NDLI: Adaptive estimation of autoregressive models with time-varying variances (2005).

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Adaptive estimation of autoregressive models with time-varying variances (2005).

Content Provider	CiteSeerX
Author	Xu, Ke-Li Phillips, Peter C. B.
Abstract	Stable autoregressive models of known finite order are considered with martingale differ-ences errors scaled by an unknown nonparametric time-varying function generating hetero-geneity. An important special case involves structural change in the error variance, but in most practical cases the pattern of variance change over time is unknown and may involve shifts at unknown discrete points in time, continuous evolution or combinations of the two. This paper develops kernel-based estimators of the residual variances and associated adap-tive least squares (ALS) estimators of the autoregressive coefficients. These are shown to be asymptotically efficient, having the same limit distribution as the infeasible generalized least squares (GLS). Comparisons of the efficient procedure and the ordinary least squares (OLS) reveal that least squares can be extremely inefficient in some cases while nearly optimal in others. Simulations show that, when least squares work well, the adaptive estimators perform comparably well, whereas when least squares work poorly, major efficiency gains are achieved by the new estimators.
File Format	PDF
Publisher Date	2005-01-01
Access Restriction	Open
Subject Keyword	Time-varying Variance Autoregressive Model Adaptive Estimation Structural Change Residual Variance Stable Autoregressive Model Continuous Evolution Ordinary Least Square Major Efficiency Gain Important Special Case Adap-tive Least Square Finite Order Error Variance Unknown Discrete Point New Estimator Martingale Differ-ences Error Efficient Procedure Autoregressive Coefficient Adaptive Estimator Limit Distribution Variance Change Unknown Nonparametric Time-varying Function Practical Case Kernel-based Estimator
Content Type	Text
Resource Type	Article

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