Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics of multivariate time series data. They have been widely used in macroeconomics, financial econometrics, neuroscience and functional genomics. In many applications, the data exhibit structural changes in their autoregressive dynamics, which correspond to changes in the transition matrices of the VAR model that specify such dynamics. We present the R package VARDetect that implements two classes of algorithms to detect multiple change points in piecewise stationary VAR models. The first exhibits sublinear computational complexity in the number of time points and is best suited for structured sparse models, while the second exhibits linear time complexity and is designed for models whose transition matrices are assumed to have a low rank plus sparse decomposition. The package also has functions to generate data from the various variants of VAR models discussed, which is useful in simulation studies, as well as to visualize the results through network layouts.
翻译:多变时间序列数据中,这些数据广泛用于宏观经济学、金融计量学、神经科学和功能基因组学。在许多应用中,数据显示了其自动递减动态的结构性变化,这些变化与VAR模型中指定这种动态的过渡矩阵的变化相对应。我们展示了R包VAR检测系统,该套系统采用两种算法,以探测小巧固定的VAR模型中的多种变化点。在时间点数中,首个显示亚线性计算复杂性,最适合结构分散模型,而第二个显示线性时间复杂性,是为那些假设过渡矩阵等级低加上稀有分解配置的模型设计的。该包还具有从所讨论的VAR模型的各种变式生成数据的职能,这对模拟研究有用,并且通过网络布局对结果进行可视化。