Nested sub-sample search algorithm for estimation of threshold models

Li, Dong and Tong, Howell (2016) Nested sub-sample search algorithm for estimation of threshold models. Statistica Sinica, 26 (4). pp. 1543-1554. ISSN 1017-0405

Preview

PDF - Published Version Download (1MB) | Preview

• Author

Abstract

Threshold models have been popular for modelling nonlinear phenomena in diverse areas, in part due to their simple fitting and often clear model interpretation. A commonly used approach to fit a threshold model is the (conditional) least squares method, for which the standard grid search typically requires O(n) operations for a sample of size n; this is substantial for large n, especially in the context of panel time series. This paper proposes a novel method, the nested sub-sample search algorithm, which reduces the number of least squares operations drastically to O(log n) for large sample size. We demonstrate its speed and reliability via Monte Carlo simulation studies with finite samples. Possible extension to maximum likelihood estimation is indicated.

Item Type:	Article
Official URL:	http://www3.stat.sinica.edu.tw/statistica/
Additional Information:	© 2016 Institute of Statistical Science, Academia Sinica
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	17 Jan 2017 12:24
Last Modified:	12 Dec 2024 01:17
URI:	http://eprints.lse.ac.uk/id/eprint/68880

Actions (login required)

View Item