Time aggregation of mixed causal–noncausal models
研究了混合因果-非因果自回归模型的系统聚合和流量聚合，发现聚合保留了非因果性并产生移动平均成分，蒙特卡洛模拟表明大样本下可识别前后向行为。
We study systematic and flow aggregation of mixed causal-noncausal autoregressive models. We show that aggregation preserves noncausality and generates a moving average component. Monte Carlo simulations demonstrate that backward- and forward-looking behavior can be identified empirically for sufficiently large samples. • Aggregation of MAR models leads to ARMA models for which noncausality is preserved. • MA components generated upon aggregation are invertible-noninvertible. • Systematic and flow aggregation generally create different MA orders. • Model identification after aggregation may be adversely affected. • Simulations show that the identification problem vanishes for larger samples.