. 177-198.
13. N.F.R. Crafts, S.J. Leybourne, and T.C. Mills, Trends and cycles in British industrial production, 1700-1913, J. R. Statist. Soc. Ser. A 152 (1989), pp. 43-60.
14. D. Croushore, Forecasting with real-time macroeconomic data, in Handbook of Economic Forecasting, Graham Elliott, CliveW.J. Granger, and Allan Timmermann, eds., North-Holland, Amsterdam, 2006.
15. D. Croushore, Frontiers of real-time data analysis, Working Paper No. 08-04, Federal Reserve Bank of Philadelphia, Philadelphia, US, 2008.
16. D. Croushore, 2009. Literature on real-time data analysis. Available at http://oncampus.richmond.edu/~dcrousho/ docs/realtime_lit.pdf.
17. D. Croushore and T. Stark, A real-time data set for macroeconomists, J. Econom. 105 (2001), pp. 111-130.
18. A. Cunningham, J. Eklund, C. Jeffery, G. Kapetanios, and V. Labhard, A state space approach to extracting the signal from uncertain data, Bank of EnglandWorking Paper No. 336, Bank of England, London, 2007.
19. R.B. D'Agostino, Transformation to normality of the null distribution of g1, Biometrika 57 (1970), pp. 679-681.
20. D. Danilov and A. Zhigljavsky, Principal Components of Time Series: The 'Caterpillar' Method, University of St. Petersburg, St. Petersburg, in Russian, 1997.
21. G. Darbellay, An adaptive histogram estimator for mutual information, UTIA Research Report 1889, Academy of Sciences, Prague, 1998.
22. D.A. Dickey and S.G. Pantula, Determining the order of differencing in autoregressive processes, J. Bus. Econ. Stat. 5 (1987), pp. 455-461.
23. F.X. Diebold and R.S. Mariano, Comparing predictive accuracy, J. Bus. Econ. Stat. 13 (1995), pp. 253-263.
24. J.A. Doornik and H. Hansen, An omnibus test for univariate and multivariate normality, Discussion Paper, Nuffield College, Oxford, 1994.
25. G. Elliott, T.J. Rothenberg, and J.H. Stock, Efficient tests for an autoregressive root, Econometrica 64 (1996), pp. 813-836.
26. J.B. Elsner and A.A. Tsonis, Singular Spectral Analysis, a New Tool in Time Series Analysis, Plenum Press, New York and London, 1996.
27. D.J. Fixler and J.J. Nalewaik, News, noise, and estimates of the 'True' unobserved state of the economy, Bureau of Economic AnalysisWorking Paper, January, Bureau of Economic Analysis, Washington, DC, 2006.
28. W. Fuller, An Introduction to Statistical Time Series, 2nd ed., JohnWiley, NewYork, 1996.
29. A. Garratt and S.P. Vahey, UK real-time macro data characteristics, Econ. J. 116 (2006), pp. F119-F135.
30. S. Ghosh, United States trade balance announcements: The nature of its data revisions, Oxford Bull. Econom. Stat. 71 (2009), pp. 371-383.
31. S. Ghosh and D. Lien, Forecasting with preliminary data, J. Forecast. 16 (1997), pp. 463-473.
32. N. Golyandina, V. Nekrutkin, and A. Zhigljavsky, Analysis of Time Series Structure: SSA and Related Techniques, Chapman & Hall, London, 2001.
33. N. Golyandina and D. Stepanov, SSA-Based Approaches to Analysis and Forecast of Multidimensional Time Series, Proceedings of the Fifth Workshop on Simulation, St. Petersburg, Russia, 2005, pp. 293-296.
34. C. Granger and J.L. Lin, Using the mutual information coefficient to identify lags in nonlinear models, J. Time Ser. Anal. 15 (1994), pp. 371-384.
35. G. Granger, E. Maasoumi, and J. Racine, A dependence metric for possibly nonlinear processes, J. Time Ser. Anal. 25 (2004), pp. 649-669.
36. D. Harvey, S. Leybourne, and P. Newbold, Testing the equality of prediction mean squared errors, Int. J. Forecast. 13 (1997), pp. 281-291.
37. A.C. Harvey, C.R. McKenzie, D.P.C. Blake, and M.J. Desai, Irregular data revisions, in Applied Time Series Analysis of Economic Data, A. Zellner, ed., Economic Research Report ER-5, U.S. Department of Commerce,Washington, DC, 1983, pp. 329-347.
38. H. Hassani, Singular spectrum analysis: Methodology and comparison, J. Data Sci. 5 (2007), pp. 239-257.
39. H. Hassani, A. Dionisio, and M. Ghodsi, The effect of noise reduction in measuring the linear and nonlinear dependency of financial markets, J. Nonlinear Anal. RealWorld Appl. 11 (2009), pp. 392-402.
40. H. Hassani, S. Heravi, and A. Zhigljavsky, Forecasting European industrial production with singular spectrum analysis, Int. J. Forecast. 25 (2009), pp. 103-118.
41. H. Hassani and D. Thomakos, A review on singular spectrum analysis for economic and financial time series, Stat. Interface 3 (2010), pp. 377-397.
42. H. Hassani and A. Zhigljavsky, Singular spectrum analysis: Methodology and application to economics data, J. Syst. Sci. Complex. 22 (2008), pp. 372-394.
43. A. Hecq and G.J. Mazzi, Advanced revision analysis for economic time series and their role in improving forecasting accuracy, Paper presented to the 5th Eurostat Colloqium on Modern Tools for Business Cycle Analysis, Eurostat, Luxemburg, 2008.
44. S.M. Heravi, D.R. Osborn, and C.R. Birchenhall, Linear versus neural network forecasts for European industrial production series, Int. J. Forecast. 20 (2004), pp. 435-446.
45. E.P. Howrey, The use of preliminary data in economic forecasting, Rev. Econ. Stat. 60 (1978), pp. 193-200.
46. E.P. Howrey, Data revision, reconstruction and prediction: An application to inventory investment, Rev. Econ. Stat. 66 (1984), pp. 386-393.
47. J.P.A.M. Jacobs and S. van Norden, Modelling data revisions: Measurement error and dynamics of 'true' values, J. Econom. 161 (2011), pp. 101-109.
48. M. Kawagoe, How is economic growth rate revised afterwards? Real-time data analysis of real GDP growth rate, Q. J. Natl Acc. Japan (134) (2007), pp. 14-19.
49. T.A. Knetsch and H.E. Reimers, Dealing with benchmark revisions in real-time data: The case of German production and orders statistics, Oxford Bull. Econ. Stat. 71 (2009), pp. 209-235.
50. H. Lütkepohl, New Introduction to Time Series Analysis, Springer, Berlin, 2005.
51. N.G. Mankiw, M. Runkle, and M.D. Shapiro, Are preliminary announcements of the money stock rational forecasts? J. Monetary Econ. 14 (1984), pp. 15-27.
52. N.G. Mankiw and M.D. Shapiro, News or noise? An analysis of GNP revisions, Surv. Curr. Bus. May (1986), pp. 20-25.
53. R.S. Mariano and H. Tanizaki, Prediction of final data with use of preliminary and/or revised data, J. Forecast. 14 (1995), pp. 351-380.
54. R.A. Mork, Ain't behavin: Forecast errors and measurement errors in early GNP estimates, J. Bus. Econ. Stat. 5 (1987), pp. 165-175.
55. V.G. Moskvina and A. Zhigljavsky, An algorithm based on singular spectrum analysis for change-point detection, Commun. Stat. Simul. Comput. 32 (2003), pp. 319-352.
56. P. Newbold and C. Agiakloglou, Looking for evolving growth rates and cycles in British industrial production, 1700-1913, J. R. Statist. Soc. Ser. A 154 (1991), pp. 341-348.
57. W.D. Nordhaus, Forecasting efficiency: Concepts and applications, Rev. Econ. Stat. 4 (1987), pp. 667-674.
58. K.D. Patterson, An integrated model of the data measurement and data generation processes with an application to consumers' expenditure, Econ. J. 105 (1995), pp. 54-76.
59. K.D. Patterson, A state space approach to forecasting the final vintage of revised data with an application to the index of industrial production, J. Forecast. 14 (1995), pp. 337-350.
60. K.D. Patterson, Forecasting the final vintage of real personal disposable income: A state space approach, Int. J. Forecast. 11 (1995), pp. 395-405.
61. K.D. Patterson, Modelling the data measurement process for the index of production, J. R. Statist. Soc. Ser. A 165 (2002), pp. 279-296.
62. K.D. Patterson and S.M. Heravi, Efficient forecasts or measurement errors? Some evidence for revisions to United Kingdom GDP growth rates, Manchester School LX (1992), pp. 249-263.
63. K.D. Patterson and S.M. Heravi, Revisions to official data on U.S. GDP: A multivariate assessment of different vintages, J. Official Stat. 20 (2004), pp. 573-602.
64. K.D. Patterson and S.M. Heravi, Rebasing, common cycles, and some practical implications of modelling data revisions, J. Official Stat. 20(4) (2004), pp. 1-15.
65. L.E. Raffalovich, G.D. Deane, D. Armstrong, and H.-S. Tsao, Model selection procedures in social research: Monte- Carlo simulation results, J. Appl. Stat. 35 (2008), pp. 1093-1114.
66. C. Richardson, Revisions to GDP: A time profile, Econ. Trends November (2002), pp. 1-8.
67. C. Richardson, Revisions analysis: A time series approach, Econ. Trends December (2003), pp. 86-89.
68. S.S. Rivero, F.F. Rodriguez, and A. Felix, Testing chaotic dynamics via Lyapunov exponents, J. Appl. Econom. 20 (2005), pp. 911-930.
69. A.B. Saïda, Using the Lyapunov Exponent as a Practical Test for Noisy Chaos, 2007. Available at SSRN: http://ssrn.com/abstract=970074.
70. D.R. Salgado and F.J. Alonso, Tool wear detection in turning operations using singular spectrum analysis, J.Mater. Process. Technol. 171 (2006), pp. 451-458
71. L.R. Shenton and K.O. Bowman, A bivariate model for the distribution of sqrt(b1) and b2, J. Amer. Statist. Assoc. 72 (1977), pp. 206-211.
72. D.W. Shin and W.A. Fuller, Unit root tests based on unconditional maximum likelihood estimation for the for the autoregressive moving average, J. Time Ser. Anal. 19 (1998), pp. 591-599.
73. T. Stark and D. Croushore, Forecasting with a real-time data set for macroeconomists, J.Macroeconomics 24 (2002), pp. 507-531.