Jointly determining the state dimension and lag order for Markov‐switching vector autoregressive models
研究了如何同时选择马尔可夫切换向量自回归模型的状态维度和滞后阶数，提出了三种复杂度惩罚准则并推导了新准则，通过蒙特卡洛实验验证效果，并应用于美国和澳大利亚的商业周期建模。
This article studies the problem of joint selection of the state dimension and lag order for a class of Markov‐switching vector autoregressive models, in which all parameters are presumed to be regime‐dependent. To this end, three complexity‐penalized criteria are considered, and a new criterion is derived by minimizing the Kullback–Leibler divergence. The efficacy of the procedure is evaluated by means of Monte Carlo experiments. We illustrate the usefulness of the joint model selection procedure with empirical applications to the modeling of business cycles in the USA and Australia.