AUTOMATIC INFERENCE FOR INFINITE ORDER VECTOR AUTOREGRESSIONS
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AI 根据所提供的论文材料归类：摘要明确研究无限阶 VAR 的滞后选择和参数推断，并提及脉冲响应应用，对应时间序列与 VAR 方法。
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无限阶向量自回归的自动推断
- 研究问题
- 无限阶 VAR 以数据选择滞后长度的有限阶模型近似时，参数估计何时仍具有中心化渐近正态分布及统一收敛速度？
- 方法与思路
- 作者对数据依赖的滞后长度施加高概率成立的增长速率条件，建立参数估计的渐近理论，并讨论两类满足要求的选择规则。
- 材料陈述的贡献
- 摘要称，在所述滞后阶数速率条件下，参数估计渐近正态且均值为零，并建立无限阶 VAR 参数的统一收敛速度。
- 阅读关注点
- 应核对滞后长度的具体速率条件、两类规则及其可操作性，并确认渐近正态化方式和统一收敛结果覆盖哪些参数。
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- 仅依据所给 RePEc 出版社摘要；具体速率和选择规则未说明，不能认为任意信息准则或一般到特殊搜索都适用，也不推断版本变化。
原文摘要
Infinite order vector autoregressive (VAR) models have been used in a number of applications ranging from spectral density estimation, impulse response analysis, and tests for cointegration and unit roots, to forecasting. For estimation of such models it is necessary to approximate the infinite order lag structure by finite order VARs. In practice, the order of approximation is often selected by information criteria or by general-to-specific specification tests. Unlike in the finite order VAR case these selection rules are not consistent in the usual sense, and the asymptotic properties of parameter estimates of the infinite order VAR do not follow as easily as in the finite order case. In this paper it is shown that the parameter estimates of the infinite order VAR are asymptotically normal with zero mean when the model is approximated by a finite order VAR with a data dependent lag length. The requirement for the result to hold is that the selected lag length satisfies certain rate conditions with probability tending to one. Two examples of selection rules satisfying these requirements are discussed. Uniform rates of convergence for the parameters of the infinite order VAR are also established.Very helpful comments by the editor and two referees led to a substantial improvement of the manuscript. I am particularly indebted to one of the referees for pointing out an error in the proofs. All remaining errors are my own. Financial support from NSF grant SES−0095132 is gratefully acknowledged.