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. Determine which statement below is accurate:

a. The standard error of a regression, , is not an unbiased estimator for , the standard deviation of the error term, u, in a multiple regression model.

b. OLS estimators are always unbiased in time series regressions.

c. Unbiasedness is widely accepted by most economists as a minimal requirement for an estimator in regression analysis.

d. All estimators in a regression model that exhibit consistency also demonstrate unbiasedness.

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: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

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

a. dispersion of j becomes increasingly dispersed around j as the sample size grows.

b. dispersion of j becomes increasingly concentrated around j as the sample size grows.

c. distribution of j tends to follow a normal distribution as the sample size grows.

d. dispersion of j remains constant as the sample size grows.

ANSWER: b

RATIONALE: FEEDBACK: When j, an unbiased estimator of j, is consistent, the dispersion of j becomes increasingly concentrated around j as the sample size grows.

POINTS: 1

DIFFICULTY: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

3. Assuming j, an unbiased estimator of j, is also a consistent estimator of j, then as the sample size approaches 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 as the sample size tends to infinity the distribution of j collapses to the single point j.

POINTS: 1

DIFFICULTY: Straightforward

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

4. Within 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 explanatory 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: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

5. When 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: Straightforward

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

6. Given 1 = Cov(x1,x2) / Var(x1) where x1 and x2 are two independent variables in a regression equation, the following statement 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 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: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

7. If the model satisfies the first 4 Gauss-Markov assumptions, then v has the following property:

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 4 Gauss-Markov assumptions, then v has a zero mean and is uncorrelated with x1 and x2.

POINTS: 1

DIFFICULTY: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Consistency

KEYWORDS: Bloom’s: Data

8. When OLS estimators exhibit asymptotic normality, it indicates that:

a. they are generally normally distributed in sufficiently large sample sizes.

b. they are generally 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 1 and variance equal to .

ANSWER: a

RATIONALE: Feedback: When OLS estimators exhibit asymptotic normality, it indicates that they are generally normally distributed in sufficiently large sample sizes.

POINTS: 1

DIFFICULTY: Easy

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Asymptotic Normality and Large Sample Inference

KEYWORDS: Bloom’s: Data

9. In a regression model, if the 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 regardless of how large the sample size is

d. the t statistics and the confidence intervals are both invalid regardless of how large the sample size is

ANSWER: d

RATIONALE: Feedback: If the variance of the dependent variable conditional on an explanatory variable is not constant, the usual t statistics and the confidence intervals are both invalid regardless of how large the sample size is.

POINTS: 1

DIFFICULTY: Reasonable

NATIONAL STANDARDS: United States – BUSPROG: Analytic

TOPICS: Asymptotic Normality and Large Sample Inference

KEYWORDS: Bloom’s: Data

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: Data

Economics Related Test Banks

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

Test Bank For Introductory Econometrics A Modern Approach 5th Edition by Jeffrey M. Wooldridge