LM Tests for Joint Breaks in the Dynamics and Level of a Long-Memory Time Series
提出一种修正的LM检验（LMW型检验），用于检测长记忆参数、短期动态和水平的联合突变，在保持原LM检验极限分布的同时提高检验功效，并通过蒙特卡洛模拟和七国集团远期贴现率数据验证其有效性。
We consider a single-step Lagrange multiplier (LM) test for joint breaks (at known or unknown dates) in the long memory parameter, the short-run dynamics, and the level of a fractionally integrated time-series process. The regression version of this test is easily implementable and allows to identify the specific sources of the break when the none hypothesis of parameter stability is rejected. However, its size and power properties are sensitive to the correct specification of short-run dynamics under the none. To address this problem, we propose a slight modification of the LM test (labeled LMW-type test) which also makes use of some information under the alternative (in the spirit of a Wald test). This test shares the same limiting distribution as the LM test under the none and local alternatives but achieves higher power by facilitating the correct specification of the short-run dynamics under the none and any alternative (either local or fixed). Monte Carlo simulations provide support for these theoretical results. An empirical application, concerning the origin of shifts in the long-memory properties of forward discount rates in five G7 countries, illustrates the usefulness of the proposed LMW-type test