ECN-4330 Exam 1

In a model relating a person's wage to observed education and other unobserved factors, wage = β 0 + β 1 educ + u, OLS estimates of the population parameters are hatbeta0=-0.90 and hat β1 = 0.54. What is the predicted wage for an individual with 15 years of education 7.2 8.1 9.0 14.04

7.2

The OLS regression line, also known as the sample regression function, will always be parallel or equal the population regression function. True False

false

Spurious correlation is finding a relationship between y and x that is really due to other unobserved factors that affect y and also happen to be correlated with x. not a problem when using cross-sectional data. a consequence of heteroskedasticity or non constant variance. All answers are correct.

finding a relationship between y and x that is really due to other unobserved factors that affect y and also happen to be correlated with x.

____________ is defined as constant variance of the unobservable, u, conditional on x. Homoskedasticity Heteroskedasticity Spurrious correlation Zero conditional mean assumption

homoskedascity

hatβ ^ 1 is an unbiased estimate of β 1 if SLR.1 (A1) through SLR.4 (A4) hold. if SLR.1 (A1) through SLR.5 (A5) hold. in all random samples. as long as there is homoskedasticity.

if SLR.1 (A1) through SLR.4 (A4) hold.

When trying to measure the returns to education (the causal effect of education on earnings), which omitted factor is most problematic: experience in the workforce innate ability age of the individual All of the above.

innate ability

The equation, hatβ ^ 1 = β 1 + (∑ i = 1 n ( x i − x ¯ i ) u i) /S S T x shows that LaTeX: \hat{\beta}_1β ^ 1 equals the population slope plus a term that ______________. is a linear combination of the errors. always equals zero. always is positive. always is negative.

is a linear combination of the errors

The sample covariance of the OLS residuals and the fitted values (LaTeX: \hat{y} y ^) _________. is always positvie. is always negative. is always zero. None of the above.

is always zero

If the dependent variable of interest is wage and the regressor of interest is education, and we believe that each year of education increases wage by a constant percentage, then the dependent variable should be _________ and the regressor should be _________. log(wage); education wage; log(education) log(wage); log(education) wage; education

log(wage); education

We can estimate a constant elasticity model by analyzing a log-log model. level-log model. log-level model. level-level model.

log-log model

Because we are interested in the behavior of LaTeX: \hat{\beta}_1β ^ 1 across all possible samples, LaTeX: \hat{\beta}_1β ^ 1is properly viewed as a standard error. population parameter. random variable. degree of freedom.

random variable

The sample average of the OLS residuals equals zero. is positive. is negative. are undefined.

equals 0

In the example data set, auto.dta, what is the median price of an automobile $3291.00 $5006.50 $13,400.00 $16,000.00

$5006.5

The 5th edition of Jeffrey M. Wooldridge, "Introductory Econometrics: A Modern Approach", is required for the class. True False

False

Which of the following is not an example of economic data: Synthetic data Time-series data Cross-sectional data Panel data

Synthetic data

An economist's goal is to infer that one variable has __________________ another variable. a causal effect on an association with a spurious correlation with perfect collinearity with

a causal effect on

Heteroskedasticity exists when Var(u|x) is a function of x. Var(y|x) is a function of x. there is non-constant variance in u across x. All of the above.

all the above

Suppose that soybean yield is determined by the model yield = β 0 + β 1 f e r t i l i z e r + u. The unobserved term, contains factors such as land quality rainfall temperature All of the above.

all the above

The term σ ^ is called the standard error of the regression. is called the root mean squared error. is calculated as ∑ i = 1 n u ^ i 2/ n − 2 Correct! All of the above.

all the above

The zero conditional mean assumption is comprised of E(u)=0 E(u|x)=E(u) u is mean independent of x All of the above.

all the above

To obtain the OLS estimates for the model y = β 0 + β 1 x + u, we require n^{-1} sum_{i=1}^n [y_i - \hat{\beta}_0-\hat\beta_1x_i ]=0n − 1 ∑ i = 1 n [ y i − β ^ 0 − β ^ 1 x i ] = 0 n^{-1} sum_{i=1}^n xi[yi - hat{beta}_0-\hat\beta_1x_i ]=0n − 1 ∑ i = 1 n x i [ y i − β ^ 0 − β ^ 1 x i ] = 0 LaTeX: \sum_{i=1}^n(x_i-\bar{x})^2>0∑ i = 1 n ( x i − x ¯ ) 2 > 0 Correct! All of the above.

all the above

When specifying an econometric model, the term u, represents unobserved factors. is called the error term. is called the disturbance term. all the above

all the above

Which of the following are true. 0 ≤ R 2 ≤ 1 R^2 = 1 − S S R /S S T SS T = S S E + S S R All of the above.

all the above

Which of the following is true about the R-squared: In the social sciences, low R-squareds in regression equations are not uncommon. A seemingly low R-squared does not necessarily mean that an OLS regression equation is useless. The ceteris paribus relationship between y and x does not depend directly on the size of R-squared. Students who are first learning econometrics tend to put too much weight on the size of the R-squared in evaluating regression equations. All of the options are correct.

all the above

Ordinary Least Squares (OLS) gets its name because we can obtain the OLS estimates by minimizing the sum of the residuals. by minimizing the sum of the squared residuals. by minimizing the sum of the absolute value of the residuals. by doing ordinary tasks that take the least amount of effort in square spaces.

by minimizing the sum of the squared residuals.

The phrase "other factors being equal" is known in Latin as magnum opus mens rea habeas corpus ceteris paribus

ceteris paribus

In the voting expenditure example, the R-squared was 0.856. Thus, the share of campaign ex-penditures explains over 0.856% of the variation in the election outcomes for this sample. True False

false

In a model relating a person's wage to observed education and other unobserved factors, wage = β 0 + β 1 e d u c + u, the zero conditional mean assumption likely holds. the zero conditional mean assumption does not hold because innate ability affects wage. the zero conditional mean assumption does not hold because innate ability is likely correlated with education and it affects wage. the zero conditional mean assumption does not hold because innate ability is independent of education.

the zero conditional mean assumption does not hold because innate ability is likely correlated with education and it affects wage

Because of the non-experimental nature of most data collected in the social sciences, uncovering causal relationships is ____________________. impossible. simple very challenging not important to economists.

very challenging