![]() ![]() " Marginal Treatment Effects with Misclassified Treatment,"Ģ02106180700001132, Iowa State University, Department of Economics. Acerenza, Santiago & Ban, Kyunghoon & Kedagni, Desire, 2021.Health Economics, John Wiley & Sons, Ltd., vol. " Correction Of Misclassification Error In Disability Rates," Amanda Gosling & Eirini‐Christina Saloniki, 2014. ![]() " Why Do Workers with Disabilities Earn Less? Occupational Job Requirements and Disability Discrimination,"īritish Journal of Industrial Relations, London School of Economics, vol. Douglas Kruse & Lisa Schur & Sean Rogers & Mason Ameri, 2018." Identification of Expected Outcomes in a Data Error Mixing Model With Multiplicative Mean Independence,"Ģ01101010800001256, Iowa State University, Department of Economics. " Identification of Expected Outcomes in a Data Error Mixing Model with Multiplicative Mean Independence,"ġ2496, Iowa State University, Department of Economics. Kreider, Brent & Pepper, John V., 2011.58(3), pages 407-449, July.įull references (including those not matched with items on IDEAS) Oxford Economic Papers, Oxford University Press, vol. " Disability, gender, and the British labour market," " The Statistical Theory of Racism and Sexism,"Īmerican Economic Review, American Economic Association, vol. " Matrix Methods for Estimating Odds Ratios with Misclassified Exposure Data: Extensions and Comparisons,"īiometrics, The International Biometric Society, vol. " The Employment Effect of the Disability Discrimination Act: Evidence from the Health Survey for England," The Review of Economics and Statistics, MIT Press, vol. " Measurement Error in Human Capital and the Black-White Wage Gap," Journal of Human Resources, University of Wisconsin Press, vol. " Overview of the Health Measures in the Health and Retirement Study," " Inferring disability status from corrupt data,"Ģ00804010700001637, Iowa State University, Department of Economics. Kreider, Brent & Pepper, John V., 2003.ġ0228, Iowa State University, Department of Economics." Inferring Disability Status from Corrupt Data,"ģ54, University of Virginia, Department of Economics. Our results show that the probability of underreporting is greater than the probability of overreporting disability. The proposed methodology is then used to identify whether people misreport their disability status using data from the British Household Panel Survey. We show that by imposing a simple restriction(s) for the joint misclassification probabilities, it is possible to measure the extent of the misclassification error in that specific variable. Based on the concept of the fourfold table and creating a nonlinear system of simultaneous equations from the observed proportions and predicted wages, we examine the need for different assumptions in order to obtain unique solutions for the system. Suppose again that \( S \subseteq \R^n\) and that \( g: S \to [0, \infty) \).This paper addresses the problem of point identification in the presence of measurement error in discrete variables in particular, it considers the case of having two "noisy" indicators of the same latent variable and without any prior information about the true value of the variable of interest. Discrete distributions are studied in detail in the chapter on Distributions. Distributions of this type are said to be discrete. ![]()
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