Artículo

Estamos trabajando para incorporar este artículo al repositorio
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

In this paper, we generalise the partly linear autoregression model considered in the literature by including moving average errors when we want to allow a large dependence to the past observations. The strong ergodicity of the process is derived. A consistent procedure to estimate the parametric and nonparametric components is provided together with a test statistic that allows to check the presence of a moving average component in the model. Also, a Monte Carlo study is carried out to check the performance of the given proposals. © American Statistical Association and Taylor & Francis 2010.

Registro:

Documento: Artículo
Título:On a partly linear autoregressive model with moving average errors
Autor:Bianco, A.; Boente, G.
Filiación:Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and CONICET, Ciudad Universitaria, Pabellón 2, Buenos Aires, C1428EHA, Argentina
Palabras clave:Ergodicity; Fisher-consistency; Moving average errors; Partly linear autoregression; Smoothing techniques
Año:2010
Volumen:22
Número:6
Página de inicio:797
Página de fin:820
DOI: http://dx.doi.org/10.1080/10485250903469744
Título revista:Journal of Nonparametric Statistics
Título revista abreviado:J. Nonparametric Stat.
ISSN:10485252
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10485252_v22_n6_p797_Bianco

Referencias:

  • Anderson, T.W., (1994) The Statistical Analysis of Time Series, , NewYork: John Wiley and Sons
  • Ango Nze, P., Critères d'Ergodicité Géométrique ouArithmétique de Modèles linéaires Perturbés à Représentation Markovienne (1998) Comptes Rendus Del'Academic Des Sciences, Series I (Paris), 326, pp. 371-376
  • Boente, G., Fraiman, R., Ergodicity, Geometric Ergodicity and Mixing Conditions for NonparametricARMA Processes (2002) Bulletin of The Brazilian Mathematical Society, 33, pp. 13-23
  • Bosq, D., (1996) Non parametric Statistics For Stochastic Processes: Estimation and Prediction, 110. , Lectures Notes in Statistics, Berlin: Springer-Verlag
  • Carbon, M., Delecroix, M., Nonparametric Forecasting in Time Series, a Computational Point of View (1993) Applied Stochastic Models and Data Analysis, 9, pp. 215-229
  • Collomb, G., Non Parametric Time Series Analysis and Prediction: Uniform Almost Sure Convergence of the Window and K-NN Autoregression Estimates (1985) Statistics, 16, pp. 297-307
  • Durbin, J., Efficient Estimation of Parameters in Moving-Average Models (1959) Biometrika, 46, pp. 306-316
  • Gao, J., Semiparametric Regression Smoothing of Nonlinear Time Series (1998) Scandinavian Journal of Statistics, 25, pp. 521-539
  • Gao, J., (2007) Non linear Time Series: Semiparametric and Non parametric Methods, , London: Chapman & Hall/CRC
  • Gao, J., Yee, T., Adaptive Estimation in Partly Linear Autoregressive Models (2000) The Canadian Journal of Statistics, 28, pp. 571-586
  • Györfi, L., Härdle, W., Sarda, P., Vieu, P., (1989) Nonparametric Curve Estimation FromTime Series, 60. , Lecture Notes in Statistics, Springer-Verlag
  • Hall, P., Lahiri, S.N., Truong, Y.K., On Bandwidth Choice for Density Estimation With Dependent Data (1995) Annals of Statistics, 23, pp. 2241-2263
  • Härdle, W., Vieu, P., Kernel Regression Smoothing of Time Series (1992) Journal of Time Series Analysis, 13, pp. 209-232
  • Härdle, W., Liang, H., Gao, J., (2000) Partially Linear Models, , Heidelberg: Physica-Verlag
  • Hart, J.D., Automated Kernel Smoothing of Dependent Data by Using Time Series Cross-validation (1994) Journal of The Royal Statistical Society, Series B, 56, pp. 529-542
  • Hart, J.D., SomeAutomated Methods of Smoothing Time-Dependent Data (1996) Journal of Nonparametric Statistics, 6, pp. 115-142
  • Hart, J.D., Wehrly, T.E., Kernel Regression Estimation Using Repeated Measurements Data (1986) Journal of American Statistical Association, 81, pp. 1080-1088
  • Hart, J.D., Andvieu, P., Data-driven Bandwidth Choice for Density Estimation Based on Dependent Data (1990) Annals of Statistics, 18, pp. 873-890
  • Masry, E., Tjøstheim, D., Nonparametric Estimation and Identification of Nonlinear ARCH Time Series (1995) Econometric Theory, 11, pp. 258-289
  • Mokkadem, A., Sur un Modèle Autorégressif Non linéaire, Ergodicité et Ergodicité Géométrique (1987) Journal of Time Series Analysis, 2, pp. 195-204
  • Nummelin, E., Tuominen, P., Geometric Ergodicity of Harris Recurrent Markov Chains WithApplications to Renewal Theory (1982) Stochastics Processes and Their Application, 2, pp. 187-202
  • Robinson, P., Root-n-Consistent Semiparametric Regression (1988) Econometrica, 56, pp. 931-954
  • Rosenblatt, M., (1971) Markov Processes: Structure and Asymptotic Behaviour, , Berlin: Springer-Verlag
  • Tweedie, R.L., Sufficient Conditions for Ergodicity and Recurrence of Markov Chains on a General State Space (1975) Stochastics Processes and Their Application, 3, pp. 385-403
  • Tweedie, R.L., Criteria for Classifying General Markov Chains (1976) Advances InApplied Probability, 8, pp. 737-771

Citas:

---------- APA ----------
Bianco, A. & Boente, G. (2010) . On a partly linear autoregressive model with moving average errors. Journal of Nonparametric Statistics, 22(6), 797-820.
http://dx.doi.org/10.1080/10485250903469744
---------- CHICAGO ----------
Bianco, A., Boente, G. "On a partly linear autoregressive model with moving average errors" . Journal of Nonparametric Statistics 22, no. 6 (2010) : 797-820.
http://dx.doi.org/10.1080/10485250903469744
---------- MLA ----------
Bianco, A., Boente, G. "On a partly linear autoregressive model with moving average errors" . Journal of Nonparametric Statistics, vol. 22, no. 6, 2010, pp. 797-820.
http://dx.doi.org/10.1080/10485250903469744
---------- VANCOUVER ----------
Bianco, A., Boente, G. On a partly linear autoregressive model with moving average errors. J. Nonparametric Stat. 2010;22(6):797-820.
http://dx.doi.org/10.1080/10485250903469744