銀慶剛 Ching-Kang  Ing Institute of Statistical Science Academia Sinica Taipei 11529, Taiwan, R.O.C. Tel: 886-2-27835611 ext. 412 FAX : 886-2-27831523 E-mail: cking@stat.sinica.edu.tw

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Recent Research Output

C.-K. Ing, J.-L. Lin and S.-H. Yu (2009). Toward optimal multistep forecasts in nonstationary autoregressions, Bernoulli, to appear. [pdf]

C.-K. Ing, C.-Y. Sin and S.-H. Yu (2009).Prediction errors in nonstationary autoregressions of infinite order, Econometric Theory, to appear. [pdf]

C.-K. Ing (2007). Accumulated prediction errors, information criteria and optimal forecasting for autoregressive time series, Annals of Statistics, 35, 1238-1277. [pdf]

C.-K. Ing and C.-Y. Sin (2006). On prediction errors in regression models with nonstationary regressors, Time Series and Related Topics: in Memory of Ching-Zong Wei, IMS Lecture Notes and Monograph Series, 52, 61-72.

C.-K. Ing and C.-Z. Wei (2006). A maximal moment inequality for long-range dependent time series with applications to estimation and model selection, Statistica Sinica, 16, 721-740.

C.-K. Ing and C.-Z. Wei (2005). Order selection for same-realization predictions in autoregressive processes, Annals of Statistics, 33, 2423-2474. [pdf]

C.-K. Ing (2004). Selecting optimal multistep predictors for autoregressive processes of unknown order, Annals of Statistics, 32, 693-722. [pdf]

C.-K. Ing (2003). Multistep prediction in autoregressive processes, Econometric Theory, 19, 254-279. [pdf]

C.-K. Ing and S.-H. Yu (2003). On estimating conditional mean-squared     prediction errors in autoregressive models, Journal of Time Series Analysis, 24, 401-422.

C.-K. Ing and C. Z. Wei (2003). On same-realization prediction in an infinite-order autoregressive process, Journal of Multivariate Analysis, 85, 130-155.

C.-K. Ing (2001). A note on mean-squared prediction errors of the least squares predictors in random walk models, Journal of Time Series Analysis, 22, 711-724.

C.-K. Ing, C.-Y. Sin and S.-H. Yu (2007). Using information criteria to select an autoregressive model: a unified approach without knowing the order of integratedness. Technical Report, Institute of Statistical, Academia Sinica.[pdf]

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