Introductory Econometrics A Modern Approach 6th Edition by Jeffrey M. Wooldridge – Test Bank

Test Bank For Introductory Econometrics A Modern Approach 6th Edition by Jeffrey M. Wooldridge

ISBN-10:130527010X , ISBN-13:978-1305270107

CHAPTER NO 5

1. Which of the next statements is true?

a. The usual error of a regression, , just isn’t an unbiased estimator for , the usual deviation of the error, u, in a a number of regression mannequin.

b. In time sequence regressions, OLS estimators are at all times unbiased.

c. Nearly all economists agree that unbiasedness is a minimal requirement for an estimator in regression evaluation.

d. All estimators in a regression mannequin which might be constant are additionally unbiased.

ANSWER: a

RATIONALE: FEEDBACK: The usual error of a regression just isn’t an unbiased estimator for the usual deviation of the error in a a number of regression mannequin.

POINTS: 1

DIFFICULTY: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

2. If j, an unbiased estimator of j, is constant, then the:

a. distribution of j turns into an increasing number of loosely distributed round j because the pattern dimension grows.

b. distribution of j turns into an increasing number of tightly distributed round j because the pattern dimension grows.

c. distribution of j tends towards a normal regular distribution because the pattern dimension grows.

d. distribution of j stays unaffected because the pattern dimension grows.

ANSWER: b

RATIONALE: FEEDBACK: If j, an unbiased estimator of j, is constant, then the distribution of j turns into an increasing number of tightly distributed round j because the pattern dimension grows.

POINTS: 1

DIFFICULTY: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

3. If j, an unbiased estimator of j, can also be a constant estimator of j, then when the pattern dimension tends to infinity:

a. the distribution of j collapses to a single worth of zero.

b. the distribution of j diverges away from a single worth of zero.

c. the distribution of j collapses to the only level j.

d. the distribution of j diverges away from j.

ANSWER: c

RATIONALE: FEEDBACK: If j, an unbiased estimator of j, can also be a constant estimator of j, then when the pattern dimension tends to infinity the distribution of j collapses to the only level j.

POINTS: 1

DIFFICULTY: Straightforward

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

4. In a a number of regression mannequin, the OLS estimator is constant if:

a. there isn’t any correlation between the dependent variables and the error time period.

b. there’s a excellent correlation between the dependent variables and the error time period.

c. the pattern dimension is lower than the variety of parameters within the mannequin.

d. there isn’t any correlation between the impartial variables and the error time period.

ANSWER: d

RATIONALE: FEEDBACK: In a a number of regression mannequin, the OLS estimator is constant if there isn’t any correlation between the explanatory variables and the error time period.

POINTS: 1

DIFFICULTY: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

5. If the error time period is correlated with any of the impartial variables, the OLS estimators are:

a. biased and constant.

b. unbiased and inconsistent.

c. biased and inconsistent.

d. unbiased and constant.

ANSWER: c

RATIONALE: FEEDBACK: If the error time period is correlated with any of the impartial variables, then the OLS estimators are biased and inconsistent.

POINTS: 1

DIFFICULTY: Straightforward

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

6. If 1 = Cov(x1,x2) / Var(x1) the place x1 and x2 are two impartial variables in a regression equation, which of the next statements is true?

a. If x2 has a optimistic partial impact on the dependent variable, and 1 > 0, then the inconsistency within the easy regression slope estimator related to x1 is destructive.

b. If x2 has a optimistic partial impact on the dependent variable, and 1 > 0, then the inconsistency within the easy regression slope estimator related to x1 is optimistic.

c. If x1 has a optimistic partial impact on the dependent variable, and 1 > 0, then the inconsistency within the easy regression slope estimator related to x1 is destructive.

d. If x1 has a optimistic partial impact on the dependent variable, and 1 > 0, then the inconsistency within the easy regression slope estimator related to x1 is optimistic.

ANSWER: b

RATIONALE: FEEDBACK: On condition that 1 = Cov(x1,x2)/Var(x1) the place x1 and x2 are two impartial variables in a regression equation, if x2 has a optimistic partial impact on the dependent variable, and 1 > 0, then the inconsistency within the easy regression slope estimator related to x1 is optimistic.

POINTS: 1

DIFFICULTY: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

7. If the mannequin ​ satisfies the primary 4 Gauss-Markov assumptions, then v has:

a. ​a zero imply and is correlated with solely x1.

b. ​a zero imply and is correlated with x1 and x2.

c. ​a zero imply and is correlated with solely x2.

d. a ​zero imply and is uncorrelated with x1 and x2.

ANSWER: d

RATIONALE: FEEDBACK: If the mannequin satisfies the primary 4 Gauss-Markov assumptions, then v has a zero imply and is uncorrelated with x1 and x2.

POINTS: 1

DIFFICULTY: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

8. If OLS estimators fulfill asymptotic normality, it implies that:

a. they’re roughly usually distributed in massive sufficient pattern sizes.

b. they’re roughly usually distributed in samples with lower than 10 observations.

c. they’ve a relentless imply equal to zero and variance equal to 2.

d. they’ve a relentless imply equal to 1 and variance equal to .

ANSWER: a

RATIONALE: Suggestions: If OLS estimators fulfill asymptotic normality, it implies that they’re roughly usually distributed in massive sufficient pattern sizes.

POINTS: 1

DIFFICULTY: Straightforward

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Asymptotic Normality and Giant Pattern Inference

KEYWORDS: Bloom’s: Data

9. In a regression mannequin, if variance of the dependent variable, y, conditional on an explanatory variable, x, or Var(y|x), just isn’t fixed, _____.

a. the t statistics are invalid and confidence intervals are legitimate for small pattern sizes

b. the t statistics are legitimate and confidence intervals are invalid for small pattern sizes

c. the t statistics and the boldness intervals are legitimate irrespective of how massive the pattern dimension is

d. the t statistics and the boldness intervals are each invalid irrespective of how massive the pattern dimension is

ANSWER: d

RATIONALE: FEEDBACK: If variance of the dependent variable conditional on an explanatory variable just isn’t a relentless the same old t statistics and the boldness intervals are each invalid irrespective of how massive the pattern dimension is.

POINTS: 1

DIFFICULTY: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Asymptotic Normality and Giant Pattern Inference

KEYWORDS: Bloom’s: Data

10. If j is an OLS estimator of a regression coefficient related to one of many explanatory variables, such that j = 1, 2, …., n, asymptotic customary error of j will seek advice from the:

a. estimated variance of j when the error time period is generally distributed.

b. estimated variance of a given coefficient when the error time period just isn’t usually distributed.

c. sq. root of the estimated variance of j when the error time period is generally distributed.

d. sq. root of the estimated variance of j when the error time period just isn’t usually distributed.

ANSWER: d

RATIONALE: FEEDBACK: Asymptotic customary error refers back to the sq. root of the estimated variance of j when the error time period just isn’t usually distributed.

POINTS: 1

DIFFICULTY: Straightforward

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Asymptotic Normality and Giant Pattern Inference

KEYWORDS: Bloom’s: Data

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