Hostname: page-component-8448b6f56d-c47g7 Total loading time: 0 Render date: 2024-04-19T21:32:30.995Z Has data issue: false hasContentIssue false

BOOTSTRAP ASSISTED SPECIFICATION TESTS FOR THE ARFIMA MODEL

Published online by Cambridge University Press:  14 April 2011

Miguel A. Delgado
Affiliation:
Universidad Carlos III
Javier Hidalgo*
Affiliation:
London School of Economics
Carlos Velasco
Affiliation:
Universidad Carlos III
*
*Address correspondence to Javier Hidalgo, Economics Department, London School of Economics, London, United Kingdom; e-mail: F.j.hidalgo@lse.ac.uk.

Abstract

This paper proposes bootstrap assisted specification tests for the autoregressive fractionally integrated moving average model based on the Bartlett Tp-process with estimated parameters whose limiting distribution under the null depends on the estimated model and the estimation method employed. The computation of the asymptotic critical values is not easy if at all possible under these circumstances. To circumvent this problem Delgado, Hidalgo, and Velasco (2005, Annals of Statistics 33, 2568–2609) proposed an asymptotically pivotal transformation of the Tp-process with estimated parameters. The aim of this paper is twofold. First, to examine alternative methods based on bootstrap algorithms for estimating the distribution of the test under the null, showing its validity. And second, to study the finite-sample performance of the different alternative procedures via Monte Carlo simulation.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2011

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.)

References

REFERENCES

Anderson, T.W. (1993) Goodness of fit tests for spectral distributions. Annals of Statistics 21, 830847.CrossRefGoogle Scholar
Bartlett, M.S. (1954) Problèmes de l’analyse spectral des séries temporelles stationnaires. Publications de l’Institut de Statistique de l’Université de Paris III-3, 119134.Google Scholar
Beran, J. (1998) Statistics for Long-Memory Processes. Chapman and Hall.Google Scholar
Billingsley, P. (1968) Convergence of Probability Measures. Wiley.Google Scholar
Box, G.E.P., Pierce, D.A. (1970) Distribution of residual autocorrelations in autoregressive-integrated moving average time series model. Journal of the American Statistical Association 65, 15091526.CrossRefGoogle Scholar
Bloomfield, P. (1973) An exponential model in the spectrum of a scalar time series. Biometrika 60, 217226.CrossRefGoogle Scholar
Brown, R.L., Durbin, J., & Evans, J.M. (1975) Techniques for testing constancy of regression relationships over time (with discussion). Journal of the Royal Statistical Society, Series B. 37, 149192.Google Scholar
Chen, H. & Romano, J.P. (1999) Bootstrap-assisted goodness-of-fit tests in the frequency domain. Journal of Time Series Analysis 20, 619654.CrossRefGoogle Scholar
Delgado, M.A., Hidalgo, J., & Velasco, C. (2005) Distribution free goodness-of-fit tests for linear processes. Annals of Statistics 33, 25682609.CrossRefGoogle Scholar
Delgado, M.A. & Stute, W. (2008) Distribution-free specification tests of conditional models. Journal of Econometrics 143, 3755.CrossRefGoogle Scholar
Giacomini, R., Politis, D.N., & White, H. (2007) A Warp-Speed Method for Conducting Monte Carlo Experiments Involving Bootstrap Estimators, Preprint, University College London.Google Scholar
Giné, E. & Zinn, S. (1990) Bootstrapping general empirical measures. Annals of Probability 18, 851869.CrossRefGoogle Scholar
Götze, F. (1979) Asymptotic expansions for bivariate von Mises functionals. Zeitschrift für Wahrscheinlichskeitstheorie and Verwondte Gebiete 50, 333355.CrossRefGoogle Scholar
Götze, F. (1984) Expansions for von Mises functionals. Zeitschrift für Wahrscheinlichskeitstheorie and Verwondte Gebiete 65, 599625.CrossRefGoogle Scholar
Granger, C. (1980) Long memory relationships and the aggregation of dynamic models. Journal of Econometrics 14, 227238.CrossRefGoogle Scholar
Granger, C. & Joyeux, R. (1980) An introduction to long-range time series models and fractional differencing. Journal of Time Series Analysis 1, 1530.CrossRefGoogle Scholar
Hall, P. (1992) The Bootstrap and Edgeworth Expansion. Springer-Verlag.CrossRefGoogle Scholar
Hidalgo, J. (2003) An alternative bootstrap to moving blocks for time series regression models. Journal of Econometrics 117, 369399.CrossRefGoogle Scholar
Hidalgo, J. (2009) Goodness of fit for lattice processes. Journal of Econometrics 151, 113128.CrossRefGoogle Scholar
Hidalgo, J. & Kreiss, J.-P. (2006) Bootstrap specification tests for linear covariance stationary processes. Journal of Econometrics 133, 807839.CrossRefGoogle Scholar
Hong, Y. (1996) Consistent testing for serial-correlation of unknown form. Econometrica 64, 837864.CrossRefGoogle Scholar
Hosking, J.R.M. (1981) Fractional differencing. Biometrika 68, 165176.CrossRefGoogle Scholar
Khmaladze, E.K. (1981) A martingale approach in the theory of goodness-of-fit tests. Theory of Probability and Its Applications 26, 240257.CrossRefGoogle Scholar
Koenker, R. & Xiao, Z. (2002) Inference on the quantile regression process. Econometrica 81, 15831612.CrossRefGoogle Scholar
Koul, H. & Stute, W. (1999) Nonparametric model checks in time series. Annals of Statistics 27, 204237.CrossRefGoogle Scholar
Ljung, G.M. & Box, G.E.P. (1978) On a measure of a lack of fit in time series models. Biometrika 65, 297303.CrossRefGoogle Scholar
Robinson, P.M. (1978) Statistical inference for a random coefficient autoregressive model. Scandinavian Journal of Statistics 5, 163168.Google Scholar
Robinson, P.M. (1994a) Time series with strong dependence. In Sims, C.A. (ed.), Advances in Econometrics: Sixth World Congress, vol. 1, pp. 4796. Cambridge University Press.CrossRefGoogle Scholar
Robinson, P.M. (1994b) Efficient tests of nonstationary hypotheses. Journal of the American Statistical Association 89, 14201437.CrossRefGoogle Scholar
Robinson, P.M. (1995a) Log-periodogram regression of time series with long range dependence. Annals of Statistics 23, 10481072.CrossRefGoogle Scholar
Robinson, P.M. (1995b) Gaussian semiparametric estimation of long range dependence. Annals of Statistics 23, 16301661.CrossRefGoogle Scholar
Shao, J. & Tu, D. (1995) The Jackknife and Bootstrap. Springer-Verlag.CrossRefGoogle Scholar
Velasco, C. & Robinson, P.M. (2000) Whittle pseudo-maximum likelihood estimates of non-stationary time series. Journal of the American Statistical Association 95, 12291243.CrossRefGoogle Scholar