Journal of the Royal Statistical Society. Series B, Statistical methodology, 2004-01, Vol.66 (2), p.463-477
2004
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Details
- Autor(en) / Beteiligte
- Titel
- Identification of non-linear additive autoregressive models
- Ist Teil von
- Journal of the Royal Statistical Society. Series B, Statistical methodology, 2004-01, Vol.66 (2), p.463-477
- Ort / Verlag
- Oxford, UK: Blackwell Publishing
- Erscheinungsjahr
- 2004
- Quelle
- Wiley-Blackwell Journals
- Beschreibungen/Notizen
- We propose a lag selection method for non-linear additive autoregressive models that is based on spline estimation and the Bayes information criterion. The additive structure of the autoregression function is used to overcome the 'curse of dimensionality', whereas the spline estimators effectively take into account such a structure in estimation. A stepwise procedure is suggested to implement the method proposed. A comprehensive Monte Carlo study demonstrates good performance of the method proposed and a substantial computational advantage over existing local-polynomial-based methods. Consistency of the lag selection method based on the Bayes information criterion is established under the assumption that the observations are from a stochastic process that is strictly stationary and strongly mixing, which provides the first theoretical result of this kind for spline smoothing of weakly dependent data.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1369-7412eISSN: 1467-9868DOI: 10.1111/j.1369-7412.2004.05500.xTitel-ID: cdi_crossref_primary_10_1111_j_1369_7412_2004_05500_x
- Format
- –
- Schlagworte
- Autoregressive models, Bayes information criterion, Distribution theory, Estimation methods, Exact sciences and technology, Forecasting models, General topics, Index sets, Lag selection, Linear regression, Mathematics, Modeling, Monte Carlo simulation, Non-linear models, Non-linear time series, Nonparametric method, Parametric inference, Polynomials, Probability and statistics, Probability theory and stochastic processes, Regression analysis, Sciences and techniques of general use, Splines, Statistical methods, Statistics, Stochastic processes, Stochastic regression, Studies, Time series, Time series forecasting, Time series models, Variable selection