Important Sampling for Autoregressive Time Series with a Unit Root

机译：具有单位根的自回归时间序列的重要采样

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The Metropolis-Hastings sampling method is exploited to construct the available sampler set via the proposal distribution function which is employed to derive statistics to achieve unit root test. Compared with the i.i.d. sampling, the novel scheme presented in this paper can reduce the unit root test estimator variance. The simulation demonstrates that the Metropolis-Hastings sampling method can improve the accuracy of the unit root test.

机译：利用Metropolis-Hastings采样方法，通过提案分配函数构造可用的采样器集，该提议分配函数用于导出统计数据以实现单位根检验。与i.d.比较抽样，本文提出的新方案可以减少单位根检验估计量方差。仿真表明，Metropolis-Hastings采样方法可以提高单位根测试的准确性。

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《International institute of statistics management engineering symposium》|2011年|p.1390-1395|共6页
会议地点 Dalian(CN)
作者
ZHOU Yuan; TENG Xin; NING Yanli;
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Department of Social Science Xidian University P.R.China 710071;

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Important sampling (IS); Unit root test; Bayesian statistic inference; Metropolis-Hastings sampling method;

机译：重要抽样（IS）；单位根测试；贝叶斯统计推断；大都市-哈斯特抽样方法;

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1. Bootstrapping Unit Root Tests for Autoregressive Time Series [J] . Efstathios PAPARODITIS, Dimitris N. POLITIS Journal of the American statistical association . 2005,第470期

机译：自回归时间序列的自举单元根测试
2. A BAYESIAN ANALYSIS OF UNIT ROOTS AND STRUCTURAL BREAKS IN THE LEVEL, TREND, AND ERROR VARIANCE OF AUTOREGRESSIVE MODELS OF ECONOMIC SERIES [J] . Loukia Meligkotsidou, Elias Tzavalis, loannis D. Vrontos Econometric Reviews . 2011,第2期

机译：经济系列自动回归模型的水平，趋势和误差方差的单位根和结构破裂的贝叶斯分析
3. An instrumental variable approach for tests of unit roots and seasonal unit roots in asymmetric time series models [J] . Dong Wan Shin, Oesook Lee Journal of Econometrics . 2003,第1期

机译：一种工具变量方法，用于测试非对称时间序列模型中的单位根和季节单位根
4. Important Sampling for Autoregressive Time Series with a Unit Root [C] . ZHOU Yuan, TENG Xin, NING Yanli International Institute of Statistics Management Engineering Symposium . 2011

机译：与单位根的自回归时间序列的重要采样
5. Two essays on time series econometrics: Multiple time series analysis of the Asian crisis and estimation and inference of the nonstationary structural vector autoregressive model. [D] . Wang, Siyan. 2001

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6. Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada [O] . Tatiana Petukhova, Davor Ojkic, Beverly McEwen, 2012

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7. Normal Tests for a Unit Root in the Autoregressive Time Series Model [O] . Yamamoto Taku 100

机译：自回归时间序列模型中单位根的正态检验

Important Sampling for Autoregressive Time Series with a Unit Root

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