Please use this identifier to cite or link to this item: https://doi.org/10.4310/SII.2013.v6.n4.a8
Title: A local vector autoregressive framework and its applications to multivariate time series monitoring and forecasting
Authors: Chen, Y. 
Li, B.
Niu, L.
Keywords: Adaptive estimation
Multivariate time series
Non-stationarity
Yield curve
Issue Date: 2013
Citation: Chen, Y., Li, B., Niu, L. (2013). A local vector autoregressive framework and its applications to multivariate time series monitoring and forecasting. Statistics and its Interface 6 (4) : 499-509. ScholarBank@NUS Repository. https://doi.org/10.4310/SII.2013.v6.n4.a8
Abstract: Our proposed local vector autoregressive (LVAR) model has time-varying parameters that allow it to be safely used in both stationary and non-stationary situations. The estimation is conducted over an interval of local homogeneity where the parameters are approximately constant. The local interval is identified in a sequential testing procedure. Numerical analysis and real data applications are conducted to illustrate the monitoring function and forecast performance of the proposed model.
Source Title: Statistics and its Interface
URI: http://scholarbank.nus.edu.sg/handle/10635/104936
ISSN: 19387989
DOI: 10.4310/SII.2013.v6.n4.a8
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