摘要翻译：
我们考虑具有大量力矩条件的连续时间模型，其中结构参数依赖于一组特征，其影响是有趣的。最主要的例子是金融经济学中的线性因子模型，其中因子β依赖于观察到的特征，如企业特定工具和宏观经济变量，它们的影响表现出长期的时变β波动。我们将因子β指定为特征效应和捕捉高频运动的正交特性参数之和。研究人员常常不知道后者是否存在，也不知道它的强弱，因此关于特征效应的推论应该在一大类产生特性参数的数据过程中一致有效。我们在一个两步连续时间GMM框架中构造我们的估计和推理。研究发现，当特征参数方差接近边界（零点）时，估计的特征效应的极限分布不连续，这使得通常使用估计的渐近方差的“插入”方法只能在点上有效，并可能产生过覆盖概率或欠覆盖概率。我们表明，均匀性可以通过横截面自举来实现。我们的程序允许已知和估计的因素，还具有对估计未知因素的影响进行偏差校正的特点。
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英文标题：
《Uniform Inference for Characteristic Effects of Large Continuous-Time
  Linear Models》
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作者：
Yuan Liao, Xiye Yang
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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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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英文摘要：
  We consider continuous-time models with a large panel of moment conditions, where the structural parameter depends on a set of characteristics, whose effects are of interest. The leading example is the linear factor model in financial economics where factor betas depend on observed characteristics such as firm specific instruments and macroeconomic variables, and their effects pick up long-run time-varying beta fluctuations. We specify the factor betas as the sum of characteristic effects and an orthogonal idiosyncratic parameter that captures high-frequency movements. It is often the case that researchers do not know whether or not the latter exists, or its strengths, and thus the inference about the characteristic effects should be valid uniformly over a broad class of data generating processes for idiosyncratic parameters. We construct our estimation and inference in a two-step continuous-time GMM framework. It is found that the limiting distribution of the estimated characteristic effects has a discontinuity when the variance of the idiosyncratic parameter is near the boundary (zero), which makes the usual "plug-in" method using the estimated asymptotic variance only valid pointwise and may produce either over- or under- coveraging probabilities. We show that the uniformity can be achieved by cross-sectional bootstrap. Our procedure allows both known and estimated factors, and also features a bias correction for the effect of estimating unknown factors.
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PDF链接：
https://arxiv.org/pdf/1711.04392