MULTISTEP PREDICTION OF PANEL VECTOR AUTOREGRESSIVE PROCESSES
研究了面板向量自回归模型中递归和直接多步预测的均方预测误差，发现模型设定对预测精度有不同影响，对从事面板数据预测的学者有参考价值。
This paper considers the conventional recursive (otherwise known as plug-in) and direct multistep forecasts in a panel vector autoregressive framework. We derive asymptotic expressions for the mean square prediction error (MSPE) of both forecasts as N (cross sections) and T (time periods) grow large. Both the bias and variance of the least squares fitting are manifest in the MSPE. Using these expressions, we consider the effect of model specification on predictor accuracy. When the fitted lag order ( q ) is equal to or exceeds the true lag order ( p ), the direct MSPE is larger than the recursive MSPE. On the other hand, when the fitted lag order is underspecified, the direct MSPE is smaller than the recursive MSPE. The recursive MSPE is increasing in q for all q ≥ p . In contrast, the direct MSPE is not monotonic in q within the permissible parameter space. Extensions to bias-corrected least squares estimators are considered.