Geometric View of Measurement Errors

The slope of the best fit line from minimizing the sum of the squared oblique errors is the root of a polynomial of degree four. This geometric view of measurement errors is used to give insight into the performance of various slope estimators for the measurement error model including an adjusted fourth moment estimator introduced by Gillard and Iles (2005) to remove the jump discontinuity in the estimator of Copas (1972). The polynomial of degree four is associated with a minimun deviation estimator. A simulation study compares these estimators showing improvement in bias and mean squared error

Measurement error and the effect of inequality on experienced versus reported crime

This paper analyzes measurement errors in crime data to see how they impact econometric estimates, particularly of the key relationship between inequality and crime. Criminal victimization surveys of 140,000 respondents in 37 industrial, transition and developing countries are used. Comparing the crimes experienced by these respondents with those reported to the police, non-random and mean-reverting measurement errors are apparent. Some time-varying factors may also affect the propensity of victims to report crimes to the police, undermining the use of country-specific fixed effects as a means of dealing with measurement errors in official crime data. These measurement errors substantially attenuate both cross-sectional and panel estimates of the effect of inequality on crime

Structural Measurement Errors in Nonseparable Models

This paper considers measurement error from a new perspective. In surveys, response errors are often caused by the fact that respondents recall past events and quantities imperfectly. We explore the consequences of recall errors for such key econometric is- sues as the identification of marginal effects or economic restrictions in structural models. Our identification approach is entirely nonparametric, using Matzkin-type nonseparable models that nest a large class of potential structural models. We establish that measurement errors due to poor recall are generally likely to exhibit nonstandard behavior, in particular be nonclassical and differential, and we provide means to deal with this situation. Moreover, our findings suggest that conventional wisdom about measurement errors may be misleading in many economic applications. For instance, under certain conditions left-hand side recall errors will be problematic even in the linear model, and quantiles will be less robust than means. Finally, we apply the main concepts put forward in this paper to real world data, and find evidence that underscores the importance of focusing on individual response behavior

Minimizing noise-temperature measurement errors

An analysis of noise-temperature measurement errors of low-noise amplifiers was performed. Results of this analysis can be used to optimize measurement schemes for minimum errors. For the cases evaluated, the effective noise temperature (Te) of a Ka-band maser can be measured most accurately by switching between an ambient and a 2-K cooled load without an isolation attenuator. A measurement accuracy of 0.3 K was obtained for this example

On the Estimation of the Linear Relation when the Error Variances are known

The present article considers the problem of consistent estimation in measurement error models. A linear relation with not necessarily normally distributed measurement errors is considered. Three possible estimators which are constructed as different combinations of the estimators arising from direct and inverse regression are considered. The efficiency properties of these three estimators are derived and analyzed. The effect of non-normally distributed measurement errors is analyzed. A Monte-Carlo experiment is conducted to study the performance of these estimators in finite samples and the effect of a non-normal distribution of the measurement errors

Some Recent Advances in Measurement Error Models and Methods

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robustness of these methods have been compared. The paper gives an account of these endeavors. In another context, when data are of a categorical nature, classification errors play a similar role as measurement errors in continuous data. The paper also reviews some recent advances in this field

Modified McLeod pressure gage eliminates measurement errors

Modification of a McLeod gage eliminates errors in measuring absolute pressure of gases in the vacuum range. A valve which is internal to the gage and is magnetically actuated is positioned between the mercury reservoir and the sample gas chamber