Testing for the presence of measurement error

Wilhelm, Daniel

doi:10.1920/wp.cem.2019.4819

Cited by 14 publications

(21 citation statements)

References 87 publications

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“…For example, see Andrews (2001). We instead follow the approach of Wilhelm (2018), who showed it is possible to construct a simple test for the presence of measurement errors under weaker conditions and without the need to first identify the entire model. Theorem 1 in Wilhelm (2018) states that: if Y ⊥ Z | X * , then Equation (11) implies Y ⊥ Z | X.…”

Section: Test For the Presence Of Measurement Errormentioning

confidence: 99%

“…We begin our empirical study by testing for the presence of measurement error in reported LS. We adopt the nonparametric approach suggested recently by Wilhelm (2018) that, under suitable conditions, stochastic dependency between some auxiliary variables conditioning on the reported variable can be used to detect measurement error. 1 We use a Kolmolgorov-Smirnov type statistic and find evidence of measurement error in the reported LS for the entire dataset as well as for 26 out of 32 socioeconomic subgroups in the sample.…”

Section: Introductionmentioning

confidence: 99%

“…Our work makes three main contributions. (1) To the best of our knowledge, we are the first to apply the nonparametric test of Wilhelm (2018) and misclassification model of Hu (2008) to analyze SWB data. These novel econometric methods do not rely on unjustified parametric assumptions, allow for nonclassical errors, and do not require validation data (cf.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing Subjective Well-Being Data with Misclassification

Oparina

Srisuma

2021

Journal of Business & Economic Statistics

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We use novel nonparametric techniques to test for the presence of nonclassical measurement error in reported life satisfaction (LS) and study the potential effects from ignoring it. Our dataset comes from Wave 3 of the UK Understanding Society that is surveyed from 35,000 British households. Our test finds evidence of measurement error in reported LS for the entire dataset as well as for 26 out of 32 socioeconomic subgroups in the sample. We estimate the joint distribution of reported and latent LS nonparametrically in order to understand the mis-reporting behavior. We show this distribution can then be used to estimate parametric models of latent LS. We find measurement error bias is not severe enough to distort the main drivers of LS. But there is an important difference that is policy relevant. We find women tend to overreport their latent LS relative to men. This may help explain the gender puzzle that questions why women are reportedly happier than men despite being worse off in objective outcomes such as income and employment.

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Section: Test For the Presence Of Measurement Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyzing Subjective Well-Being Data with Misclassification

Oparina

Srisuma

2021

Journal of Business & Economic Statistics

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“…Such a test is useful in many economic applications, for example for testing monotone IV assumptions (e.g. Kasy (2014), Hoderlein, Holzmann, Kasy, and Meister (2016), Chetverikov and Wilhelm (2017), Wilhelm (2019)) and for testing identifying assumptions (e.g. Matzkin (1994), Lewbel and Linton (2007), Banerjee, Mukherjee, and Mishra (2009)).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Test of Stochastic Monotonicity

2020

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We propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive. In particular, we show that the test asymptotically controls size at a polynomial rate, is nonconservative, and detects certain smooth local alternatives that converge to the null with the fastest possible rate. Our test is based on a data-driven bandwidth value and the critical value for the test takes this randomness into account. Monte Carlo simulations indicate that the test performs well in finite samples. In particular, the simulations show that the test controls size and, under some alternatives, is significantly more powerful than existing procedures.

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Section: Introductionmentioning

confidence: 99%

An adaptive test of stochastic monotonicity

Kim¹,

Wilhelm²,

Chetverikov³

2019

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. AbstractWe propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive. In particular, we show that the test asymptotically controls size at a polynomial rate, is non-conservative, and detects certain smooth local alternatives that converge to the null with the fastest possible rate. Our test is based on a data-driven bandwidth value and the critical value for the test takes this randomness into account. Monte Carlo simulations indicate that the test performs well in finite samples. In particular, the simulations show that the test controls size and, under some alternatives, is significantly more powerful than existing procedures.

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Testing for the presence of measurement error

Cited by 14 publications

References 87 publications

Analyzing Subjective Well-Being Data with Misclassification

Analyzing Subjective Well-Being Data with Misclassification

An Adaptive Test of Stochastic Monotonicity

An adaptive test of stochastic monotonicity

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