leads to correlation between the regressor and the error term. Instruction: choose as many that apply. Multicollineality Measurement error in an explanatory variable Simultaneous causality Heteroskedasticity O Mean conditional independence Omission of a relevant variable
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- Which of the following is true of heteroskedasticity? a) The R-squared statistic is affected by the presence of heteroskedasticity b) Heteroskedasticty causes inconsistency in the Ordinary Least Squares estimators c) The OLS estimators are not the best linear unbiased estimators if heteroskedasticity is present d) It is not possible to obtain F statistics that are robust to heteroskedasticity of an unknown formWith Panel Data, if we assume that the individual effects vi are not correlated with the regressors Xit (i.e. E(vi|Xit) = 0), which one of the following statements is correct: The Fixed Effects estimator is not consistent. Both the OLS and the Random Effects estimators are not consistent. The OLS estimator is not consistent, but the Random Effects estimator is consistent. The OLS and the Random Effects estimator are consistent. All of the above. None of the above(Yi, X1i, X2i) satisfy the assumptions of the attachment. You are interestedin β1, the causal effect of X1 on Y. Suppose that X1 and X2 are uncorrelated.You estimate β1 by regressing Y onto X1 (so that X2 is not included in theregression). Does this estimator suffer from omitted variable bias? Explain.
- et R2unrestricted and R2restricted be 0.54 and 0.30 respectively. The difference between the unrestricted and the restricted model is that you have imposed two restrictions. The number of regressors in the unrestricted model is 5. There are 500 observations. The F-statistic (under the assumption of homoskedasticity) in this case is 1. 4.61 2. 8.01 3. 98.5 4. 128.9Imperfect multicollinearity occurs when A. The explanatory variables are highly correlated with the dependent variable B. The explanatory variables are highly correlated with the error term C. The dependent variable is highly correlated with all the explanatory variables D. Two or more explanatory variables are highly correlated with one anotherI dont understand why my question is being rejected so i'll resend this one. The data is attached in the image. Please help me with these questions. Thank you in advanced!! a. How many observations are included in the data? Is the data balanced?b. Is the above result estimated from the fixed effects model or the random effects model?c. Explain the meaning of the estimate coefficient of the variable ???
- Q. Which of the following is true of heteroscedasticity? Options- a. Population R-squared is affected by the presence of heteroscedasticity. b. Heteroscedasticity causes inconsistency in the ordinary least squares estimators. c. The ordinary least squares estimators are not the best linear unbiased estimators if heteroscedasticity is present. d. It is not possible to obtain F statistics that are robust to heteroscedasticity of an unknown form.Please ASAP. Thank you. QUESTION Morbidity is defined as? A. The presence of disease B. The absence of disease C. Hospitalization rates D. The combination of mortality and number of diseases 2. A household is frequently used in analyses because more consistent data are usually collected at that level.TrueFalseQ1. State the property of an estimator that gives a correct estimate of the population parameter of interest, but only on average. Q2. State the property of ian estimal or thal converges to the populalion paaameter of interest as the sample size goes to infinity. Q3. Why is an efficient estimator a desirable property of the OLS estimator? Q4. Stare the assumption(s)under the classical linearregression model giving rise to a biased standard error of the coefficient estimates when violated.
- Please give a detailed answer to the question below.Options For Fill In Blank Answers:.05, .2, .25, and 5elastic or inelasticWhat does the term “variance analysis mean when applied of financial performance of health care organizations?The net reproduction rate of a population is defined as r=b1+b2s1+b3s1s2+.......bns1s2.....sn-1 where the b1 are the birth rates and the sj are the survival rates for the population . Explain why r can be interpreted as the average number of daughters born to a single female over her lifetim