Misclassification of the dependent variable in a discrete-response setting

By Jerry A. Hausman, Jason Abrevaya, F. Scott-Morton. 1998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Learning kernel logistic regression in the presence of class label noise (2014, background): “[17] has given a foundation of a statistical model for the binary classification problem but provide no algorithmic solution to the learning of label noise parameters.”
Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud (2016, unknown): “In § 3, we formalize the assumption, suggest a method of evaluating its validity, and use it to develop our empirical methodology for identifying the incentives of review fraud.”
Financial Distress and Bank Restructuring of Small to Medium Size UK Companies (2005, unknown): “Such a classification error biases (downwards) the estimated coefficients (see Hausman, Abrevaya, and Scott-Morton (1998)).”
Estimation of Average Treatment Effects With Misclassification (2007, background): “Hu (2004) obtains identification of h∗(x, t) under similar assumptions for multiple valued discrete treatments t , and weakens some of Mahajan’s side conditions.”
How robust is the evidence on the effects of college quality? Evidence from matching (2004, background): “First, following Hausman, Abrevaya, and Scott-Morton (1998), we worry that high ˆ( )P X respondents observed at low- quality schools may actually represent high ˆ( )P X respondents with measurement error in their college attended variable.”
Data mining for credit card fraud: A comparative study (2011, background): “Prior work in related areas has estimated logit models of fraudulent claims in insurance, food stamp programs, and so forth [2] [7] [23] [41].”
Teardowns and land values in the Chicago metropolitan area (2007, unknown): “Hausman, Abrevaya, and Scott-Morton [8] develop a simple estimation procedure that controls for misclassification of the dependent variable in a probit model.”
Valuing Alternative Work Arrangements (2017, methodology): “23We have also estimated WTP allowing α to be estimated internally within the model, as in Hausman et al. (1998). The resulting WTP estimates are presented in an appendix table and very close to those from the approach that uses the dominated choice.”
Discussion Papers in Economics and Econometrics A Computationally Practical Simulation Estimation Algorithm for Dynamic panel Data Models with Unobserved Endogenous State Variables (2005, background): “Notice that the speciÞcation in (9) allows the probability of reporting a particular choice to differ by the true choice, and allows for dynamic misreporting, since dit−1 appears in the index function.”
Understanding Off-the-Books Politics: Conducting Inference on the Determinants of Sensitive Behavior with Randomized Response Surveys (2010, background): “…causal effects in studies of sensitive political behavior can be seen as an alternative to the set of ex post statistical adjustments developed in literature on inference with a mismeasured response variable (cf. Hausman, Abrevaya, and Scott-Morton 1998; Dustmann and van Soest 2000; Ramalho 2002).”
Detection of Automobile Insurance Fraud With Discrete Choice Models and Misclassified Claims (2002, unknown): “We use a scoring method to obtain the maximum likelihood estimates as in Hausman et al. (1998). The information matrix is then:”