| [1] Dahlhuaus R. Fitting time series models to nonstationary processes [J]. The Annals of Statistics, 1997, 25(1): 1-37.
[2] West M, Prado R, Krystal A D. Evaluation and comparision of EEG traces: latent structure in nonstationary time series [J]. Journal of the American Statistical Association, 1999, 94(446): 375-387.
[3] Prado R, Huerta G. Time-varying autoregressions with model order uncertainty [J]. Journal of Time, 2002, 23(5): 599-618.
[4] Bibi A, Francq C. Consistent and asymptotically normal estimators for cyclically timedependent linear models [J]. Annals of the Institute of Statistical Mathematics, 2003, 55(1):
41-68.
[5] Francq C, Gautier A. Estimation of time-varying ARMA models with Markovian changes in regime [J]. Statistics and Probability Letters, 2004, 70(4): 243-251.
[6] Triantafyllopoulos K, Nason G P. A Bayesian analysis of moving average processes with time-varying parameters [J]. Computational Statistics and Data Analysis, 2007, 52(2): 1025-
1046.
[7] Nakajima J, Kasuya M, Watanabe T. Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese Economy and Monetary Policy [J]. Journal of the Japanese and International Economies, 2011, 25(3): 225-245.
[8] Ijima H, Grivel E. Deterministic regression methods for unbiased estimation of time-varying autoregressive parameters from noisy observations [J]. Signal Processing, 2012, 92(4): 857-871.
[9] 王永民, 何幼桦, 忻莉莉, 等. 时变自回归模型系数的估计及预测[J]. 应用数学与计算数学学报, 2007, 21(2): 35-41.
[10] Bekiros S. Forecasting with a state space time-varying parameter VAR model: evidence from the Euro area [J]. Economic Modelling, 2014, 38(C): 619-626.
[11] Barnett A, Mumtaz H, Theodoridis K. Forecasting UK GDP growth and inflation under structural change: a comparison of models with time-varying parameters [J]. Bank of England
Quarterly Bulletin, 2012, 89(3): 1237-1246.
[12] 中华人民共和国国家统计局National data—–国家数据[EB/OL]. [2015-12-30]. http://data.stats.gov.cn/easyquery.htm?cn=C01. |
