Multistep forecast selection for panel data
针对面板数据向量自回归过程，开发了基于最小化多步二次预测风险的新模型选择方法，使用偏差校正最小二乘估计，并在美国都市区人口增长预测中表现更优。
We develop a new set of model selection methods for direct multistep forecasting of panel data vector autoregressive processes. Model selection is based on minimizing the estimated multistep quadratic forecast risk among candidate models. To attenuate the small sample bias of the least squares estimator, models are fitted using bias-corrected least squares. We provide conditions sufficient for the new selection criteria to be asymptotically efficient as n (cross sections) and T (time series) approach infinity. The new criteria outperform alternative selection methods in an empirical application to forecasting metropolitan statistical area population growth in the US.