Time Series Approach to the Evolution of Networks: Prediction and Estimation
提出一种处理非负多元时间序列的模型，将其解释为加权网络，结合经典自回归与非负性、消失概率及同伴效应，用于预测和估计网络演化，并以欧盟贸易数据验证模型有效性。
The article analyzes nonnegative multivariate time series which we interpret as weighted networks. We introduce a model where each coordinate of the time series represents a given edge across time. The number of time periods is treated as large compared to the size of the network. The model specifies the temporal evolution of a weighted network that combines classical autoregression with nonnegativity, a positive probability of vanishing, and peer effect interactions between weights assigned to edges in the process. The main results provide criteria for stationarity versus explosiveness of the network evolution process and techniques for estimation of the parameters of the model and for prediction of its future values. Natural applications arise in networks of fixed number of agents, such as countries, large corporations, or small social communities. The article provides an empirical implementation of the approach to monthly trade data in European Union. Overall, the results confirm that incorporating nonnegativity of dependent variables into the model matters and incorporating peer effects leads to the improved prediction power.