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Measurement Error Downward Bias


Analysis and resultsIn the previous section we showed that females in both the GUI survey and the NHANES III data are more likely to underreport their BMI. J Health Econ 2008, 27: 519–529. 10.1016/j.jhealeco.2007.05.005View ArticleGoogle ScholarCarroll RJ, Ruppert D, Stefanski L: Measurement error in nonlinear models. The second column reports the results for self-reported BMI. The estimated coefficient on true BMI shows a significant negative relationship between BMI and income. http://threadspodcast.com/measurement-error/measurement-error-bias.html

In our application –cov(X*, u) = 2.88 and var(u) = 2.81, which explains the similarity of the estimates from the two approaches. The results are provided in Table9 and are consistent with the findings we reported using the Irish data. A statistical test of the hypothesis that the coefficients on the recorded BMI and self-reported BMI are equal is rejected with a p-value of .0008.13 Using self-reported BMI we estimate that Econ Human Biol 2010, 8: 309–319. 10.1016/j.ehb.2010.08.004View ArticleGoogle ScholarLittle R, Rubin D: Statistical analysis with missing data. https://en.wikipedia.org/wiki/Endogeneity_(econometrics)

Measurement Error Attenuation Bias

Privacy policy About Wikipedia Disclaimers Contact Wikipedia Developers Cookie statement Mobile view current community blog chat Cross Validated Cross Validated Meta your communities Sign up or log in to customize your Simultaneity[edit] Suppose that two variables are codetermined, with each affecting the other. Please try the request again.

Generated Thu, 20 Oct 2016 11:27:06 GMT by s_wx1085 (squid/3.5.20) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: Connection As a result we have estimated the income model using both interval regressions and OLS with the midpoint of the bracket as the dependent variable. One possibility would be to consider the stochastic imputation approach developed for missing data, which has been extended to tackle measurement error problems (e.g. Attenuation Bias Proof However, most papers in the literature do not adopt this approach and simply instrument the self-reported measure.

The results from the second stage are presented in the final column of Table5. Measurement Error Ols How do spaceship-mounted railguns not destroy the ships firing them? Your cache administrator is webmaster. https://izajole.springeropen.com/articles/10.1186/2193-8997-2-3 In other areas of labour economics repeated measures are often used as instruments when one is concerned about measurement error; for instance in the returns to education literature one may have

The latter situation is sometimes referred to as differential measurement error; in this case X contains information about Y even after we condition on X * .4The probability limit of the Measurement Error Instrumental Variables The availability of internal validation in our study allows us to examine the issue of differential measurement error in more detail. College educated, older, married women who are proficient in English receive an income premium, while women in poor health and those who smoke regularly have lower incomes. To examine the impact of measurement error we estimate the same regression only this time using self-reported BMI.

Measurement Error Ols

In contrast to what we would expect with classical measurement error, we see that using the self-reported BMI overstates rather than attenuates the relationship between BMI and income. J Bus Econ Stat 2001,19(4):475–481. 10.1198/07350010152596727View ArticleGoogle ScholarIASCO International Association for the Study of Obesity 2009–2010 Report. Measurement Error Attenuation Bias vol 5, Handbook of Econometrics. Correlated Measurement Error Not the answer you're looking for?

This means that the reported BMI contains information on the respondent, even after controlling for true BMI. check my blog JEL codes C13, C26, I14 Keywords Obesity Non-classical measurement error Auxiliary data Instrumental variables 1. The tendency for respondents in our sample to underestimate their BMI in self-reported data is evident in both these data sets and is consistent with previous findings.8Table 1 Summary statistics on Contents 1 Exogeneity versus endogeneity 1.1 Static models 1.1.1 Omitted variable 1.1.2 Measurement error 1.1.3 Simultaneity 1.2 Dynamic models 1.2.1 Simultaneity 2 See also 3 References 4 Further reading 5 External Classical Errors-in-variables (cev) Assumptions

IntroductionObesity is a medical condition described as excess body weight in the form of fat. Stommel and Schoenborn (2009) compare self-reported and recorded BMI using US data and find a substantial amount of misclassification of obesity status when using self-reported BMI, particularly in the extreme (over We will briefly return to this issue later. http://threadspodcast.com/measurement-error/measurement-error-bias-ols.html In this case, a model given by y i = α + β x i ∗ + ε i {\displaystyle y_{i}=\alpha +\beta x_{i}^{*}+\varepsilon _{i}} is written in terms of observables and

The true parameter estimate is –.821, which indicates that a 5 point increase in BMI (which corresponds to approximately a 1 standard deviation increase) is associated with a €4105 reduction in Measurement Error Bias Definition A Guide to Econometrics (Sixth ed.). The system returned: (22) Invalid argument The remote host or network may be down.

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One can view this approach as an extension of the mean imputation adopted by the CE estimator. J Bus Econ Stat 1995,13(3):305–314.Google ScholarRigobon R: Identification through Heteroscedasticity. where C = X V ' X V X NV ' X NV − 1 is a matrix that will converge in probability to the identity matrix under reasonable assumptions on Measurement Error Models Fuller Pdf We next consider the extent to which the alternative estimators proposed in the literature succeed in tackling the bias associated with self-reported BMI.

Is it possible to keep publishing under my professional (maiden) name, different from my married legal name? To determine this we examine the relationship between the error and the true measure of BMI. The GUI data has the advantage of relatively large samples. have a peek at these guys Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the

pp.82–83. J Am Stat Assoc 1992,87(418):328–336. 10.1080/01621459.1992.10475212View ArticleGoogle ScholarAngrist J, Krueger A: Instrumental variables and the search for identification: from supply and demand to natural experiments. Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. The third column shows how the availability of “valid” instruments helps overcome the attenuation bias when the measurement error is classical.

The NHANES data have been used in previous studies looking at the impact of obesity on labour market outcomes (e.g. Furthermore we show that popular alternatives estimators that have been adopted to address problems of measurement error in BMI, such as the CE approach and the IV approach, continue to exhibit The system returned: (22) Invalid argument The remote host or network may be down. The interviews were carried out over the period from 1988–1994.

regression econometrics instrumental-variables share|improve this question edited Dec 22 '14 at 10:38 Andy 11.8k114671 asked Dec 22 '14 at 10:10 TomCat 3314 add a comment| 1 Answer 1 active oldest votes In the GUI sample the recorded measures were obtained by the interviewer in the respondent’s home at the end of the interview. However, as noted in section2, the implications of measurement error for economic analysis will differ depending on whether the error is classical or non-classical in nature. Section3, discusses our data and examines the nature of measurement error in the self-reported measures of BMI.

In this case X is said to be a surrogate for X*.5Bound et al. (2001) page 3738, discuss an extension of this approach that uses internal validation data to correct for As before we check the robustness of these findings to the choice of additional controls specified in the model. BMC Public Health 2001, 1: 11. 10.1186/1471-2458-1-11View ArticleGoogle Scholarvon Hinke S, Smith G, Lawlor D, Propper C, Windemeijer F: The effect of Fat mass on educational attainment: examining the sensitivity to The instrument Z should be such that E(ZX*) ≠ 0 and E(Zu) = 0.

North-Holland, Amsterdam; 2001.View ArticleGoogle ScholarBrownstone D, Valletta R: Modelling earnings measurement error: a multiple imputation approach. Tables3 and 4 report the summary statistics for self-reported and recorded height and weight respectively.