Bayesian High-Dimensional Regression for Change Point Analysis.
Bayesian High-Dimensional Regression for Change Point Analysis.
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DOI：
10.4310/sii.2019.v12.n2.a6
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发表时间：
2019
影响因子：
0.8
通讯作者：
A. Datta;H. Zou;Sudipto Banerjee
中科院分区：
文献类型：
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作者：
A. Datta;H. Zou;Sudipto Banerjee
在许多计量经济学应用中，被调查的数据集跨越不同的制度，使用由变化点分隔的每个数据段的分段组件来更合适地建模。我们考虑在变点设置中使用贝叶斯高维收缩先验来理解响应和协变量之间的特定于段的关系。在每个变化点之前和之后的协变量选择可以识别可能不同的相关协变量集合，而完全贝叶斯方法确保了对变化点的后验推断也是可用的。我们展示了该方法的灵活性，可以施加不同的变量选择约束，如分组或部分选择，并讨论了检测未知数量的变化点的策略。仿真实验表明，这种简单的方法能够提供准确的变量选择和对变化点位置的推断，并且在广泛的场景中一致地优于基于频率的Lasso方法。将我们的模型应用于明尼苏达州房价数据集，揭示了次贷危机前后房价和股票价格之间关系的变化。
In many econometrics applications, the dataset under investigation spans heterogeneous regimes that are more appropriately modeled using piece-wise components for each of the data segments separated by change-points. We consider using Bayesian high-dimensional shrinkage priors in a change point setting to understand segment-specific relationship between the response and the covariates. Covariate selection before and after each change point can identify possibly different sets of relevant covariates, while the fully Bayesian approach ensures posterior inference for the change points is also available. We demonstrate the flexibility of the approach for imposing different variable selection constraints like grouping or partial selection and discuss strategies to detect an unknown number of change points. Simulation experiments reveal that this simple approach delivers accurate variable selection, and inference on location of the change points, and substantially outperforms a frequentist lasso-based approach, uniformly across a wide range of scenarios. Application of our model to Minnesota house price dataset reveals change in the relationship between house and stock prices around the sub-prime mortgage crisis.