Structural Break Inference Using Information Criteria in Models Estimated by Two-Stage Least Squares
Structural Break Inference Using Information Criteria in Models Estimated by Two-Stage Least Squares
复制标题
在两阶段最小二乘估计模型中使用信息准则进行结构断裂推断
DOI：
10.1111/jtsa.12107
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发表时间：
2015
影响因子：
0.9
通讯作者：
Hall A
中科院分区：
文献类型：
--
作者：
Hall A
本文提出了两个贡献有关使用的信息准则推断结构突变时，一个线性模型的系数与内生回归可能会经历多次变化。首先，我们表明，适当定义的信息准则产生一致的估计的休息，当采用在第二阶段的两阶段最小二乘（2SLS）程序的第二阶段与休息在减少的形式考虑在第一阶段。其次，Monte Carlo分析研究了基于贝叶斯信息准则（BIC），Hannan-Quinn信息准则（HQIC）和Akaike信息准则（AIC）的一系列准则对2SLS估计方程的有限样本性能。当惩罚项对每个断点的估计的权重大于对每个系数的估计时，一致性准则BIC和HQIC的版本总体上表现良好，而AIC是不一致的，并且严重高估了真实断点的数量。
This paper makes two contributions in relation to the use of information criteria for inference on structural breaks when the coefficients of a linear model with endogenous regressors may experience multiple changes. First, we show that suitably defined information criteria yield consistent estimators of the number of breaks, when employed in the second stage of a two‐stage least squares (2SLS) procedure with breaks in the reduced form taken into account in the first stage. Second, a Monte Carlo analysis investigates the finite sample performance of a range of criteria based on Bayesian information criterion (BIC), Hannan–Quinn information criterion (HQIC) and Akaike information criterion (AIC) for equations estimated by 2SLS. Versions of the consistent criteria BIC and HQIC perform well overall when the penalty term weights estimation of each break point more heavily than estimation of each coefficient, while AIC is inconsistent and badly over‐estimates the number of true breaks.