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Autor
Bagnato Luca (Universita degli Studi di Milano-Bicocca), Punzo Antonio (Universita di Catania)
Tytuł
Nonparametric Bootstrap Test for Autoregressive Additive Models
Źródło
Statistics in Transition, 2009, vol. 10, nr 3, s. 359-370, rys., tab., bibliogr. s. 369-370
Słowa kluczowe
Szeregi czasowe, Estymacja, Modele autoregresji, Metody samowsporne
Time-series, Estimation, Autoregression models, Bootstrap
Uwagi
summ.
Abstrakt
Additive autoregressive models are commonly used to describe and simplify the behaviour of a nonlinear time series. When the additive structure is chosen, and the model estimated, it is important to evaluate if it is really suitable to describe the observed data since additivity represents a strong assumption. Although literature presents extensive developments on additive autoregressive models, few are the methods to test additivity which are generally applicable. In this paper a procedure for testing additivity in nonlinear time series analysis is provided. The method is based on: Generalized Likelihood Ratio, Volterra expansion and nonparametric conditional bootstrap (Jianqing and Qiwei, 2003). Investigation on performance (in terms of empirical size and power), and comparisons with other additivity tests proposed by Chen et al. (1995) are made recurring to Monte Carlo simulations. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej w Warszawie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu
Pełny tekst
Pokaż
Bibliografia
Pokaż
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  2. BOX, G. E. P. and PIERCE, D. A., 1970. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65(332), pp. 1509-1526.
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  4. BUJA, A., HASTIE, T., and TIBSHIRANI, R., 1989. Linear smoothers and additive models. The Annals of Statistics, 17(2), pp. 453-510.
  5. CHEN, R. and TSAY, R. S., 1993. Functional-coefficient autoregressive models. Journal of the American Statistical Association, 88(421), pp. 298-308.
  6. CHEN, R., LIU, J. S., and TSAY, R. S., 1995. Additivity tests for nonlinear autoregression. Biometrika, 82(2), pp. 369-383.
  7. FAN, J., ZHANG, C., and ZHANG, J., 2001. Generalized likelihood ratio statistics and Wilks phenomenon. The Annals of Statistics, 29(1), pp. 153193.
  8. HASTIE, T. and TIBSHIRANI, R. J., 1990. Generalized additive models. London: Chapman and Hall.
  9. JIANQING, F. and QIWEI, Y., 2003. Nonlinear time series: nonparametric and parametric methods. New York: Springer.
  10. LEWIS, P. and RAY, B., 1993. Nonlinear modelling of multivariate and categorical time series using multivariate adaptive regression splines. In H. Tong, ed. Dimension Estimation and Models. Singapore: World Scientific, pp. 136-169.
  11. LINTON, O. and NIELSEN, J. P., 1995. A kernel method of estimating structured nonparametric regression based on marginal integration. Biometrika, pp. 82(1), 93-100.
  12. TRUONG, Y., 1993. A nonparametric framework for time series analysis. New Directions in Time Series Analysis, pp. 371-386. New York: Springer.
Cytowane przez
Pokaż
ISSN
1234-7655
Język
eng
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