Business cycles and turning points: A survey of statistical techniques

National Institute Economic Review

Volumn 183, Issue 1, 2003, Pages 90-106

  Massmann, Michael ^a   Mitchell, James ^a   Weale, Martin ^b  

^a National Institute of Economic and Social Research ^*

^b NATIONAL INSTITUTE OF ECONOMIC AND SOCIAL RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84992785667     PISSN: 00279501     EISSN: None     Source Type: Journal    
DOI: 10.1177/0027950103183001465     Document Type: Article

References (59)

1. Artis, M.J., Marcellino, M. and Proietti, T. (2003), ‘Dating the Euro Area business cycle’, CEPR Discussion Paper no. 3696.
2. Baxter, M. and King, R.G. (1999), ‘Measuring business cycles: approximate band-pass filters for economic time series’, Review of Economics and Statistics, 81, 4, pp. 575-593.
3. Bell, W. (1984), ‘Signal extraction for nonstationary time series’, The Annals of Statistics, 12, pp. 646-664.
4. Benati, L. (2001), ‘Band pass filtering, cointegration and business cycle analysis’, Bank of England Working Paper no. 142.
5. Beveridge, S. and Nelson, C.R. (1981), ‘A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the ‘business cycle’, Journal of Monetary Economics, 7, 2, pp. 151-174.
6. Boone, L. (2000), ‘Comparing semi-structural methods to estimate unobserved variables: the HPMV and Kalman filters approaches’, OECD Economics Department Working Papers no. 240.
7. Bry, G. and Boschan, C. (1971), ‘Interpretation and analysis of time-series scatters’, The American Statistician, 25, 2, pp. 29-33.
8. Canova, F. (1998a), ‘Detrending and business cycle facts’, Journal of Monetary Economics, 41, 3, pp. 475-512.
9. Canova, F. (1998b), ‘Detrending and business cycle facts: a user’s guide’, Journal of Monetary Economics, 41, 3, pp. 533-540.
10. Christiano, L.J. and Fitzgerald, T.J. (1999), ‘The band pass filter’, NBER Working Paper no. 7257.
11. Clements, M.P. and Krolzig, H.-M. (2001), ‘Can regime switching models reproduce the business cycle: features of US aggregate consumption, investment and output?’ mimeo, University of Warwick and University of Oxford.
12. Cogley, T. and Nason, J.M. (1995), ‘Effects of the Hodrick-Prescott filter on trend and difference stationary time series: implications for business cycle research’, Journal of Economic Dynamics and Control, 19, 1-2, pp. 253-278.
13. Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977), ‘Maximum likelihood from incomplete data via the EM algorithm’, Journal of the Royal Statistical Society, Series B., Methodological, 39, pp. 1-37 (with comments and reply).
14. Doornik, J.A. and Hendry, D.F. (1997), Modelling Dynamic Systems using PcFiml 9.0, Thomson Business Press.
15. Durbin, J. and Koopman, S.J. (2000), ‘Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives’, Journal of the Royal Statistical Society, Series B, 62, 1, pp. 3-56 (with discussion).
16. Findley, D., Monsell, B.H., Bell, W., Otto, M. and Chen, B.-C. (1998), ‘New capabilities and methods of the X-12-ARIMA seasonal adjustment program’, Journal of Business and Economic Statistics, pp. 127-152.
17. Gomez, V. (1999), ‘Three equivalent methods for filtering finite nonstationary time series’, Journal of Business and Economic Statistics, 17, 1, pp. 109-116.
18. Hamilton, J.D. (1989), ‘A new approach to the analysis of non-stationary time series and the business cycle’, Econometrica, 57, pp. 357-384.
19. Hamilton, J.D. (1990), ‘Analysis of time series subject to changes in regime’, Journal of Econometrics, 45, pp. 39-70.
20. Hamilton, J.D. (1994), Time Series Analysis, Princeton University Press.
21. Harding, D. and Pagan, A. (2000), ‘Knowing the cycle’, in Backhouse, R. and Salanti, A. (eds), Macroeconomics and the Real World, Oxford, Oxford University Press.
22. Harding, D. and Pagan, A. (2001), ‘Extracting, analysing and using cyclical information’, mimeo, University of Melbourne.
23. Harding, D. and Pagan, A. (2002), ‘Dissecting the cycle: a methodological investigation’, Journal of Monetary Economics, 49, pp. 365-381.
24. Harding, D. and Pagan, A. (2003), ‘A comparison of two business cycle dating methods’, Journal of Economic Dynamics and Control (forthcoming).
25. Harvey, A.C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge, Cambridge University Press.
26. Harvey, A.C. (1993), Time Series Models, 2nd edition, Harvester-Wheatsheaf.
27. Harvey, A.C. and Jaeger, A. (1993), ‘Detrending, stylized facts and the business cycle’, Journal of Applied Econometrics, 8, pp. 231-247.
28. Harvey, A.C. and Koopman, S.J. (2000), ‘Signal extraction and the formulation of unobserved components models’, Econometrics Journal, 3, 1, pp. 84-107.
29. Harvey, A.C. and Trimbur, T. (2002), ‘General model based filters for extracting trends and cycles in economic time series’, Review of Economics and Statistics (forthcoming).
30. Hodrick, R.J. and Prescott, E. (1980), ‘Postwar US business cycles: an empirical investigation’, Discussion paper no. 451, Carnegie-Mellon University.
31. Kaiser, R. and Maravall, A. (2001), Measuring Business Cycles in Economic Time Series, Springer-Verlag Lecture Notes in Statistics 154.
32. Kalman, R.E. (1960), ‘A new approach to linear filtering and prediction problems’, Transactions AMSE Journal of Basic Engineering D, 82, pp. 35-45.
33. Kalman, R.E. and Bucy, R. (1961), ‘New results in linear filtering and prediction theory’, Transactions AMSE Journal of Basic Engineering D, pp. 95-108.
34. Kenny, P. and Durbin, J. (1982), ‘Local trend estimation and seasonal adjustment of economic and social time series’, Journal of the Royal Statistical Society, A, pp. 1-41.
35. Kim, C.J. (1994), ‘Dynamic linear models with markov-switching’, Journal of Econometerics, 60, 1-2, pp. 1-22.
36. Kim, C.J. and Nelson, C.R. (1999), State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications, Cambridge, Mass., MIT Press.
37. King, R.G. and Rebelo, S.T. (1993), ‘Low frequency filtering and real business cycles’, Journal of Economic Dynamics and Control, 17, pp. 207-232.
38. Kolmogorov, A. (1939), ‘Sur l’interpolation et l’extrapolation des suites stationnaires’, C.R. Acad. Sci., Paris, 208, pp. 2043-2045.
39. Kolmogorov, A. (1941), ‘Stationary sequences in Hilbert space’, Bulletin Math. University of Moscow, 2, pp. 1-40.
40. Koopman, S.J., Harvey, A.C., Doornik, J.A. and Shephard, N. (1999), Stamp 6.0, Timberlake Consultants.
41. Koopman, S.J., Shephard, N. and Doornik, J.A. (1999), ‘Statistical algorithms for models in state space using SsfPack 2.2’, Econometrics Journal, 2, 1, pp. 107-160.
42. Krolzig, H.-M. (1998), ‘Econometric modelling of Markov-switching vector autoregressions using MSVAR for Ox’, mimeo, University of Oxford.
43. Lam, P.S. (1990), ‘The Hamilton model with a general autoregressive component: estimation and comparison with other models of economic time series’, Journal of Monetary Economics, 26, 3, pp. 409-432.
44. Laxton, D. and Tetlow, R. (1992), ‘A simple multivariate filter for the measurement of potential output’, Technical Report no. 59, Bank of Canada.
45. Massmann, M. and Mitchell, J. (2002), ‘Have UK and Eurozone business cycles become more correlated?’, National Institute Economic Review, 182, pp. 58-71.
46. Mitchell, J. and Mouratidis, K. (2002), ‘Is there a common Eurozone business cycle?’, paper presented at Colloquium on ‘Modern Tools for Business Cycle Analysis’, Eurostat, Luxembourg, 28/9 November.
47. Morley, J., Nelson, C.R. and Zivot, E. (2002), ‘Why are Beveridge-Nelson and unobserved component decompositions of GDP so different?’, Review of Economics and Statistics (forthcoming).
48. Murray, C.J. (2001), ‘Cyclical properties of Baxter-King filtered time series’, mimeo, University of Houston.
49. Osborn, D.R. (1995), ‘Moving average detrending and the analysis of business cycles’, Oxford Bulletin of Economics and Statistics, 57, pp. 547-558.
50. Pagan, A. (1980), ‘Some identifications and estimation results for regression models with stochastically varying coefficients’, Journal of Econometrics, 13, pp. 341-363.
51. Pedersen, T.M. (2001), ‘The Hodrick-Prescott filter, the Slutzky effect and the distortionary effect of filters’, Journal of Economic Dynamics and Control, 25, pp. 1081-1101.
52. Proietti, T. and Harvey, A. (2000), ‘A Beveridge-Nelson smoother’, Economics Letters, 67, 2, pp. 139-146.
53. Slutsky, E. (1937), ‘The summation of random causes as the source of cyclical processes’, Econometrica, 5, pp. 105-146.
54. Treasury Committee (2002), The 2002 Pre-Budget Report, HC 159 (http://www.publications.parliament.uk/pa/cm200203/cmselect/cmtreasy/159/159.pdf.
55. Watson, M.W. (1986), ‘Univariate detrending methods with stochastic trends’, Journal of Monetary Economics, 18, 1, pp. 49-75.
56. Whittle, P. (1963), Prediction and Regulation by Linear Least-Square Methods, 2nd edn 1983, Oxford, Basic Blackwell.
57. Wiener, N. (1949), Extrapolation, Interpolation and Smoothing of Stationary Time Series, Chichester, Wiley.
58. Wold, H. (1938), A Study in the Analysis of Stationary Time Series, 2nd edn, 1954, Almqvist and Wicksell.
59. Yule, G.U. (1921), ‘On the time-correlation problem’, Journal of the Royal Statistical Society, pp. 497-526.