MEASUREMENT ERROR MODELS
MEASUREMENT ERROR MODELS
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DOI：
10.1002/9780470316665
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Xiaohong Chen;Han Hong;Denis Nekipelov
中科院分区：
文献类型：
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作者：
Xiaohong Chen;Han Hong;Denis Nekipelov
许多经济数据集被错误测量的变量所污染。计量误差问题是经验经济学中最基本的问题之一。在经济分析中，计量误差的存在会造成参数估计的偏差和不一致，并导致不同程度的错误结论。用于解决测量误差问题的技术可以沿着两个维度分类。在线性变量误差(EIV)模型和非线性EIV模型中使用不同的技术。(在本文中，“线性”EIV模型意味着它在错误测量的变量和感兴趣的参数中都是线性的；“非线性”EIV模型意味着它在错误测量的变量中是非线性的。)对经典测量误差和非经典测量误差采用了不同的处理方法。(如果测量误差与潜在的真实变量无关，则它是“经典的”；否则它是“非经典的”。)由于具有经典测量误差的线性EIV模型的各种方法已为人所知，并在经验经济学中得到了广泛的应用，在这篇综述中，我们将更多地关注具有经典或非经典测量误差的非线性EIV模型的辨识和估计方法的最新理论进展。虽然时间序列数据的测量误差问题可能与横截面数据的测量误差问题一样严重，但在这次调查中，我们将重点关注纽约大学经济学系和斯坦福大学经济学系，以及
Many economic data sets are contaminated by the mismeasured variables. The problem of measurement errors is one of the most fundamental problems in empirical economics. The presence of measurement errors causes biased and inconsistent parameter estimates and leads to erroneous conclusions to various degrees in economic analysis. Techniques for addressing measurement error problems can be classified along two dimensions. Different techniques are employed in linear errors-in-variables (EIV) models and in nonlinear EIV models. (In this article, a “linear” EIV model means it is linear in both the mismeasured variables and the parameters of interest; a “nonlinear” EIV model means it is nonlinear in the mismeasured variables.) Different methods are used to treat classical measurement errors and nonclassical measurement errors. (A measurement error is “classical” if it is independent of the latent true variable; otherwise it is “nonclassical”.) Since various methods for linear EIV models with classical measurement errors are already known and are widely applied in empirical economics, in this survey we shall focus more on recent theoretical advances on methods for identification and estimation of nonlinear EIV models with classical or nonclassical measurement errors. While measurement error problems can be as severe with time series data as with cross sectional data, in this survey we shall focus on cross Department of Economics, New York University and Department of Economics, Stanford University and