Consistent Estimation of Binary Models of Mental Illness

该项目将开发计量经济学方法，当健康指标容易出错时，对精神疾病的二元模型产生一致的估计。这一主题与健康应用高度相关，因为疾病通常由指示有无诊断的二元变量来衡量。关于控制中的测量误差和连续的结果变量的计量经济学后果，我们知道得很多。直到最近，研究人员才认识到，测量有误差的连续变量的理论含义并不延伸到错误测量的变量是二分法的情况。特别是，与经典结果相反，在线性和非线性回归模型中，即使二元结果变量中的纯随机测量误差也会使控制变量上的系数发生偏差。此外，经典的用于消除错误测量的控制变量引起的测量误差偏差的计量经济学解决方案--工具变量方法--在受误差影响的变量是二进制的情况下不是一个有效的解决方案。这些方法将在两种一般应用的背景下开发：一种是精神疾病作为控制变量--精神疾病对劳动力市场的影响；另一种是心理健康是结果变量--各种精神疾病的风险因素。这些方法将依赖于线性和非线性回归技术以及最大似然法。所有方法都将是易于处理的，并对任何与健康相关的行为的实证分析具有广泛的适用性。主要数据来源将是全国共病调查。

This project will develop econometric methods which yield consistent estimates of binary models of psychiatric illness when the health indicator is subject to error. This topic is highly relevant to health applications since illness is often measured by a binary variable indicating the presence or absence of a diagnosis. Much is known about the econometric consequences of measurement error in control and outcome variables that are continuous. Only recently have researchers recognized that the theoretical implications of measuring a continuous variable with error do not extend to the situation where the mismeasured variable is dichotomous. In particular, contrary to the classical result, even purely random measurement error in a binary outcome variable will bias the coefficients on control variables in both linear and nonlinear regression models. Moreover, the classic textbook econometric solution for eliminating measurement error bias arising from a mismeasured control variable, the instrumental variables approach, is not a valid solution when the variable subject to error is binary. The methods will be developed in the context of two general applications: one where psychiatric diseases serve as control variables--the labor market consequences of mental illness; and one where mental health is the outcome variable--the risk factors for various psychiatric disorders. The methods will rely on both linear and nonlinear regression techniques as well as the maximum likelihood approach. All approaches will be tractable and have broad applicability to any empirical analysis of health-related behavior. The primary data source will be the National Comorbidity Survey.