TESTING FOR COEFFICIENT RANDOMNESS IN LOCAL-TO-UNITY AUTOREGRESSIONS
针对自回归系数接近单位根时的随机性检验问题，提出一种对系数与扰动项相关性稳健的检验方法，并给出可处理序列相关和条件异方差的修正版本，在大小样本中均优于现有方法。
This study proposes a test for coefficient randomness in autoregressive models where the autoregressive coefficient is local to unity, which is empirically relevant given earlier work. Under this specification, we analyze the effect of the correlation between the random coefficient and disturbance on the properties of tests, a matter that remains largely unexplored in the literature. Our analysis reveals that tests proposed in earlier studies can have poor power when the correlation is moderate to large. The test proposed here is designed to have power functions robust to the correlation. A modified version of the test is suggested that can be applied when the disturbance is serially correlated and conditionally heteroskedastic. The test is shown to have better power properties than existing ones in large and finite samples.