Question

What happens to the OLS estimators of β2 in the model yi=β1+β2xi+ui when ui and uj...

What happens to the OLS estimators of β2 in the model yi=β1+β2xi+ui when ui and uj are not independent?

a. b2is biased and its t-statistic needs an adjustment.

b. b2is biased. but its t-statistic is correct.

c. b2is unbiased. but its t-statistic needs an adjustment.

d. b2 is unbiased and its t-statistic is correct.

Please Explained

Math   Statistics-And-Probability   
0 0
Add a commentTranscribed image text
Next >

Homework Answers

Answer #1

The biasedness of the co-efficient estimates occur due to lack of independent variables with higher explanatory power. Presence of autocorrelation does not make the estimates biased. Also, presence of autocorrelation decreases the standard error of the co-efficient estimates. Since, the standard error decreases, the t-statistic to test the significance of the estimates increases. So, the t-statistic needs an adjustment. Hence, Option (a) is the correct choice. (Ans).

0 0
Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Log In

Sign Up

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
1. Consider the bivariate model: Yi = β0+β1Xi+ui . Explain what it means for the OLS...
1. Consider the bivariate model: Yi = β0+β1Xi+ui . Explain what it means for the OLS estimator, βˆ 1, to be consistent. (You may want to draw a picture.) 2. (Circle all that applies) Which of the following regression functions is/are linear in the parameters a) Yi = β1 + β2 1 Xi b) Yi = β1 + β 3 2Xi c) Yi = β1 + β2Xi
1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and...
1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=21.0213; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively. The total number of observations is 2950.According to these results the relationship between C and Y is: A. no relationship B. impossible to tell C. positive D. negative 2. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this...
7) Consider the following regression model Yi = β0 + β1X1i + β2X2i + β3X3i + ...
7) Consider the following regression model Yi = β0 + β1X1i + β2X2i + β3X3i + β4X4i + β5X5i + ui This model has been estimated by OLS. The Gretl output is below. Model 1: OLS, using observations 1-52 coefficient std. error t-ratio p-value const -0.5186 0.8624 -0.6013 0.5506 X1 0.1497 0.4125 0.3630 0.7182 X2 -0.2710 0.1714 -1.5808 0.1208 X3 0.1809 0.6028 0.3001 0.7654 X4 0.4574 0.2729 1.6757 0.1006 X5 2.4438 0.1781 13.7200 0.0000 Mean dependent var 1.3617 S.D. dependent...
Consider the following model: Yi = β0 + β1(X1)i + β2(X2)i + β3(X3)i + β4(X4)i +...
Consider the following model: Yi = β0 + β1(X1)i + β2(X2)i + β3(X3)i + β4(X4)i + ui Where: Y = Score in Standardized Test X1 = Student IQ X2 = School District X3 = Parental Education X4 = Parental Income The data for 5,000 students was collected via a simple random sample of all 8th graders in New Jersey.  Suppose you want to test the hypothesis that parental attributes have no impact on student achievement.  Which of the following is most accurate?...
Consider the following model: Yi = β0 + β1(X1)i + β2(X2)i + β3(X3)i + β4(X4)i +...
Consider the following model: Yi = β0 + β1(X1)i + β2(X2)i + β3(X3)i + β4(X4)i + ui Where: Y = Score in Standardized Test X1 = Student IQ X2 = School District X3 = Parental Education X4 = Parental Income The data for 5,000 students was collected via a simple random sample of all 8th graders in New Jersey. Suppose you want to test the hypothesis that parental attributes have no impact on student achievement. Which of the following is...
Consider the model, Yi = B0 + B1 X1,i + B2 X2,i + Ui, where sorting...
Consider the model, Yi = B0 + B1 X1,i + B2 X2,i + Ui, where sorting the residuals based on the X1,i and X2,i gives: X1 X2 Goldfeld-Quandt Statistic 1.362 (X1) 4.527 (X2) If there is heteroskedasticity present at the 5% critical-F value of 1.624, then choose the most appropriate heteroskedasticity correction method. A. Heteroskedastic correction based on X2. B. Heteroskedastic correction based on X1. C. No heteroskedastic correction needed. D. White's heteroskedastic-consistent standard errors E. Not enough information.
Consider the following linear regression model: yi=β1+β2x2i+β3x3i+ei σ2i=α1+α2x2i+α3x22i What is the form of the auxiliary regression...
Consider the following linear regression model: yi=β1+β2x2i+β3x3i+ei σ2i=α1+α2x2i+α3x22i What is the form of the auxiliary regression of the LM test for heteroskedasticity? Select one: a. ^e2i=α1+α2x2i+α3x22i b. ^e2i=α1+α2x2i c. ^e2i=α1+α2x2i+α3x3i d. ^e2i=α1+α2x22i
1. This question is concerned with bias for estimates in an OLS framework. Consider the model:...
1. This question is concerned with bias for estimates in an OLS framework. Consider the model: yi =Xiβ+εi (a) What is the critical assumption needed for the OLS estimate βˆ to be unbiased? (5 pts) (b) What is an endogeneity problem? (5 pts) (c) What is a simulteneity bias problem? (5 pts) (d) Why does a simulteneity bias problem cause bias? Show analytically. (5 pts) (e) consider the model above where yi is wage and Xi is marital status. What...
1. When the error-terms are heteroskedastic, then: a) WLS is more efficient than OLS in large...
1. When the error-terms are heteroskedastic, then: a) WLS is more efficient than OLS in large samples, if the functional form of the heteroskedasticity is known. b) OLS coefficients are biased. c) OLS is still BLUE but t and F distributions are invalid. d) there are only two solutions: either use WLS if the functional form of the heteroskedasticity is known or use GLS if the functional form of the heteroskedasticity is known. e) None of the above 2. Which...
Q1) answer the following a) Omitting an important variable that is uncorrelated with the included regressors...
Q1) answer the following a) Omitting an important variable that is uncorrelated with the included regressors will lead to biased coefficients on the included regressors and incorrect coefficient signs.True or False b) Experience (EXP) is positively associated with earnings (EARN), and years of schooling (S) is positively associated with earnings. Also, EXP and S are negatively correlated in younger workers. If a researcher estimates the regression : log(EARNINGS) = β1 + β2EXP + u, on younger workers, the estimated coefficient...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT