Consider the linear regression model Y = x theta + epsilon where Y denotes a vector of n observations on the dependent variable, x is a known matrix, theta is a vector of parameters to be estimated and epsilon is a random vector of uncorrelated errors. If X'X is nearly singular, that ...
Measurement error biases OLS results. When the measurement error variance in absolute or relative (reliability) form is known, adjustment is simple. We link the (known) estimators for these cases to GMM theory and provide simple derivations of their standard errors. Our focus is on the test ...
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We study the consequences of measurement error in the dependent variable of random-coefficients models, focusing on the particular case of quantile regress... YPC Hausman - 《Econometrica》 被引量: 0发表: 2021年 Estimation of sparse functional quantile regression with measurement error: a SIMEX appr...
This chapter discusses the use of instrumental variables for dealing with measurement error in regression covariates. Instruments are defined as observed variables that correlate with the mismeasured covariates, but do not correlate with the measurement error and with the model error. The approaches cons...
This indicates that the bias we found in average Ellenberg indicator values cannot be explained by measurement errors or by regression to the mean. In the end, Smart & Scott, as we did, come to the conclusion that there is a bias present and that separate regression lines per vegetation ...
error.Alsowewillstartbyassuming 2 v =0,i.e.thereisonlymeasurement errorinx.Theseassumptionsde…netheclassicalerrors-in-variablesmodel. Substitute(2)into(1): y= (exu)+ =y i = ex+( u)(8) Themeasurementerrorinxbecomespartoftheerrortermintheregression ...
Measurement errorEstimating equationGMMIn a linear mean regression setting with repeated measurement errors, we develop asymptotic properties of a naive estimator to better clarify the effects of these errors. We then construct a group of unbiased estimating equations with independent repetitions and make...
When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small ...
The simple linear regression model is Yi = a + bXi + εi, but when there is a measurement error in X another term needs to be added to the model: Yi = a + b(Xi + δi) + εi, where δi is the random measurement error in X. Let the population variance of X be denoted by...