On the forecasting ability of ARFIMA models when infrequent breaks occur
通过蒙特卡洛模拟和葡萄牙通胀率数据，比较长记忆模型与马尔可夫转换模型的预测表现，发现存在结构突变时长记忆模型预测效果较差。
Recent research has focused on the links between long memory and structural breaks, stressing the memory properties that may arise in models with parameter changes. In this paper, we question the implications of this result for forecasting. We contribute to this research by comparing the forecasting abilities of long memory and Markov switching models. Two approaches are employed: the Monte Carlo study and an empirical comparison, using the quarterly Consumer Price inflation rate in Portugal in the period 1968–1998. Although long memory models may capture some in‐sample features of the data, we find that their forecasting performance is relatively poor when shifts occur in the series, compared to simple linear and Markov switching models.