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CUMULATED SUM OF SQUARES STATISTICS FOR NONLINEAR AND NONSTATIONARY REGRESSIONS

Published online by Cambridge University Press:  22 February 2019

Vanessa Berenguer-Rico
Affiliation:
University of Oxford
Bent Nielsen*
Affiliation:
University of Oxford
*
*Address correspondence to Bent Nielsen, Nuffield College, Oxford, OX1 1NF, UK; e-mail: bent.nielsen@nuffield.ox.ac.uk.

Abstract

We show that the cumulated sum of squares statistic has a standard Brownian bridge–type asymptotic distribution in nonlinear regression models with (possibly) nonstationary regressors. This contrasts with cumulated sum statistics which have been previously studied and whose asymptotic distribution has been shown to depend on the functional form and the stochastic properties, such as persistence and stationarity, of the regressors. A recursive version of the test is also considered. A local power analysis is provided, and through simulations, we show that the test has good size and power properties across a variety of situations.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2019 

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Footnotes

Comments from Giuseppe Cavaliere and the referees are gratefully acknowledged.

References

REFERENCES

Berkes, I. & Horváth, L. (2006) Convergence of integral functionals of stochastic processes. Econometric Theory 22, 304322.CrossRefGoogle Scholar
Billingsley, P. (1999) Convergence of Probability Measures. 2nd edition. Wiley.CrossRefGoogle Scholar
Brown, B.M. (1971) Martingale central limit theorems. Annals of Mathematical Statistics 42, 5966.CrossRefGoogle Scholar
Brown, R.L., Durbin, J., & Evans, J.M. (1975) Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society B37, 149192.Google Scholar
Chan, N. & Wang, Q. (2015) Nonlinear regressions with nonstationary time series. Journal of Econometrics 185, 182195.CrossRefGoogle Scholar
Chan, N.H. & Wei, C.Z. (1988) Limiting distributions of least squares estimates of unstable autoregressive processes. Annals of Statistics 16, 367401.CrossRefGoogle Scholar
Choi, I. & Saikkonen, P. (2010) Tests for non-linear cointegration. Econometric Theory 26, 682709.CrossRefGoogle Scholar
Chow, Y.S. (1965) Local convergence of martingales and the law of large numbers. Annals of Mathematical Statistics 36, 552558.CrossRefGoogle Scholar
Christopeit, N. (2009) Weak convergence of nonlinear transformations of integrated processes: The multivariate case. Econometric Theory 25, 11801207.CrossRefGoogle Scholar
Davidson, J. (1994) Stochastic Limit Theory. Oxford University Press.CrossRefGoogle Scholar
de Jong, R.M. (2004) Addendum to asymptotics for nonlinear transformations of integrated time series. Econometric Theory 20, 627635.CrossRefGoogle Scholar
de Jong, R.M. & Wang, C.H. (2005) Further results on the asymptotics for nonlinear transformations of integrated time series. Econometric Theory 21, 413430.CrossRefGoogle Scholar
de Jong, R.M. & Hu, L. (2011) A note on nonlinear models with integrated regressors and convergence order results. Economic Letters 111, 2325.CrossRefGoogle Scholar
Deng, A. & Perron, P. (2008a) A non-local perspective on the power properties of the CUSUM and CUSUM of squares tests for structural change. Journal of Econometrics 142, 212240.CrossRefGoogle Scholar
Deng, A. & Perron, P. (2008b) The limit distribution of the CUSUM of squares test under general mixing conditions. Econometric Theory 24, 809822.CrossRefGoogle 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, 427431.Google Scholar
Donati-Martin, C. & Yor, M. (1991) Fubini’s theorem for double Wiener integrals and the variance of the Brownian path. Annales de l’Institut Henri Poincaré 27, 181200.Google Scholar
Edgerton, D. & Wells, C. (1994) Critical values for the CUSUMSQ statistic in medium and large sized samples. Oxford Bulletin of Economics and Statistics 56, 355365.CrossRefGoogle Scholar
Hammersley, J.M. (1956) The zeros of a random polynomial. Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability 2, 89111.Google Scholar
Jakubowski, A., Ménin, J., & Pages, G. (1989) Convergence en loi des suites d’intégrales stochastiques sur l’espace D1 de Skorokhod. Probability Theory and Related Fields 81, 111137.CrossRefGoogle Scholar
Jennrich, R.I. (1969) Asymptotic properties of non-linear least squares estimators. Annals of Mathematical Statistics 40, 633643.CrossRefGoogle Scholar
Karlsen, H.A., Myklebust, T., & Tjøstheim, D. (2007) Nonparametric estimation in a non-linear cointegration type model. Annals of Statistics 35, 252299.CrossRefGoogle Scholar
Kasparis, I. (2008) Detection of functional form misspecification in cointegrating relations. Econometric Theory 24, 13731403.CrossRefGoogle Scholar
Kasparis, I. (2011) Functional form misspecification in regressions with a unit root. Econometric Theory 27, 285311.CrossRefGoogle Scholar
Kasparis, I., Andreou, E., & Phillips, P.C.B. (2015) Nonparametric predictive regression. Journal of Econometrics 185, 468494.CrossRefGoogle Scholar
Kristensen, D. & Rahbek, A. (2010) Likelihood-based inference for cointegration with nonlinear error-correction. Journal of Econometrics 158, 7894.CrossRefGoogle Scholar
Kurtz, T.G. & Protter, P. (1991) Weak limit theorems for stochastic integrals and stochastic differential equations. Annals of Probability 19, 10351070.CrossRefGoogle Scholar
Lai, T.L. & Wei, C.Z. (1982) Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems. Annals of Statistics 10, 154166.CrossRefGoogle Scholar
Lai, T.L. & Wei, C.Z. (1985) Asymptotic properties of multivariate weighted sums with applications to stochastic regression in linear dynamic systems. In Krishnaiah, P.R. (ed.) Multivariate Analysis VI, Elsevier, pages 375393.Google Scholar
Lee, S., Na, O., & Na, S. (2003) On the CUSUM of squares test for variance change in non-stationary and non-parametric time series models. Annals of the Institute of Statistical Mathematics 55, 467485.CrossRefGoogle Scholar
McCabe, B.P.M. & Harrison, M.J. (1980) Testing the constancy of regression relationships over time using least squares residuals. Applied Statistics 29, 142148.CrossRefGoogle Scholar
Nielsen, B. (2005) Strong consistency results for least squares estimators in general vector autoregressions with deterministic terms. Econometric Theory 21, 534561.CrossRefGoogle Scholar
Nielsen, B. & Sohkanen, J. (2011) Asymptotic behaviour of the CUSUM of squares test under stochastic and deterministic time trends. Econometric Theory 27, 913927.CrossRefGoogle Scholar
Park, J.Y. & Phillips, P.C.B. (1999) Asymptotics for nonlinear transformations of integrated time series. Econometric Theory 15, 269298.CrossRefGoogle Scholar
Park, J.Y. & Phillips, P.C.B. (2001) Non-linear regressions with integrated time series. Econometrica 69, 117161.CrossRefGoogle Scholar
Phillips, P.C.B. (2007) Regression with slowly varying regressors and nonlinear trends. Econometric Theory 23, 557614.CrossRefGoogle Scholar
Phillips, P.C.B. & Solo, V. (1992) Asymptotics for linear processes. Annals of Statistics 20, 9711001.CrossRefGoogle Scholar
Pitarakis, J.Y. (2017) A simple approach for diagnosing instabilities in predictive regressions. Oxford Bulletin of Economics and Statistics 79, 851874.CrossRefGoogle Scholar
Ploberger, W. & Krämer, W. (1986) On studentizing a test for structural change. Economics Letters 20, 341344.CrossRefGoogle Scholar
Ploberger, W. & Krämer, W. (1990) The local power of the CUSUM and CUSUM of squares test. Econometric Theory 6, 335347.CrossRefGoogle Scholar
Pötscher, B.M. (2004) Nonlinear functions and convergence to Brownian motion: Beyond the continuous mapping theorem. Econometric Theory 20, 122.CrossRefGoogle Scholar
Smirnov, N. (1948) Tables for estimating the goodness of fit of empirical distributions. Annals of Mathematical Statistics 19, 279281.CrossRefGoogle Scholar
Turner, P. (2010) Power properties of the CUSUM and CUSUMSQ tests for parameter instability. Applied Economic Letters 17, 10491053.CrossRefGoogle Scholar
Wang, Q. (2015) Limit Theorems for Nonlinear Cointegration Regression. World Scientific.CrossRefGoogle Scholar
Wang, Q. & Phillips, P.C.B. (2009) Structural nonparametric cointegrating regression. Econometrica 77, 19011948.Google Scholar
Wang, Q. & Phillips, P.C.B. (2012) A specification test for nonlinear nonstationary models. Annals of Statistics 40, 727758.CrossRefGoogle Scholar
Wu, C.F. (1981) Asymptotic theory of nonlinear least squares estimation. Annals of Statistics 9, 501513.CrossRefGoogle Scholar
Xiao, Z. & Phillips, P.C.B. (2002) A CUSUM test for cointegration using regression residuals. Journal of Econometrics 108, 4361.CrossRefGoogle Scholar