Skip to main content
SpringerLink
Log in

Structural Change and Monitoring Tests

  • Reference work entry
  • First Online:
Handbook of Financial Econometrics and Statistics

Abstract

This chapter focuses on various structural change and monitoring tests for a class of widely used time series models in economics and finance, including I(0), I(1), I(d) processes and the cointegration relationship. A structural break appears a change in endogenous relationships. This break could be caused by a shift in mean, variance, or a persistent change in the data property. In general, structural change tests can be categorized into two types: one is the classical approach to testing for structural change, which employs retrospective tests using a historical data set of a given length; the other one is the fluctuation-type test in a monitoring scheme, which means for given a history period for which a regression relationship is known to be stable, we then test whether incoming data are consistent with the previously established relationship. Several structural changes such as CUSUM squared tests, the QLR test, the prediction test, the multiple break test, bubble tests, cointegration breakdown tests, and the monitoring fluctuation test are discussed in this chapter, and we further illustrate all details and usefulness of these tests.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 849.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61, 821–856.

    Article  Google Scholar 

  • Andrews, D. W. K., & Kim, J. (2003). End-of-sample cointegration breakdown tests. Cowles Foundation Discussion Paper No. 1404.

    Google Scholar 

  • Bai, J., & Perron, P. (1996). Computation and analysis of multiple structural change models. Manuscript in Preparation, Universite de Montreal.

    Google Scholar 

  • Bai, J., & Perron, P. (1998). Estimation and testing linear models with multiple structural changes. Econometrica, 66, 47–78.

    Article  Google Scholar 

  • Beran, J. (1991). M-estimators of location for data with slowly decaying serial correlations. Biometrika, 76, 261–269.

    Article  Google Scholar 

  • Beran, J., & Terrin, N. (1996). Testing for a change of the long-memory parameter. Biometrika, 83, 627–638.

    Article  Google Scholar 

  • Campbell, J. Y., & Perron, P. (1991). Pitfalls and opportunities: What macroeconomists should know about unit roots. National Bureau of Economic Research Macroeconomics Annual, 6, 201–210.

    Google Scholar 

  • Cavaliere, G. (2004). Unit root tests under time-varying variances. Econometric Reviews, 23, 259–292.

    Article  Google Scholar 

  • Cavaliere, G., & Taylor, A. M. R. (2006). Testing for unit roots in time series models with non-stationary volatility. Journal of Econometrics, 140, 919–947.

    Article  Google Scholar 

  • Chan, N. H., & Tran, L. T. (1989). On the first order autoregressive process with infinite variance. Econometric Theory, 5, 354–362.

    Article  Google Scholar 

  • Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28, 591–605.

    Article  Google Scholar 

  • Chu, C. J., Stinchcombe, M., & White, H. (1996). Monitoring structural change. Econometrica, 64, 1045–1065.

    Article  Google Scholar 

  • Clements, M. P., & Hendry, D. F. (1998). Forecasting economic processes. International Journal of Forecasting, 14, 111–131.

    Google Scholar 

  • Clements, M. P., & Hendry, D. F. (1999). On winning forecasting competitions in economics. Spanish Economic Review, 1, 123–160.

    Google Scholar 

  • Davies, R. B. (1977). Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika, 64, 247–254.

    Article  Google Scholar 

  • Diba, B. T., & Grossman, H. I. (1988). Explosive rational bubbles in stock prices? The American Economic Review, 78, 520–530.

    Google Scholar 

  • Dickey, D. A., & 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.

    Google Scholar 

  • Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057–1072.

    Article  Google Scholar 

  • Evans, G. W. (1991). Pitfalls in testing for explosive bubbles in asset prices. American Economic Review, 81, 922–930.

    Google Scholar 

  • Granger, C. (1980). Long memory relationships and aggregation of dynamics models. Journal of Econometrics, 14, 227–238.

    Article  Google Scholar 

  • Granger, C. W. J., & Joyeux, R. (1980). An introduction to long-memory time series and fractional differencing. Journal of Time Series, 1, 15–29.

    Article  Google Scholar 

  • Hamori, S., & Tokihisa, A. (1997). Testing for a unit root in the presence of a variance shift. Economics Letters, 57, 245–253.

    Article  Google Scholar 

  • Hansen, B. E. (1992). Tests for parameter instability in regressions with I(1) processes. Journal of Business and Economic Statistics, 10, 321–336.

    Google Scholar 

  • Hidalgo, J., & Robinson, P. M. (1996). Testing for structural change in a long-memory environment. Journal of Econometrics, 70, 159–174.

    Article  Google Scholar 

  • Horvath, L., & Kokoszka, P. S. (2003). A bootstrap approximation to a unit root test statistic for heavy tailed observations. Statistics and Probability Letters, 62, 163–173.

    Article  Google Scholar 

  • Hsiao, C., & Robinson, P. M. (1978). Efficient estimation for a dynamic error-shock model. International Economic Review, 19, 467–480.

    Article  Google Scholar 

  • Jach, A., & Kokoszka, P. S. (2004). Subsampling unit root tests for heavy-tailed observations. Methodology and Computing in Applied Probability, 6, 73–97.

    Article  Google Scholar 

  • Kazakevicius, V., Leipus, R., & Viano, M. (2004). Stability of random coefficient ARCH models and aggregation schemes. Journal of Econometrics, 120, 139–158.

    Article  Google Scholar 

  • Kim, T. H., Leybourne, S., & Newbold, P. (2002). Unit root tests with a break in innovation variance. Journal of Econometrics, 109, 365–387.

    Article  Google Scholar 

  • Kindleberger, C. P. (2005). Manias, panics, and crashes: A history of financial crises. New Jersey: Wiley.

    Book  Google Scholar 

  • Kirman, A., & Teyssiere, G. (2005). Testing for bubbles and change-points. Journal of Economic Dynamic Control, 29, 765–799.

    Article  Google Scholar 

  • Kokoszka, P. S., & Parfionovas, A. (2004). Bootstrap unit root tests for heavy tailed time series. In S. T. Rachev (Ed.), Handbook of computational and numerical methods in finance (pp. 175–197). Boston: Birkhäuser.

    Chapter  Google Scholar 

  • Kuan, C., & Hsu, C. (1998). Change-point estimation of fractionally integrated processes. Journal of Time Series Analysis, 19, 693–708.

    Article  Google Scholar 

  • Leybourne, S., Taylor, R., & Kim, T. (2006). Cusum of squares-based tests for a change in persistence. Journal of Time Series Analysis, 28, 408–433.

    Article  Google Scholar 

  • Lutkepohl, H. (1988). Prediction tests for structural stability. Journal of Econometrics, 39, 267–296.

    Google Scholar 

  • Lutkepohl, H. (1989). Prediction tests for structural stability of multiple time series. Journal of Business Economic Statistics, 7, 129–135.

    Google Scholar 

  • Mandelbrot, B. B., & Vanness, J. W. (1968). Fractional Brownian motions, fractional noises and applications. SIAM Review, 10, 422–437.

    Article  Google Scholar 

  • Paparoditis, E., & Politis, D. (2003). Residual-based block bootstrap for unit root testing. Econometrica, 71, 813–855.

    Article  Google Scholar 

  • Perron, P., & Ng, S. (1996). Useful modifications to some unit root tests with dependent errors and their local asymptotic properties. Review of Economic Studies, 63, 435–463.

    Article  Google Scholar 

  • Phillips, P. C. B., & Hansen, B. E. (1990). Statistical inference in instrumental variables regression with I(1) processes. Review of Economic Studies, 57, 99–125.

    Google Scholar 

  • Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75, 335–346.

    Article  Google Scholar 

  • Phillips, P. C. B., Wu, Y., & Yu, J. (2009). Explosive behavior in the 1990s Nasdaq: When did exuberance escalate asset values?. Cowles Foundation Discussion Paper No.1699

    Google Scholar 

  • Phillips P. C. B., Shi, S. & Yu, J. (2011). Testing for multiple bubbles. Singapore Management University working paper.

    Google Scholar 

  • Quandt, R. E. (1960). Tests of the hypothesis that a linear regression system obeys two separate regimes. Journal of the American Statistical Association, 55, 324–330.

    Article  Google Scholar 

  • Quintos, C. E., & Phillips, P. C. B. (1993). Parameter constancy in cointegrating regressions. Empirical Economics, 18, 675–706.

    Article  Google Scholar 

  • Robinson, H., & Siegmund, D. (1970). Boundary crossing probabilities for the Wiener process and sample sums. The Annals of Mathematical Statistics, 41, 1410–1429.

    Article  Google Scholar 

  • Robinson, P. M., & Zaffaroni, P. (1998). Nonlinear time series with long memory: A model for stochastic volatility. Journal of Statistical Planning and Inference, 68, 359–371.

    Article  Google Scholar 

  • Segen, J., & Sanderson, A. (1980). Detection of changes in a time series. IEEE Transaction in Information Theory, 26, 249–257.

    Article  Google Scholar 

  • Shiller, R. J. (2000). Irrational exuberance. Princeton: Princeton University Press.

    Google Scholar 

  • Sibbertsen, P., & Kruse, R. (2009). Testing for a break in persistence under long-range dependencies. Journal of Time Series Analysis, 30, 268–285.

    Article  Google Scholar 

  • Stock, J. H. (1994). Unit roots, structural breaks and trends, Chapter 46. In Handbook of econometrics (pp. 2740–2841). Amsterdam: Elsevier

    Google Scholar 

  • Taylor, S. (1986). Modeling financial time series. New Jersey: Wiley.

    Google Scholar 

  • Welch, I., & Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. Review of Financial Studies, 21, 1455–1508.

    Article  Google Scholar 

  • Wu, G. & Xiao, Z. (2008). Are there speculative bubbles in stock markets? Evidence from an alternative approach. Statistics and its Interface, 1, 307–320.

    Google Scholar 

  • Xiao, Z., & Phillips, P. C. B. (2002). A CUSUM test for cointegration using regression residuals. Journal of Econometrics, 108, 43–61.

    Article  Google Scholar 

  • Yajima, Y. (1988). On estimation of a regression model with long-memory stationary errors. Annals of Statistics, 16, 791–807.

    Article  Google Scholar 

  • Zaffaroni, P. (2000). Contemporaneous aggregation of GARCH processes. Journal of Time Series Analysis, 28, 521–544.

    Google Scholar 

  • Zaffaroni, P. (2006). Aggregation and memory of models of changing volatility. Journal of Econometrics, 136, 237–249.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CORE, Université Catholique de Louvain and FUNDP, Academie Louvain, Louvain-la-Neuve, Belgium

    Cindy Shin-Huei Wang

  2. Department of Quantitative Finance, National TsingHwa University, Hsinchu City, Taiwan

    Cindy Shin-Huei Wang

  3. School of Business and Administration, Beijing Normal University, Beijing, China

    Yi Meng Xie

  4. Department of Economics, University of Southern California, Los Angeles, CA, USA

    Yi Meng Xie

Authors
  1. Cindy Shin-Huei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Meng Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cindy Shin-Huei Wang .

Editor information

Editors and Affiliations

  1. Department of Finance and Economics, Rutgers Business School, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA

    Cheng-Few Lee

  2. Center for PBBEF Research, North Brunswick, New Jersey, USA

    John C. Lee

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this entry

Cite this entry

Wang, C.SH., Xie, Y.M. (2015). Structural Change and Monitoring Tests. In: Lee, CF., Lee, J. (eds) Handbook of Financial Econometrics and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7750-1_31

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7750-1_31

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7749-5

  • Online ISBN: 978-1-4614-7750-1

  • eBook Packages: Business and Economics

Publish with us

Policies and ethics

Search

Navigation