Testing for observation-dependent regime switching in mixture autoregressive models
Testing for observation-dependent regime switching in mixture autoregressive models
复制标题
混合自回归模型中观察依赖状态切换的测试
DOI：
10.1016/j.jeconom.2020.04.048
复制
发表时间：
2017
影响因子：
6.3
通讯作者：
P. Saikkonen
中科院分区：
文献类型：
--
作者：
Mika Meitz;P. Saikkonen
测试政权切换时，政权切换概率被指定为常数（“混合模型”）或由有限状态马尔可夫链（“马尔可夫切换模型”）是长期存在的问题，也吸引了最近的兴趣。本文考虑测试时的政权切换概率是随时间变化的，并依赖于观测数据（“观测依赖政权切换”）。具体地说，我们考虑了混合自回归模型中依赖于观测的状态转换的似然比检验。测试问题是高度非标准的，涉及未识别的滋扰参数下的零，边界上的参数，奇异信息矩阵，和高阶近似的对数似然。我们推导出渐近零分布的似然比检验统计量在一般的混合自回归设置使用高层次的条件，允许各种形式的依赖关系的政权切换概率对过去的观察，我们说明了理论使用两个特定的混合自回归模型。似然比检验有一个非标准的渐近分布，可以很容易地模拟，和蒙特卡洛研究表明，测试有良好的有限样本量和功率属性。
Testing for regime switching when the regime switching probabilities are specified either as constants (‘mixture models’) or are governed by a finite-state Markov chain (‘Markov switching models’) are long-standing problems that have also attracted recent interest. This paper considers testing for regime switching when the regime switching probabilities are time-varying and depend on observed data (‘observation-dependent regime switching’). Specifically, we consider the likelihood ratio test for observation-dependent regime switching in mixture autoregressive models. The testing problem is highly nonstandard, involving unidentified nuisance parameters under the null, parameters on the boundary, singular information matrices, and higher-order approximations of the log-likelihood. We derive the asymptotic null distribution of the likelihood ratio test statistic in a general mixture autoregressive setting using high-level conditions that allow for various forms of dependence of the regime switching probabilities on past observations, and we illustrate the theory using two particular mixture autoregressive models. The likelihood ratio test has a nonstandard asymptotic distribution that can easily be simulated, and Monte Carlo studies show the test to have good finite sample size and power properties.
影响因子：
6.1
作者：
PAKES, A;POLLARD, D
通讯作者：
POLLARD, D