Testing Parameter Constancy in Unit Root Autoregressive Models Against Multiple Continuous Structural Changes
研究了在数据生成过程为随机游走时，针对多重时间依赖区间转换的逻辑平滑转换自回归模型的检验方法，发现检验统计量的渐近零分布非标准，蒙特卡洛实验显示检验具有适度的尺寸扭曲和满意的检验功效，并应用于瑞典失业率数据推翻了滞后假说。
This article considers tests for logistic smooth transition autoregressive (LSTAR) models accommodating multiple time dependent transitions between regimes when the data generating process is a random walk. The asymptotic null distributions of the tests, in contrast to the standard results in Lin and Teräsvirta (1994), are nonstandard. Monte Carlo experiments reveal that the tests have modest size distortions and satisfactory power against LSTAR models with multiple smooth breaks. The tests are applied to Swedish unemployment rates and the hysteresis hypothesis is over-turned in favour of an LSTAR model with two transitions between extreme regimes.