AUTOMATED ESTIMATION OF VECTOR ERROR CORRECTION MODELS
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向量误差修正模型的自动化估计
- 研究问题
- 在协整系统中，如何同时确定未知协整秩和短期动态滞后阶数，并减少逐步模型选择对参数估计及后续推断的影响？
- 方法与思路
- 作者利用自适应收缩估计向量误差修正模型，联合选择协整秩和自回归阶数，并发展极限理论、实现方法、模拟及宏观应用。
- 材料陈述的贡献
- 摘要称，该方法实现秩和阶数的同时估计，并对协整矩阵与短期动态提供类似预言机的高效估计，形成自动化建模方案。
- 阅读关注点
- 应核对惩罚与调参规则、秩和阶数选择一致性的条件，以及类似预言机性质的准确含义和模型选择后推断的实际处理。
- 阅读边界
- 本导读仅据所给 Crossref 摘要；收缩条件、选择错误下的推断及模拟数值摘要未说明，类似预言机性质不能自动保证有限样本可靠性。
原文摘要
Model selection and associated issues of post-model selection inference present well known challenges in empirical econometric research. These modeling issues are manifest in all applied work but they are particularly acute in multivariate time series settings such as cointegrated systems where multiple interconnected decisions can materially affect the form of the model and its interpretation. In cointegrated system modeling, empirical estimation typically proceeds in a stepwise manner that involves the determination of cointegrating rank and autoregressive lag order in a reduced rank vector autoregression followed by estimation and inference. This paper proposes an automated approach to cointegrated system modeling that uses adaptive shrinkage techniques to estimate vector error correction models with unknown cointegrating rank structure and unknown transient lag dynamic order. These methods enable simultaneous order estimation of the cointegrating rank and autoregressive order in conjunction with oracle-like efficient estimation of the cointegrating matrix and transient dynamics. As such they offer considerable advantages to the practitioner as an automated approach to the estimation of cointegrated systems. The paper develops the new methods, derives their limit theory, discusses implementation, reports simulations, and presents an empirical illustration with macroeconomic aggregates.