Hostname: page-component-89b8bd64d-dvtzq Total loading time: 0 Render date: 2026-05-10T09:20:20.307Z Has data issue: false hasContentIssue false
  • English
  • Français

Business Cycles and Turning Points: A Survey of Statistical Techniques

Published online by Cambridge University Press:  26 March 2020

Michael Massmann ,
James Mitchell  and
Martin Weale
Michael Massmann
Affiliation:
National Institute of Economic and Social Research
James Mitchell*
Affiliation:
National Institute of Economic and Social Research
Martin Weale*
Affiliation:
National Institute of Economic and Social Research
*
e-mail: j.mitchell@niesr.ac.uk or mweale@niesr.ac.uk
e-mail: j.mitchell@niesr.ac.uk or mweale@niesr.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

The business cycle has an importance in the popular debate which can tend to run ahead of the problems in measuring it. This paper provides a survey of the main statistical techniques that are used to measure the cycle. An application to the UK illustrates that the choice of what measure, or measures, to use is more than a dry academic issue. Inference about the business cycle is potentially sensitive to measurement. Fortunately, however, there is an element of consensus.

Information

Type
Articles
Information
National Institute Economic Review , Volume 183 , January 2003 , pp. 90 - 106
Copyright
Copyright © 2003 National Institute of Economic and Social Research

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

∗

The authors are grateful to EUROSTAT for supporting this work and for giving permission to publish the paper.

References

Artis, M.J., Marcellino, M. and Proietti, T. (2003), ‘Dating the Euro Area business cycle’, CEPR Discussion Paper no. 3696.Google Scholar
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.CrossRefGoogle Scholar
Bell, W. (1984), ‘Signal extraction for nonstationary time series’, The Annals of Statistics, 12, pp. 646–664.CrossRefGoogle Scholar
Benati, L. (2001), ‘Band pass filtering, cointegration and business cycle analysis’, Bank of England Working Paper no. 142.Google Scholar
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.CrossRefGoogle Scholar
Boone, L. (2000), ‘Comparing semi-structural methods to estimate unobserved variables: the HPMV and Kalman filters approaches’, OECD Economics Department Working Papers no. 240.Google Scholar
Bry, G. and Boschan, C. (1971), ‘Interpretation and analysis of time-series scatters’, The American Statistician, 25, 2, pp. 29–33.Google Scholar
Canova, F. (1998a), ‘Detrending and business cycle facts’, Journal of Monetary Economics, 41, 3, pp. 475–512.CrossRefGoogle Scholar
Canova, F. (1998b), ‘Detrending and business cycle facts: a user's guide’, Journal of Monetary Economics, 41, 3, pp. 533–540.CrossRefGoogle Scholar
Christiano, L.J. and Fitzgerald, T.J. (1999), ‘The band pass filter’, NBER Working Paper no. 7257.Google Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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).Google Scholar
Doornik, J.A. and Hendry, D.F. (1997), Modelling Dynamic Systems using PcFiml 9.0, Thomson Business Press.Google Scholar
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).CrossRefGoogle Scholar
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.Google Scholar
Gomez, V. (1999), ‘Three equivalent methods for filtering finite nonstationary time series’, Journal of Business and Economic Statistics, 17, 1, pp. 109–116.Google Scholar
Hamilton, J.D. (1989), ‘A new approach to the analysis of non-stationary time series and the business cycle’, Econometrica, 57, pp. 357–384.CrossRefGoogle Scholar
Hamilton, J.D. (1990), ‘Analysis of time series subject to changes in regime’, Journal of Econometrics, 45, pp. 39–70.CrossRefGoogle Scholar
Hamilton, J.D. (1994), Time Series Analysis, Princeton University Press.Google Scholar
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.Google Scholar
Harding, D. and Pagan, A. (2001), ‘Extracting, analysing and using cyclical information’, mimeo, University of Melbourne.Google Scholar
Harding, D. and Pagan, A. (2002), ‘Dissecting the cycle: a methodological investigation’, Journal of Monetary Economics, 49, pp. 365–381.CrossRefGoogle Scholar
Harding, D. and Pagan, A. (2003), ‘A comparison of two business cycle dating methods’, Journal of Economic Dynamics and Control (forthcoming).CrossRefGoogle Scholar
Harvey, A.C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge, Cambridge University Press.Google Scholar
Harvey, A.C. (1993), Time Series Models, 2nd edition, Harvester-Wheatsheaf.Google Scholar
Harvey, A.C. and Jaeger, A. (1993), ‘Detrending, stylized facts and the business cycle’, Journal of Applied Econometrics, 8, pp. 231–247.CrossRefGoogle Scholar
Harvey, A.C. and Koopman, S.J. (2000), ‘Signal extraction and the formulation of unobserved components models’, Econometrics Journal, 3, 1, pp. 84–107.Google Scholar
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).Google Scholar
Hodrick, R.J. and Prescott, E. (1980), ‘Postwar US business cycles: an empirical investigation’, Discussion paper no. 451, Carnegie-Mellon University.Google Scholar
Kaiser, R. and Maravall, A. (2001), Measuring Business Cycles in Economic Time Series, Springer-Verlag Lecture Notes in Statistics 154.Google Scholar
Kalman, R.E. (1960), ‘A new approach to linear filtering and prediction problems’, Transactions AMSE Journal of Basic Engineering D, 82, pp. 35–45.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
Kim, C.J. (1994), ‘Dynamic linear models with markov-switching’, Journal of Econometerics, 60, 1-2, pp. 1–22.CrossRefGoogle Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
Kolmogorov, A. (1939), ‘Sur l'interpolation et l'extrapolation des suites stationnaires’, C.R. Acad. Sci., Paris, 208, pp. 2043–2045.Google Scholar
Kolmogorov, A. (1941), ‘Stationary sequences in Hilbert space’, Bulletin Math. University of Moscow, 2, pp. 1–40.Google Scholar
Koopman, S.J., Harvey, A.C., Doornik, J.A. and Shephard, N. (1999), Stamp 6.0, Timberlake Consultants.Google Scholar
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.CrossRefGoogle Scholar
Krolzig, H.-M. (1998), ‘Econometric modelling of Markov-switching vector autoregressions using MSVAR for Ox’, mimeo, University of Oxford.Google Scholar
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.CrossRefGoogle Scholar
Laxton, D. and Tetlow, R. (1992), ‘A simple multivariate filter for the measurement of potential output’, Technical Report no. 59, Bank of Canada.Google Scholar
Massmann, M. and Mitchell, J. (2002), ‘Have UK and Eurozone business cycles become more correlated?’, National Institute Economic Review, 182, pp. 58–71.CrossRefGoogle Scholar
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.Google Scholar
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).CrossRefGoogle Scholar
Murray, C.J. (2001), ‘Cyclical properties of Baxter-King filtered time series’, mimeo, University of Houston.Google Scholar
Osborn, D.R. (1995), ‘Moving average detrending and the analysis of business cycles’, Oxford Bulletin of Economics and Statistics, 57, pp. 547–558.CrossRefGoogle Scholar
Pagan, A. (1980), ‘Some identifications and estimation results for regression models with stochastically varying coefficients’, Journal of Econometrics, 13, pp. 341–363.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Proietti, T. and Harvey, A. (2000), ‘A Beveridge-Nelson smoother’, Economics Letters, 67, 2, pp. 139–146.CrossRefGoogle Scholar
Slutsky, E. (1937), ‘The summation of random causes as the source of cyclical processes’, Econometrica, 5, pp. 105–146.CrossRefGoogle Scholar
Treasury Committee (2002), The 2002 Pre-Budget Report, HC 159 (http://www.publications.parliament.uk/pa/cm200203/cmselect/cmtreasy/159/159.pdf.Google Scholar
Watson, M.W. (1986), ‘Univariate detrending methods with stochastic trends’, Journal of Monetary Economics, 18, 1, pp. 49–75.Google Scholar
Whittle, P. (1963), Prediction and Regulation by Linear Least-Square Methods, 2nd edn 1983, Oxford, Basic Blackwell.Google Scholar
Wiener, N. (1949), Extrapolation, Interpolation and Smoothing of Stationary Time Series, Chichester, Wiley.CrossRefGoogle Scholar
Wold, H. (1938), A Study in the Analysis of Stationary Time Series, 2nd edn, 1954, Almqvist and Wicksell.Google Scholar
Yule, G.U. (1921), ‘On the time-correlation problem’, Journal of the Royal Statistical Society, pp. 497–526.Google Scholar