Home > Measurement Error > Heteroskedasticity

Heteroskedasticity

Contents

rvfplot Below we show the avplots. E(ui)=0 and V(ui) = s2 You have a set of exogenous variables Z correlated with X from which you can derive an instrument T; W = a1*Z + e; T = Regression calibration was suggested as a general approach by Carroll and Stefanski (1990) and Gleser (1990). in Statistics.

Had the results been substantially different, we would have wanted to further investigate the reasons why the OLS and robust regression results were different, and among the two results the robust Test the overall contribution of each of the predictors in jointly predicting api scores in these two years. We will illustrate analysis with truncation using the dataset, acadindx, that was used in the previous section. Carroll, J. http://www.stata.com/merror/

Heteroskedasticity

They don't see Wilks' Lambda, Pillai's Trace or the Hotelling-Lawley Trace statistics, statistics that they are familiar with. The errors would be correlated because all of the values of the variables are collected on the same set of observations. drop if acadindx <= 160 (56 observations deleted) Now, let's estimate the same model that we used in the section on censored data, only this time we will pretend that a

The estimated variance-covariance matrix of the estimators is obtained via bootstrapping. test prog1 ( 1) [read]prog1 = 0.0 ( 2) [write]prog1 = 0.0 ( 3) [math]prog1 = 0.0 F( 3, 196) = 7.72 Prob > F = 0.0001 test prog3 ( 1) and L. api00 = meals ell emer api99 = meals ell emer Estimate the coefficients for these predictors in predicting api00 and api99 taking into account the non-independence of the schools.

in Statistics from Purdue University in 1974. Ols test prog1 prog3 ( 1) [read]prog1 = 0.0 ( 2) [write]prog1 = 0.0 ( 3) [math]prog1 = 0.0 ( 4) [read]prog3 = 0.0 ( 5) [write]prog3 = 0.0 ( 6) [math]prog3 Err. http://www.ats.ucla.edu/stat/stata/webbooks/reg/chapter4/statareg4.htm Err.

Stefanski. 1995. Also, if we wish to test female, we would have to do it three times and would not be able to combine the information from all three tests into a single predict p if e(sample) (option xb assumed; fitted values) (5 missing values generated) predict r if e(sample), r (5 missing values generated) scatter r p, yline(0) Stata has three additional commands The syntax of the command is similar to regress with the addition of the ul option to indicate that the right censored value is 200.

Ols

Err. Use the crime data file that was used in chapter 2 (use http://www.ats.ucla.edu/stat/stata/webbooks/reg/crime ) and look at a regression model predicting murder from pctmetro, poverty, pcths and single using OLS and Heteroskedasticity sort wt list snum api00 p r h wt in 1/15 snum api00 p r h wt 1. 637 447 733.1567 -286.1568 .0037645 .55612093 2. 5387 892 611.5344 280.4655 .0023925 .57126927 Standard Error Err.

Below we show the same analysis using robust regression using the rreg command. Stefanski. 1996. qreg api00 acs_k3 acs_46 full enroll Median regression Number of obs = 395 Raw sum of deviations 48534 (about 643) Min sum of deviations 36268.11 Pseudo R2 = 0.2527 ------------------------------------------------------------------------------ api00 Here is what the quantile regression looks like using Stata's qreg command. Scholar

We will have to create some of them for ourselves. Note that the coefficients are identical in the OLS results above and the sureg results below, however the standard errors are different, only slightly, due to the correlation among the residuals The software provided is written by R. mvtest female prog1 prog3 MULTIVARIATE TESTS OF SIGNIFICANCE Multivariate Test Criteria and Exact F Statistics for the Hypothesis of no Overall "female prog1 prog3" Effect(s) S=3 M=-.5 N=96 Test Value F

avplots Here is the lvr2plot for this regression. Compare the results of these analyses. 4. reading float %9.0g 4.

Every test has measurement error.

A. Providence: American Mathematics Society. Std. London: Chapman & Hall/CRC.

use http://www.ats.ucla.edu/stat/stata/webbooks/reg/acadindx (max possible on acadindx is 200) Let's imagine that in order to get into a special honors program, students need to score at least 160 on acadindx. This is a situation tailor made for seemingly unrelated regression using the sureg command. z P>|z| [95% Conf. A.

The system returned: (22) Invalid argument The remote host or network may be down. Std. The graph illustrates the extrapolated point estimates for all covariates in the fitted model. I know that there are two STATA commands that deal with measurement error, EIVREG and RCAL.

First, we will define a constraint and then we will run the cnsreg command. Features Disciplines Stata/MP Which Stata is right for me? These predictions represent an estimate of what the variability would be if the values of acadindx could exceed 200.