Invariant Inference and Efficient Computation in the Static Factor Model
Invariant Inference and Efficient Computation in the Static Factor Model
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DOI：
10.1080/01621459.2017.1287080
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发表时间：
2013-06
影响因子：
3.7
通讯作者：
Joshua Chan;R. León-González;Rodney W. Strachan
中科院分区：
文献类型：
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作者：
Joshua Chan;R. León-González;Rodney W. Strachan
摘要因素模型被广泛应用于各个领域。这些模型的贝叶斯版本的两个问题是缺乏对变量的排序和缩放的不变性以及计算效率低下。本文发展了估计静态因素模型的不变贝叶斯方法和高效贝叶斯方法。这种方法导致的推断不依赖于变量的顺序或比例，我们提供了参数来解释这种不变性。从满足正交性约束的已识别参数出发，利用参数展开得到一个计算方便的条件后验的规范。我们在计算效率方面取得了显著的进步。确定通常使用的限制会产生可解释的因素或负载，并且使用我们的方法，可以在事后施加这些限制。这使我们可以研究几种可选的识别(非不变)方案，而不需要重新指定和重新采样模型。我们用两个宏观经济数据集来说明这些方法。
ABSTRACT Factor models are used in a wide range of areas. Two issues with Bayesian versions of these models are a lack of invariance to ordering of and scaling of the variables and computational inefficiency. This article develops invariant and efficient Bayesian methods for estimating static factor models. This approach leads to inference that does not depend upon the ordering or scaling of the variables, and we provide arguments to explain this invariance. Beginning from identified parameters which are subject to orthogonality restrictions, we use parameter expansions to obtain a specification with computationally convenient conditional posteriors. We show significant gains in computational efficiency. Identifying restrictions that are commonly employed result in interpretable factors or loadings and, using our approach, these can be imposed ex-post. This allows us to investigate several alternative identifying (noninvariant) schemes without the need to respecify and resample the model. We illustrate the methods with two macroeconomic datasets.