Instrumental Variable Treatment of Nonclassical Measurement Error Models

By Yingyao Hu, Susanne M. Schennach. 2008

373 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/36b32d544b9003483c19222804c8cdb4a60f27fc

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Citations in the corpus

Identifying distributional characteristics in random coefficients panel data models (2009, background): “In the case where uit is MA(r), vit given by (47) follows an ARMA(p,r) process.”
Regression Discontinuity Design with Continuous Measurement Error in the Running Variable (2017, neutral): “10 Assumption 5 is common in the nonparametric IV framework (Newey & Powell, 2003) or in models with measurement error (Hu & Schennach, 2008).”
Nonparametric Identification and Semiparametric Estimation of Classical Measurement Error Models Without Side Information (2012, methodology): “Finally, we consider the sieve-based instrumental variable estimator of Hu and Schennach (2008), denoted θ̂HS, which is consistent in the presence of measurement error, but requires the availability of an instrument.”
Local indirect least squares and average marginal effects in nonseparable structural systems (2012, methodology): “More general proxies contaminated by nonclassical or Berkson-type 2 measurement errors can be treated by adapting methods of Hu and Schennach (2008) or Schennach (2007).”
Teacher Expectations Matter (2016, neutral): “Drawing on lessons from the measurement error literature (Hu & Schennach, 2008), the model formalizes two key ideas.6 First, the same factors jointly generate teacher expectations and student edu- 4On negative bias and self-fulfilling prophecies, Rist (1970) provides a rather harrowing account of…”
Nonlinear Panel Data Estimation via Quantile Regressions (2015, methodology): “An extension of Hu and Schennach (2008)’s theorem, along the lines of Hu and Shum (2012), then shows nonparametric identification of all conditional densities fY2|Y1,η,X , fY3|Y2,η,X , fY4|Y3,η,X , and fη|Y1,X , in the autoregressive model, under suitable assumptions.”
ON THE COMPLETENESS CONDITION IN NONPARAMETRIC INSTRUMENTAL PROBLEMS (2010, methodology): “…local instrumental variables (see Florens, Heckman, Meghir, and Vytlacil 2003), and nonclassical measurement error problems (see Chen and Hu, 2006; Hu and Schennach, 2008).3 This dependence condition is quite abstract though, and a characterization or at least sufficient conditions on the joint…”
On the Testability of Identification in Some Nonparametric Models With Endogeneity (2012, background): “http://www.econometricsociety.org/ Econometrica, Vol. 81, No. 6 (November, 2013), 2535–2559”
Examples of L2-Complete and Boundedly-Complete Distributions (2011, neutral): “…and Pouzo (2009b), and Chen, Chernozhukov, Lee, and Newey (2010), (iv) measurement error models, see Bissantz, Hohage, Munk, and Ruymgaart (2007), Hu and Schennach (2008), Carroll, Chen, and Hu (2009), An and Hu (2009), Song (2011), and Wilhelm (2011), (v) demand models, see Berry and Haile…”
Regression towards the mode (2012, background): “…particular, there is evidence that the conditional mode may be reasonably resilient to some forms of measurement error (see Bound and Krueger, 1991; Hu and Schennach, 2008) and we will show that mode regression is a limiting case of a family of well-known robust regression estimators, which are…”
A New Instrumental Method for Dealing with Endogenous Selection (2010, methodology): “Table 2 displays the average test scores on 23A notable exception is the United States where several states have reintroduced this policy by linking promotion to state or district assessment (see Jacob and Lefgren, 2004). 24Troncin (2005) measures the effect of grade retention on achievement in the ”
Controlling for endogeneity with instrumental variables in strategic management research (2008, background): “3 As an exception, Hu and Schennach (2008) showed that, under specific assumptions about how errors are reported, an analyst can still use instrumental variables, with the use of advanced econometric methods, when measurement error is non-classical and non-linear.”
NONPARAMETRIC IDENTIFICATION USING INSTRUMENTAL VARIABLES: SUFFICIENT CONDITIONS FOR COMPLETENESS (2011, background): “…models (see Chernozhukov and Hansen (2005), Chernozhukov, Imbens, and Newey (2007), and Horowitz and Lee (2007)), measurement error models (see Hu and Schennach (2008), An and Hu (2012), Carroll, Chen, and Hu (2010), and Chen and Hu (2006)), random coefficient models (see Hoderlein, Nesheim,…”