CAJ | 학술논문

针对时间序列分布特征多样性的问题,不考虑序列本身的分布特征而选择非对称Laplace分布的似然函数对模型进行贝叶斯分位回归分析.利用Metropolis-Hastings算法模拟参数的后验边缘分布,解决了参数估计过程遇到的高维数值积分的问题.仿真分析中,参数的迭代轨迹是收敛的,说明MH抽样有效地模拟了参数的后验边缘分布;并且应用该方法估计出了不同分位数下模型参数的后验均值,标准差,MC误差和95%的置信区间.非对称和局部持续性数据的数值模拟,证实了贝叶斯分位自回归模型可以更全面有效地描述滞后变量对响应变量变化范围和条件分布形状的影响.
침대시간서렬분포특정다양성적문제,불고필서렬본신적분포특정이선택비대칭Laplace분포적사연함수대모형진행패협사분위회귀분석.이용Metropolis-Hastings산법모의삼수적후험변연분포,해결료삼수고계과정우도적고유수치적분적문제.방진분석중,삼수적질대궤적시수렴적,설명MH추양유효지모의료삼수적후험변연분포;병차응용해방법고계출료불동분위수하모형삼수적후험균치,표준차,MC오차화95%적치신구간.비대칭화국부지속성수거적수치모의,증실료패협사분위자회귀모형가이경전면유효지묘술체후변량대향응변량변화범위화조건분포형상적영향.
To address the problem that the distribution feature of time series could not always be easily described due to its diversity, the likelihood function based on the asymmetric Laplace distribution was employed irrespective of the original distribution of the data. To carry out Bayesian inference on the quantile autoregression, the Metropolis-Hastings algorithm was utilized to simulate the posterior marginal distribution of quantile autoregressive parameters,which resolved the difficulties of the high dimension numerical integral. The simulation result has shown that the MH algorithm is effective in simulating the posterior marginal densities because trace plots are convergent. The posterior mean, standard deviation, MC error and 95% posterior confidence interval of the quantile autoregressive parameters were estimated, which has comprehensively described how lag variables influence the location, scale and shape of the conditional distribution of the response.