Skip to main content
Log in

Methods of Analyzing the Nonstationary Time Series with Implicit Changes in Their Properties

  • Reviews
  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

The problems of analysis of the nonstationary time series with explicit and implicit changes in their properties were discussed. The main approaches and methods of analysis of the nonstationary processes were described. Consideration was given to the trend-stationary and difference-stationary processes. The algorithms to determine the process type hinge on verifying hypotheses like “initial process is a DS-process (TS-process)” against the alternative ones. The notions of false regression and cointegration were discussed. The problems arising at analysis of the varying-property processes and methods of their solution were reviewed. Consideration was given to the cases of explicit and implicit changes and algorithms of detection in the current and a posteriori modes.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

REFERENCES

  1. Kendall, M. and Stuart, A., The Advanced Theory of Statistics, vol. 3: Design and Analysis, and Time Series, London: Charles Griffin, 1973, 2nd ed. Translated under the title Mnogomerny statisticheskii analiz i vremennye ryady, Moscow: Nauka, 1976.

    Google Scholar 

  2. Nosko, V.P., Ekonometrika. Vvedenie v regressionnyi analiz vremennykh ryadov (Econometrics. Introduction to Regression Analysis of the Time Series), Moscow: Manuscript available at www.iet.ru, 2002.

  3. Kantorovich, G.G., Analysis of Time Series, Ekonom. Zh. VSHE, 2002, no. 2, pp. 251–273.

  4. Nelson, C.R. and Kang, H., Pitfalls in the Use of Time as an Explanatory Variable in Regression, J. Business & Economic Stat., 1984, vol. 2, pp. 73–81.

    Google Scholar 

  5. Chan, K.H, Hayya, J.C., and Ord, J.K., A Note on Trend Removal Methods: The Case of Polynomial Versus Variate Differencing, Econometrica, 1977, vol. 45, pp. 737–744.

    Google Scholar 

  6. Nelson, C.R. and Kang, H., Spurious Periodicity in Inappropriately Detrended Time Series, J. Monetary Econom., 1981, no. 10, pp. 139–162.

  7. Slutsky, E., The Summation of Random Causes as the Source of Cyclical Processes, Econometrica, 1937, vol. 4, pp. 105–146.

    Google Scholar 

  8. Stock, J.H., Unit Roots, Structural Breaks and Trends, in Handbook of Econometrics, 1994, vol. IV, pp. 2740–2841.

    Google Scholar 

  9. Mann, H.B. and Wald, A., On Stochastic Limit and Order Relationships, Ann. Math. Stat., 1943, vol. 14, pp. 217–277.

    MathSciNet  Google Scholar 

  10. White, J.S., The Limiting Distribution of the Serial Correlation Coefficient in the Explosive Case, Ann. Math. Stat., 1958, vol. 29, pp. 1188–1197.

    MATH  Google Scholar 

  11. Phillips, P.C.B., Time Series Regression with a Unit Root, Econometrica, 1987, vol. 55, pp. 277–301.

    MathSciNet  MATH  Google Scholar 

  12. Dickey, D.A., Estimation and Hypothesis Testing in Nonstationary Time Series, PhD Dissertation, Ames: Iowa State Univ., 1976.

    Google Scholar 

  13. Fuller, W.A., Introduction to Statistical Time Series, New York: Wiley, 1976.

    Google Scholar 

  14. Hamilton, J.D., Time Series Analysis, Princeton: Princeton Univ. Press, 1994.

    Google Scholar 

  15. Dickey, D.A. and Fuller, W.A., Distribution of the Estimators for Autoregressive Time Series with a Unit Root, J. Am. Stat. Ass., 1979, vol. 74, pp. 427–431.

    MathSciNet  Google Scholar 

  16. Dickey, D.A. and Fuller, W.A., Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root, Econometrica, 1981, vol. 49, pp. 1057–1072.

    MathSciNet  Google Scholar 

  17. Dolado, H., Jenkinson, T., and Sosvilla-Rivero, S., Cointegration and Unit Roots, J. Econom. Surveys, 1990, no. 4, pp. 243–273.

  18. Phillips, P.C.B. and Perron, P., Testing for a Unit Root in Time Series Regression, Biometrika, 1987, vol. 75, pp. 335–346.

    MathSciNet  Google Scholar 

  19. Newey, W. and West, K., A Simple Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix, Econometrica, 1987, vol. 55, pp. 703–708.

    MathSciNet  Google Scholar 

  20. MacKinnon, J.G., Critical Values for Cointegration Tests, in Longrun Economic Relationships: Readings in Cointegration, Ch. 13, Engle, R.F. and Granger, C.W.J., Eds., Oxford: Oxford Univ. Press, 1991.

    Google Scholar 

  21. MacKinnon, J.G., Approximate Asymptotic Distribution Functions for Unit-Root and Cointegration Tests, J. Business Econom. Stat., 1994, no. 12, pp. 167–176.

  22. Ericsson, N.R. and MacKinnon, J.G., Distributions of Error Correction Tests for Cointegration, Econom. J., 2002, no. 5, pp. 185–318.

  23. Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., and Shin, Y., Testing of the Null Hypothesis of Stationary against the Alternative of a Unit Root, J. Econom., 1992, vol. 54, pp. 159–178.

    Google Scholar 

  24. Elliott, G., Rothenberg, T.J., and Stock, J.H., Efficient Tests for an Autoregressive Unit Root, Econometrica, 1996, vol. 64, pp. 813–836.

    MathSciNet  Google Scholar 

  25. Granger, C.W.J., Developments in the Study of Cointegrated Variables, Oxford Bull. Econom. Stat., 1986, vol. 48, pp. 213–228.

    Google Scholar 

  26. Pollak, R., The Theory of the Cost-of-Living Index, Oxford: Oxford Univ. Press, 1989.

    Google Scholar 

  27. Straim, S.S., Survey of Literature of Demand for Money: Theoretical and Empirical Work with Special Reference to Error—Correction Models, IMF Working Papers/WP/99/64, Washington Int. Monetary Fund, 1999.

  28. Johansen, S. and Juselius, K., Maximum Likelihood Estimation and Inferences on Cointegration—with Applications to the Demand for Money, Oxford Bull. Econom. Stat., 1990, vol. 52, pp. 169–210.

    Google Scholar 

  29. Pelipas', I., Demand for Money and Inflation in Belarus, EKOVEST, 2001, no. 1, pp. 6–63.

  30. Granger, C.W.J. and Newbold, P., Spurious Regressions in Econometrics, J. Econom., 1974, vol. 2, pp. 111–120.

    Google Scholar 

  31. Phillips, P.C.B., Understanding Spurious Regressions in Econometrics, J. Econom., 1986, vol. 33, pp. 311–40.

    MATH  Google Scholar 

  32. Granger, C.W.J., Some Properties of Time Series Data and Their Use in Econometric Model Specication, J. Econom., 1981, vol. 16, pp. 121–130.

    Google Scholar 

  33. Granger, C.W.J., Co-Integrated Variables and Error-Correcting Models, UCSD Discussion Paper, 1983, pp. 83–113.

  34. Engle, R.F. and Granger, C.W.J., Co-integration and Error Correction: Representation, Estimation, and Testing, Econometrica, 1987, vol. 55, pp. 251–276.

    MathSciNet  Google Scholar 

  35. Johansen, S., Statistical Analysis of Cointegration Vectors, J. Econom. Dynam. Control, 1988, no. 12, pp. 231–254.

  36. Johansen, S., Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models, Econometrica, 1991, vol. 59, pp. 1551–1580.

    MathSciNet  MATH  Google Scholar 

  37. Phillips, P.C.B., Optimal Inference in Cointegrated Systems, Econometrica, 1991, vol. 59, pp. 283–306.

    MathSciNet  MATH  Google Scholar 

  38. Phillips, P.C.B. and Ouliaris, S., Testing for Cointegration Using Principal Components Methods, J. Econom. Dynam. Control, 1988, no. 12, pp. 205–230.

  39. Phillips, P.C.B. and Ouliaris, S., Asymptotic Properties of Residual based Tests for Cointegration, Econometrica, 1990, vol. 58, pp. 165–193.

    MathSciNet  Google Scholar 

  40. Phillips, P.C.B. and Durlauf, S.N., Multiple Time Series Regression with Integrated Processes, Rev. Econom. Studies, 1986, vol. 53, pp. 473–495.

    MathSciNet  Google Scholar 

  41. Sims, C.A., Macroeconomics and Reality, Econometrica, 1980, vol. 48, pp. 1–48.

    Google Scholar 

  42. Lutkepohl, H., Introduction to Multiple Time Series Analysis, Berlin: Springer, 1993.

    Google Scholar 

  43. Watson, M.W., Vector Avtoregression and Cointegration, in Handbook of Econometrics, Amsterdam: North-Holland, 1994, vol. 4, pp. 2844–2915.

    Google Scholar 

  44. Sargan, J.D., Wages and Prices in the United Kingdom: A Study in Econometric Methodology, in Econometric Analysis for National Economic Planning, Hart, P.E., Mills, G., and Whittaker, J.N., Eds., London: Butterworths, 1964.

    Google Scholar 

  45. Beveridge, S. and Nelson, C.R., A New Approach to Decomposition of Time Series in Permanent and Transitory Components with Particular Attention to Measurement of the ‘Business Cycle’, J. Monetary Econom., 1981, vol. 7, no.15, pp. 1–74.

    Google Scholar 

  46. Perron, P., The Great Crash, the Oil Price Shock and the Unit Root Hypothesis, Econometrica, 1989, vol. 57, pp. 1361–1401.

    MATH  Google Scholar 

  47. Maddala, G., The Effects of Different Types of Outliers on Unit Root Tests, Advance Econom., vol. 13, Greenwich: JAI Press, 1997.

    Google Scholar 

  48. Maddala, G. and Kim, I.M., Unit Roots, Cointegration and Structural Change, Cambridge: Cambridge Univ. Press, 1998.

    Google Scholar 

  49. Perron, P., Further Evidence on Breaking Trend Functions in Macroeconomic Variables, J. Econom., 1997, vol. 80, pp. 355–385.

    MathSciNet  MATH  Google Scholar 

  50. Gregory, A.W., Nason, J.M., and Watt, D., Testing for Structural Breaks in Cointegrated Relationships, J. Econom., 1994, pp. 491–504.

  51. Borovkov, A.A., Matematicheskaya statistika (Mathematical Statistics), Moscow: Nauka, 1984.

    Google Scholar 

  52. Wilks, S., Mathematical Statistics, New York: Wiley, 1962. Translated under the title Matematicheskaya statistika, Moscow: Nauka, 1967.

    Google Scholar 

  53. Chow, G.C., Tests of Equality between Sets of Coefficients in Two Linear Regressions, Econometrica, 1960, vol. 28, pp. 591–605.

    MathSciNet  MATH  Google Scholar 

  54. Chow, G.C., A Comparison of the Information and Posterior Probability Criteria for Model Selection, J. Econom., 1981, vol. 16, pp. 21–33.

    MATH  Google Scholar 

  55. Quandt, R.E., Tests of the Hypothesis that a Linear Regression System Obeys Two Separate Regimes, J. Am. Stat. Ass., 1960, vol. 55, pp. 324–330.

    MathSciNet  MATH  Google Scholar 

  56. Davies, R.B., Hypothesis Testing when a Nuisance Parameter is Present Only under the Alternative, Biometrika, 1977, vol. 64, pp. 247–254.

    MathSciNet  MATH  Google Scholar 

  57. Andrews, D.W.K., Tests for Parameter Instability and Structural Change with Unknown Change Point, Econometrica, 1993, vol. 61, pp. 821–856.

    MathSciNet  MATH  Google Scholar 

  58. Kim, H.J. and Siegmund, D., The Likelihood Ratio Test for a Change-Point in Simple Linear Regression, Biometrika, 1989, vol. 76, pp. 409–423.

    MathSciNet  Google Scholar 

  59. Hansen, B.E., Approximate Asymptotic p Values for Structural-Change Tests, J. Business & Econom. Stat., 1997, vol. 15, pp. 60–67.

    Google Scholar 

  60. Hansen, B.E., Tests for Parameter Instability in with Regression with I(1) Processes, J. Business Econom. Stat., 1992, no. 10, pp. 321–335.

  61. Andrews, D.W.K. and Ploberger, W., Optimal Tests when a Nuisance Parameter is Present Only under the Alternative, Econometrica, 1994, vol. 62, pp. 1383–1414.

    MathSciNet  Google Scholar 

  62. Bai, J., Least Squares Estimation of a Shift in Linear Processes, J. Time Series Anal., 1994, vol. 5, pp. 453–472.

    MATH  Google Scholar 

  63. Bai, J., Estimating Multiple Breaks One at a Time, Econom. Theory, 1997, no. 13, pp. 551–563.

  64. Bai, J., Estimation of a Change Point in Multiple Regression Models, Rev. Econom. Stat., 1997, vol. 79, pp. 551–563.

    Google Scholar 

  65. Bai, J. and Perron, P., Estimating and Testing Linear Models with Multiple Structural Changes, Econometrica, 1998, vol. 66, pp. 47–78.

    MathSciNet  Google Scholar 

  66. Chong, T.T.L., Partial Parameter Consistency in a Misspecified Structural Change Model, Econom. Lett., 1995, vol. 49, pp. 351–357.

    MathSciNet  MATH  Google Scholar 

  67. Hardle, W., Kleinow, T., and Stahl, G., Applied Quantitative Finance, New York: Springer, 2002.

    Google Scholar 

  68. Lepski, O., One Problem of Adaptive Estimation in Gaussian White Noise, Theory Probab. Appl., 1990, vol. 35, pp. 459–470.

    MathSciNet  Google Scholar 

  69. Lepski, O. and Spokoiny, V., Optimal Pointwise Adaptive Methods in Nonparametric Estimation, Ann. Stat., 1997, vol. 25, pp. 2512–2546.

    MathSciNet  Google Scholar 

  70. Liptser, R. and Spokoiny, V., Deviation Probability Bound for Martingales with Applications to Statistical Estimation, Stat. & Prob. Lett., 1999, vol. 46, pp. 347–357.

    MathSciNet  Google Scholar 

  71. Kuan, C.M. and Hornik, K., The Generalized Fluctuation Test: A Unifying View, Econom. Rev., 1995, vol. 14, pp. 135–161.

    MathSciNet  Google Scholar 

  72. Brown, R.L., Durbin, J., and Evans, J.M., Techniques for Testing the Constancy of Regression Relationships over Time with Comments, J. Royal Stat. Soc., 1975, series B, vol. 37, pp. 149–192.

    MathSciNet  Google Scholar 

  73. Ploberger, W. and Kramer, W., The CUSUM Test with OLS Residuals, Econometrica, 1992, vol. 60, pp. 271–286.

    MathSciNet  Google Scholar 

  74. Chu, C.S.J., Hornik, K., and Kuan, C.M., MOSUM Tests for Parameter Constancy, Biometrika, 1995, vol. 82, pp. 603–617.

    MathSciNet  Google Scholar 

  75. Ploberger, W., Kramer, W., and Kontrus, K., A New Test for Structural Stability in the Linear Regression Model, J. Econom., 1989, vol. 40, pp. 307–318.

    MathSciNet  Google Scholar 

  76. Chu, C.S.J., Hornik, K., and Kuan, C.M., The Moving-Estimates Test for Parameter Stability, Econom. Theory., 1995, no. 11, pp. 699–720.

  77. Billingsley, P., Weak Convergence of Probability Measures, New York: Wiley, 1968.

    Google Scholar 

  78. Karatzas, I. and Shreve, S.E., Brownian Motion and Stochastic Calculus, New York: Springer, 1991.

    Google Scholar 

  79. Inclan, C. and Tiao, G.C., Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance, J. Am. Stat. Ass., 1994, vol. 89, pp. 913–923.

    MathSciNet  Google Scholar 

  80. Kramer, W., Ploberger, W., and Alt, R., Testing for Structural Change in Dynamic Regression Models, Econometrica, 1988, vol. 56, pp. 1355–1369.

    MathSciNet  Google Scholar 

  81. Tran, K.C., Testing for Structural Change in the Dynamic Adjustment Model with Autoregressive Errors, Empirical Econom., 1999, vol. 24, pp. 61–76.

    Google Scholar 

  82. Chu, C.S.J., Stinchcombe, M., and White, H., Monitoring Structural Change, Econometrica, 1996, vol. 64, pp. 1045–1065.

    Google Scholar 

  83. Leisch, F., Hornik, K., and Kuan, C.M., Monitoring Structural Changes with the Generalized Fluctuation Test, Econometric Theory, 2000, vol. 16, pp. 835–854.

    Article  MathSciNet  Google Scholar 

  84. Wald, A., Posledovatel'nyi analiz (Successive Analysis), Moscow: Fizmatgiz, 1960.

    Google Scholar 

  85. Shiryaev, A.N., Statisticheskii posledovatel'nyi analiz. Optimal'nye pravila ostanovki (Successive Statistical Analysis. Optimal Stop Rules), Moscow: Nauka, 1976.

    Google Scholar 

  86. Page, E.S., Continuous Insrection Schemes, Biometrika, 1954, vol. 41, pp. 100–115.

    MathSciNet  MATH  Google Scholar 

  87. Lorden, G., Procedures for Reacting to a Change in Distribution, Ann. Math. Stat., 1971, vol. 42, pp. 1897–1908.

    MathSciNet  MATH  Google Scholar 

  88. Moustakides, G.V., Optimal Stopping Times for Detecting Changes in Distributions, Ann. Stat., 1986, vol. 14, pp. 1379–1387.

    MathSciNet  MATH  Google Scholar 

  89. Ritov, Y., Decision Theoretic Optimality of the CUSUM Procedure, Ann. Satist., 1990, vol. 18, pp. 1464–1469.

    MathSciNet  MATH  Google Scholar 

  90. Grebenyuk, E.A., Monitoring of Nonstationary Processes: Analysis and Study of Changes in the Staitonarity Properties, Probl. Upravlen., 2004, no. 3, pp. 15–20.

  91. Grebenyuk, E.A., Detection of Changes in the Properties of Nonstationary Random Processes, Avtom. Telemekh., 2003, no. 12, pp. 44–59.

  92. Grebenyuk, E.A., Analysis and On-line Diagnosis of Systems Described by Nonstationary Random Processes, Probl. Upravlen., 2003, no. 4, pp. 23–29.

  93. Perron, P. and Vogelsang, T., Nonstationarity and Level Shifts with an Application to Purchasing Power Parity, J. Business Econom. Stat., 1992, no. 10, pp. 301–320.

  94. Zivot, E. and Andrews, D.W.K., Further Evidence on the Great Crash, the Oil Price Shock and the Unit Root Hypothesis, J. Business Econom. Stat., 1992, no. 10, pp. 251–270.

  95. Perron, P., Further Evidence on Breaking Trend Functions in Macroeconomic Variables, J. Econom., 1997, vol. 80, pp. 355–385.

    MathSciNet  MATH  Google Scholar 

  96. Nelson, C.R. and Plosser, C.I., Trends and Random Walks in Macroeconomic Time Series, J. Monetary Econom., 1982, no. 10, pp. 139–162.

  97. Gregory, A.W. and Hansen, B.E., Residual-based Tests for Cointegration in Models with Regime Shifts, J. Econom., 1996, vol. 70, pp. 99–126.

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

__________

Translated from Avtomatika i Telemekhanika, No. 12, 2005, pp. 3–30.

Original Russian Text Copyright © 2005 by Grebenyuk.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Grebenyuk, E.A. Methods of Analyzing the Nonstationary Time Series with Implicit Changes in Their Properties. Autom Remote Control 66, 1871–1896 (2005). https://doi.org/10.1007/s10513-005-0221-z

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10513-005-0221-z

Keywords

Navigation