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摘要翻译：
在非线性状态空间模型中，当观测密度与状态相关时，可以通过粒子滤波进行隐状态的序贯学习（如Gordon et al.，1993)。这一条件在复杂环境下不一定成立，如金融经济学中考虑的不完全信息均衡模型。在本文中，我们对学习文学有两点贡献。首先，针对观测密度难以处理的一般状态空间模型，提出了一种新的滤波方法&状态观测采样(SOS)滤波。其次，我们对一类不完全信息经济建立了一个基于间接推理的估计量。我们在一个资产定价模型上证明了这些技术的良好性能，投资者学习应用于80多年的每日股票回报。
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英文标题：
《State-Observation Sampling and the Econometrics of Learning Models》
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作者：
Laurent E. Calvet and Veronika Czellar
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最新提交年份：
2011
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
In nonlinear state-space models, sequential learning about the hidden state can proceed by particle filtering when the density of the observation conditional on the state is available analytically (e.g. Gordon et al., 1993). This condition need not hold in complex environments, such as the incomplete-information equilibrium models considered in financial economics. In this paper, we make two contributions to the learning literature. First, we introduce a new filtering method, the state-observation sampling (SOS) filter, for general state-space models with intractable observation densities. Second, we develop an indirect inference-based estimator for a large class of incomplete-information economies. We demonstrate the good performance of these techniques on an asset pricing model with investor learning applied to over 80 years of daily equity returns.
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PDF链接：
https://arxiv.org/pdf/1105.4519
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