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

Introductory Econometrics A Modern Approach 6th Edition by Jeffrey M. Wooldridge - Test Bank   Instant Download - Complete Test Bank With Answers     Sample Questions Are Posted Below   1. Which of the following statements is true?   a. The standard error of a regression, , is not an unbiased estimator for , the …

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

 

Instant Download – Complete Test Bank With Answers

 

 

Sample Questions Are Posted Below

 

1. Which of the following statements is true?

  a. The standard error of a regression, , is not an unbiased estimator for , the standard deviation of the error, u, in a multiple regression model.
  b. In time series regressions, OLS estimators are always unbiased.
  c. Almost all economists agree that unbiasedness is a minimal requirement for an estimator in regression analysis.
  d. All estimators in a regression model that are consistent are also unbiased.

 

ANSWER:   a
RATIONALE:   FEEDBACK: The standard error of a regression is not an unbiased estimator for the standard deviation of the error in a multiple regression model.
POINTS:   1
DIFFICULTY:   Moderate
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Consistency
KEYWORDS:   Bloom’s: Knowledge

 

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

  a. distribution of j becomes more and more loosely distributed around j as the sample size grows.
  b. distribution of j becomes more and more tightly distributed around j as the sample size grows.
  c. distribution of j tends toward a standard normal distribution as the sample size grows.
  d. distribution of j remains unaffected as the sample size grows.

 

ANSWER:   b
RATIONALE:   FEEDBACK: If j, an unbiased estimator of j, is consistent, then the distribution of j becomes more and more tightly distributed around j as the sample size grows.
POINTS:   1
DIFFICULTY:   Moderate
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Consistency
KEYWORDS:   Bloom’s: Knowledge

 

3. If j, an unbiased estimator of j, is also a consistent estimator of j, then when the sample size tends to infinity:

  a. the distribution of j collapses to a single value of zero.
  b. the distribution of j diverges away from a single value of zero.
  c. the distribution of j collapses to the single point j.
  d. the distribution of j diverges away from j.

 

ANSWER:   c
RATIONALE:   FEEDBACK: If j, an unbiased estimator of j, is also a consistent estimator of j, then when the sample size tends to infinity the distribution of j collapses to the single point j.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Consistency
KEYWORDS:   Bloom’s: Knowledge

 

4. In a multiple regression model, the OLS estimator is consistent if:

  a. there is no correlation between the dependent variables and the error term.
  b. there is a perfect correlation between the dependent variables and the error term.
  c. the sample size is less than the number of parameters in the model.
  d. there is no correlation between the independent variables and the error term.

 

ANSWER:   d
RATIONALE:   FEEDBACK: In a multiple regression model, the OLS estimator is consistent if there is no correlation between the explanatory variables and the error term.
POINTS:   1
DIFFICULTY:   Moderate
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Consistency
KEYWORDS:   Bloom’s: Knowledge

 

5. If the error term is correlated with any of the independent variables, the OLS estimators are:

  a. biased and consistent.
  b. unbiased and inconsistent.
  c. biased and inconsistent.
  d. unbiased and consistent.

 

ANSWER:   c
RATIONALE:   FEEDBACK: If the error term is correlated with any of the independent variables, then the OLS estimators are biased and inconsistent.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Consistency
KEYWORDS:   Bloom’s: Knowledge

 

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

  a. If x2 has a positive partial effect on the dependent variable, and 1 > 0, then the inconsistency in the simple regression slope estimator associated with x1 is negative.
  b. If x2 has a positive partial effect on the dependent variable, and 1 > 0, then the inconsistency in the simple regression slope estimator associated with x1 is positive.
  c. If x1 has a positive partial effect on the dependent variable, and 1 > 0, then the inconsistency in the simple regression slope estimator associated with x1 is negative.
  d. If x1 has a positive partial effect on the dependent variable, and 1 > 0, then the inconsistency in the simple regression slope estimator associated with x1 is positive.

 

ANSWER:   b
RATIONALE:   FEEDBACK: Given that 1 = Cov(x1,x2)/Var(x1) where x1 and x2 are two independent variables in a regression equation, if x2 has a positive partial effect on the dependent variable, and 1 > 0, then the inconsistency in the simple regression slope estimator associated with x1 is positive.
POINTS:   1
DIFFICULTY:   Moderate
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Consistency
KEYWORDS:   Bloom’s: Knowledge

 

7. If the model ​ satisfies the first four Gauss-Markov assumptions, then v has:

  a. ​a zero mean and is correlated with only x1.
  b. ​a zero mean and is correlated with x1 and x2.
  c. ​a zero mean and is correlated with only x2.
  d. a ​zero mean and is uncorrelated with x1 and x2.

 

ANSWER:   d
RATIONALE:   FEEDBACK: If the model  satisfies the first four Gauss-Markov assumptions, then v has a zero mean and is uncorrelated with x1 and x2.
POINTS:   1
DIFFICULTY:   Moderate
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Consistency
KEYWORDS:   Bloom’s: Knowledge

 

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

  a. they are approximately normally distributed in large enough sample sizes.
  b. they are approximately normally distributed in samples with less than 10 observations.
  c. they have a constant mean equal to zero and variance equal to 2.
  d. they have a constant mean equal to one and variance equal to .

 

ANSWER:   a
RATIONALE:   Feedback: If OLS estimators satisfy asymptotic normality, it implies that they are approximately normally distributed in large enough sample sizes.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

9. In a regression model, if variance of the dependent variable, y, conditional on an explanatory variable, x, or Var(y|x), is not constant, _____.

  a. the t statistics are invalid and confidence intervals are valid for small sample sizes
  b. the t statistics are valid and confidence intervals are invalid for small sample sizes
  c. the t statistics and the confidence intervals are valid no matter how large the sample size is
  d. the t statistics and the confidence intervals are both invalid no matter how large the sample size is

 

ANSWER:   d
RATIONALE:   FEEDBACK: If variance of the dependent variable conditional on an explanatory variable is not a constant the usual t statistics and the confidence intervals are both invalid no matter how large the sample size is.
POINTS:   1
DIFFICULTY:   Moderate
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

10. If j is an OLS estimator of a regression coefficient associated with one of the explanatory variables, such that j = 1, 2, …., n, asymptotic standard error of j will refer to the:

  a. estimated variance of j when the error term is normally distributed.
  b. estimated variance of a given coefficient when the error term is not normally distributed.
  c. square root of the estimated variance of j when the error term is normally distributed.
  d. square root of the estimated variance of j when the error term is not normally distributed.

 

ANSWER:   d
RATIONALE:   FEEDBACK: Asymptotic standard error refers to the square root of the estimated variance of j when the error term is not normally distributed.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

11. A useful rule of thumb is that standard errors are expected to shrink at a rate that is the inverse of the:

  a. square root of the sample size.
  b. product of the sample size and the number of parameters in the model.
  c. square of the sample size.
  d. sum of the sample size and the number of parameters in the model.

 

ANSWER:   a
RATIONALE:   FEEDBACK: Standard errors can be expected to shrink at a rate that is the inverse of the square root of the sample size.
POINTS:   1
DIFFICULTY:   Moderate
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

12. An auxiliary regression refers to a regression that is used:

  a. when the dependent variables are qualitative in nature.
  b. when the independent variables are qualitative in nature.
  c. to compute a test statistic but whose coefficients are not of direct interest.
  d. to compute coefficients which are of direct interest in the analysis.

 

ANSWER:   c
RATIONALE:   FEEDBACK: An auxiliary regression refers to a regression that is used to compute a test statistic but whose coefficients are not of direct interest.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

13. The n–R-squared statistic also refers to the:

  a. F statistic.
  b. t statistic.
  c. z statistic.
  d. LM statistic.

 

ANSWER:   d
RATIONALE:   FEEDBACK: The n–R-squared statistic also refers to the LM statistic.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

14. The LM statistic follows a:

  a. t distribution.
  b. f distribution.
  c. distribution.
  d. binomial distribution.

 

ANSWER:   c
RATIONALE:   FEEDBACK: The LM statistic follows a distribution.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

15. Which of the following statements is true?

  a. In large samples there are not many discrepancies between the outcomes of the F test and the LM test.
  b. Degrees of freedom of the unrestricted model are necessary for using the LM test.
  c. The LM test can be used to test hypotheses with single restrictions only and provides inefficient results for multiple restrictions.
  d. The LM statistic is derived on the basis of the normality assumption.

 

ANSWER:   a
RATIONALE:   FEEDBACK: In large samples there are not many discrepancies between the F test and the LM test because asymptotically the two statistics have the same probability of a Type 1 error.
POINTS:   1
DIFFICULTY:   Moderate
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

16. When the error term is not normally distributed, then ​ is sometimes called the:

  a. ​asymptotic standard error.
  b. ​asymptotic t statistic.
  c. ​asymptotic confidence interval.
  d. ​asymptotic normality.

 

ANSWER:   a
RATIONALE:   FEEDBACK: When the error term is not normally distributed, then ​is sometimes called the asymptotic standard error.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

17. Which of the following statements is true under the Gauss-Markov assumptions?

  a. Among a certain class of estimators, OLS estimators are best linear unbiased, but are asymptotically inefficient.
  b. Among a certain class of estimators, OLS estimators are biased but asymptotically efficient.
  c. Among a certain class of estimators, OLS estimators are best linear unbiased and asymptotically efficient.
  d. The LM test is independent of the Gauss-Markov assumptions.

 

ANSWER:   c
RATIONALE:   FEEDBACK: Under the Gauss-Markov assumptions, among a certain class of estimators, OLS estimators are best linear unbiased and asymptotically efficient.
POINTS:   1
DIFFICULTY:   Moderate
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Efficiency of OLS
KEYWORDS:   Bloom’s: Knowledge

 

18. The Cauchy-Schwartz inequality implies that the asymptotic variance of ​ is:

  a. ​greater than .
  b. ​less than or equal to .
  c. ​equal to .
  d. ​less than .

 

ANSWER:   b
RATIONALE:   FEEDBACK: The Cauchy-Schwartz inequality implies that the asymptotic variance of is less than or equal to .
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Efficiency of OLS
KEYWORDS:   Bloom’s: Knowledge

 

19. If variance of an independent variable in a regression model, say x1, is greater than 0, or Var(x1) > 0, the inconsistency in 1 (estimator associated with x1) is negative, if x1 and the error term are positively related.

  a. True
  b. False

 

ANSWER:   False
RATIONALE:   FEEDBACK: If variance of an independent variable, say x1, is greater than 0, the inconsistency in 1 (estimator associated with x1) is positive if x1 and the error term are positively related.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Consistency
KEYWORDS:   Bloom’s: Knowledge

 

20. In the multiple regression model , if x1 is correlated with u but the other independent variables are uncorrelated with u, then all of the OLS estimators are generally consistent.

  a. True
  b. False

 

ANSWER:   False
RATIONALE:   FEEDBACK: In the multiple regression model , if x1 is correlated with u but the other independent variables are uncorrelated with u, then all of the OLS estimators are generally inconsistent.​
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Consistency
KEYWORDS:   Bloom’s: Knowledge

 

21. Even if the error terms in a regression equation, u1, u2, …, un, are not normally distributed, the estimated coefficients can be normally distributed.

  a. True
  b. False

 

ANSWER:   False
RATIONALE:   FEEDBACK: Even if the error terms in a regression equation, u1, u2, …, un, are not normally distributed, the estimated coefficients cannot be normally distributed.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

22. A normally distributed random variable is symmetrically distributed about its mean, it can take on any positive or negative value (but with zero probability), and more than 95% of the area under the distribution is within two standard deviations.

  a. True
  b. False

 

ANSWER:   True
RATIONALE:   FEEDBACK: A normally distributed random variable is symmetrically distributed about its mean, it can take on any positive or negative value (but with zero probability), and more than 95% of the area under the distribution is within two standard deviations.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

23. The F statistic is also referred to as the score statistic.

  a. True
  b. False

 

ANSWER:   False
RATIONALE:   FEEDBACK: The LM statistic is also referred to as the score statistic.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

24. The LM statistic requires estimation of the unrestricted model only.

  a. True
  b. False

 

ANSWER:   False
RATIONALE:   FEEDBACK: The LM statistic requires estimation of the restricted model only.
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Normality and Large Sample Inference
KEYWORDS:   Bloom’s: Knowledge

 

25. If Cov(z,x) ≠ 0, then z and x are correlated.​

  a. True
  b. False

 

ANSWER:   True
RATIONALE:   If Cov(z,x) ≠ 0, then z and x are correlated.​
POINTS:   1
DIFFICULTY:   Easy
NATIONAL STANDARDS:   United States – BUSPROG: Analytic
TOPICS:   Asymptotic Efficiency of OLS
KEYWORDS:   Bloom’s: Knowledge

 

 

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