Economics Capstone Seminar Practice
Exam
Course Introduction
The Economics Capstone Seminar is a culminating course designed for senior students to integrate and apply the knowledge and analytical skills acquired throughout their economics studies. Through a combination of independent research, group discussions, and critical analysis of current economic issues, students engage with complex real-world problems. The seminar emphasizes the development of a substantive research project or thesis, effective communication of economic ideas, and the use of empirical and theoretical tools to evaluate policy and theoretical questions. This course also fosters collaboration, presentation skills, and intellectual independence, preparing students for professional careers or further study in economics and related fields.
Recommended Textbook
Practical Econometrics data collection analysis and application 1st Edition by Christiana E. Hilmer
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Chapter 1: An Introduction to Econometrics and Statistical
Inference
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Sample Questions
Q1) Sampling with replacement is
A)when a unit is drawn from a finite population,the value is recorded,and then returned to the sample.
B)a method that ensures the individuals in the sample are related to each other.
C)when a unit is drawn from a finite population,the value is recorded,and then not returned to the sample.
D)obtaining the same unit over and over when conducing a sample.
Answer: A
Q2) What is a population? A sample? How do the two combine to form the study of statistics? Explain.
Answer: A population is the entire group of entities that we are interested in learning about.A sample is a subset or part of the population and it is what is used to perform statistical inference.The relationship between the two is that the study of statistics aims to draw conclusions about a population of interest on the basis of observations about a particular sample drawn from the population.
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Chapter 2: Collection and Management of Data
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Q1) Why is data collection so important? Explain.
Answer: Analyzing data is the entire purpose of an empirical research project.After identifying a question of interest and developing the appropriate theory,it is vital to examine actual data to gain insight as to the extent to which observed behavior is consistent with the developed theory.For this process to be informative,you must be able to collect appropriate data.
Q2) Publicly-available data can be obtained through
A)formal request and/or having the appropriate connections.
B)personally conducting a survey asking people for information and recording their responses.
C)the internet or through formal Freedom of Information Act (FOIA)requests from the appropriate agency.
D)obtaining data from your work that cannot be shared with other individuals. Answer: C
Q3) Suppose you have identified a good question of interest and you expect appropriate data to be available on the internet.What is the first thing that you should do to locate such data? Explain.
Answer: Perform an internet search.These days,it is amazing how much data is publicly-available over the internet.
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Chapter 3: Summary Statistics
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Q1) a.If the correlation coefficient between two random variables equals zero,does that imply that there is no relationship between those random variables?
b.If the correlation coefficient between two random variables is close to one in absolute value,does that imply that one random variable causes another random variable?
Answer: a.No,the correlation coefficient only measures if there is a linear relationship between the two random variables.It could be that there is no relationship between the two variables but it could also be that the two variables are related to each other in some non-linear way.
b.A high correlation coefficient does not imply that one variable causes another variable.It could be that there is spurious correlation between the two variables in that they are related to a third random variable.
Q2) The covariance
A)measures the linear relationship between two random variables.
B)must fall between -1 and 1.
C)must be positive.
D)measures the quadratic relationship between two random variables. Answer: A
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Chapter 4: Simple Linear Regression
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Sample Questions
Q1) Simple linear regression analysis determines
A)the true value of the population slope coefficient.
B)the linear relationship between two random variables.
C)the linear relationship between many different random variables.
D)the true value of the population intercept.
Q2) In simple linear regression analysis,the dependent variable
A)is the variable that changes in response to changes in an independent variable.
B)is the variable whose changes affect the dependent variable.
C)is on the right-hand side variable.
D)can be either variable.
Q3) Why is an error term included in the estimated sample regression function? What factors does this term include? Explain.
Q4) In simple linear regression analysis,the independent variable is
A)the variable that changes in response to changes in an independent variable.
B)the variable whose changes affect the dependent variable.
C)the left-hand side variable.
D)can be either variable.
Q5) Why does Ordinary Least Squares (OLS)produce the "best-fit" line? Explain.
Q6) What is the goal of linear regression analysis? Explain.
Page 6
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Chapter 5: Hypothesis Testing in Linear Regression Analysis
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Q1) Based on the Excel output in Figure 5.1,you should conclude that the estimated slope coefficient is
A)statistically insignificant because the 95% confidence interval contains the estimated slope coefficient.
B)statistically insignificant because 5.138 > .05.
C)statistically insignificant because 2.39636<i>E</i>- 06 < .05.
D)statistically significant because 2.39636<i>E</i> - 06 < .05.
Q2) The confidence interval method for hypothesis testing (at the 95% level)relies on the fact that
A)95 percent of all sample statistics are correct.
B)the observed sample statistic is correct 95 percent of the time.
C)there is a 95 percent chance that the calculated confidence interval contains the true value of the unobserved population parameter.
D)95 percent of all confidence intervals that could be constructed contain the true value of the unobserved population parameter.
Q3) What is a sampling distribution? Why is it important? Explain.
Q4) What is the intuition behind the critical-value method of hypothesis testing? Explain.
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Chapter 6: Multiple Linear Regression Analysis
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Sample Questions
Q1) Based on the estimates in Figure 6.2,you should conclude that,holding all other independent variables constant,each additional year of age is estimated to be associated with
A)a statistically significant increase of 1.56 percent in cigarettes smoked per day.
B)a statistically insignificant increase of 1.56 percent in cigarettes smoked per day.
C)a statistically significant increase of 0.0156 cigarettes smoked per day.
D)a statistically insignificant increase of 0.0156 cigarettes smoked per day.
Q2) Chow tests are based on comparing the A)sum of the unexplained sums of squares added together from separate regressions to the unexplained sum of squares for the pooled regression. B)unexplained sums of squares from separate regressions to each other.
C)sum of the explained sums of squares from separate regressions to the explained sum of squares for the pooled regression.
D)explained sums of squares from separate regressions to each other.
Q3) How do you perform a Chow test for structural differences between two subsets of the data? What is the rejection rule? What is the intuition for why the test works? Explain.
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Chapter 7: Qualitative Variables and Non-Linearities in
Multiple Linear Regression Analysis
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Sample Questions
Q1) Suppose you estimate the sample regression function \( tes\widehat { t~ sco}re _ { i } = 56.78 + 12.36 \cdot \text { Age } _ { i } + 4.82 \cdot \text { Senior } _ { i }\)
You would conclude that,holding age constant,the average test score for seniors is A)4.82.
B)56.78.
C)61.60.
D)69.14.
Q2) Suppose you estimate the sample regression function \(tes\widehat { t~ sco}re _ { i }=61.13+9.42 \cdot \text { Age }_{i}+2.57 \cdot \text { Green }_{i}-6.32 \cdot \text { Brown }_{i}+1.48 \cdot \text { Other }_{i}\)
You should conclude that,when age is equal to zero,students with other eye colors average
A)61.13 points.
B)1.48 points.
C)62.61 points.
D)59.65 points.
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Chapter 8: Model Selection in Multiple Linear Regression Analysis
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Q1) Suppose that you estimate the sample regression function \(\log \text { Salary } _ { i } = 6.84 + .062 \text { Experience } _ { i } + .038 \text { Education } _ { i } - 8.538 \text { Blue Eyes } _ { i } + .019 \text { GPA } _ { i }\)
Employing the "eye test" you might suspect that the marginal effect of A)experience is estimated incorrectly because it does not seem reasonable that each additional year of experience increases salary by 6.2 percent,holding all other independent constant.
B)education is estimated incorrectly because it does not seem reasonable that each additional year of education increases salary by 3.8 percent,holding all other independent constant.
C)having Blue Eyes is estimated incorrectly because it does not seem reasonable that the average salary of individuals with Blue Eyes is 853.8 percent less than the average salary of individuals with other eye colors,holding all other independent constant. D)GPA is estimated incorrectly because it does not seem reasonable that each one unit increase in GPA increases salary by 1.9 percent,holding all other independent constant.
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Chapter 9: Heteroskedasticity
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Sample Questions
Q1) The second step in the Modified White's test is to regress the A)residuals on the predicted value of the dependent variable.
B)squared residuals on the predicted value of the dependent variable.
C)squared residuals on the predicted value of the dependent variable squared.
D)squared residuals on the predicted value of the dependent variable and the predicted value of the dependent variable squared.
Q2) The appropriate critical value for performing the Goldfeld-Quandt test at the 95% level is
A) \(F _ { n _ { 1 } - k _ { 1 } , n _ { 2 } - k _ { 2 } , .025 }\)
B) \(F _ { n _ { 1 } - k _ { 1 } , 05 }\)
C) \(F _ { n _ { 2 } - k _ { 2 } , 05 }\)
D) \(F _ { n _ { 1 } - k _ { 1 } , n _ { 2 } - k _ { 2 } , 05 }\)
Q3) The first step in the Modified White's test is to estimate the model and A)calculate the predicted values of the dependent variable.
B)calculate the estimated residuals.
C)calculate the estimated residuals and the predicted values of the dependent variable.
D)plot the estimated residuals.
Q4) What is the intuition behind the Breusch-Pagan test for heteroskedasticity? Explain.
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Chapter 10: Time Series Analysis
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Sample Questions
Q1) You can control for quarterly seasonality in your data by
A)de-trending your time-series data.
B)including a time-trend in your regression.
C)including binary dummy variables for all four quarters in your regression.
D)including binary dummy variables for three of the four quarters in your regression.
Q2) A structural break occurs when we see
A)an unexpected shift in time-series data.
B)a number of outliers in cross-section data.
C)a general upward trend over time in time-series data.
D)an independent variable is correlated with the dependent variable but there is no theoretical justification on for the relationship.
Q3) What are the assumptions required for OLS to be BLUE for time-series data? Write out and explain each.
Q4) A static time-series model _______ for the time dependence of the data.
A)is estimated by OLS with no controls
B)includes lagged dependent variables to control
C)includes lagged independent variables to control
D)includes weighted residuals to control
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Chapter 11: Auto-Correlation
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Q1) The third step in the first iteration of the Cochrane-Orcutt method for AR(1)is to
A)use the estimate of \(\hat { \rho }\) to adjust the dependent variable.
B)use the estimate of \(\hat { \rho }\) to adjust the independent variables.
C)use the estimate of \(\hat { \rho }\) to adjust the dependent variable and the independent variables.
D)use the estimate of \(\hat { \rho } _ { 1 }\) and \(\hat { \rho } _ { 2 }\) to adjust the dependent variable and the independent variables.
Q2) The third step of the Regression test for AR(1)is to estimate the model
A) \(e _ { t } = \rho e _ { t - 1 } + u _ { t }\)
B) \(e _ { t - 1 } = \rho e _ { t } + u _ { t }\)
C) \(e _ { t } = \rho _ { 1 } e _ { t - 1 } + \rho _ { 2 } e _ { t - 2 } + u _ { t }\)
D) \(y _ { t } = \rho x _ { t } + u _ { t }\)
Q3) Newey-West robust standard errors
A)the preferred method for correcting for potential autocorrelation.
B)calculated through an iterative process.
C)automatically calculated in Excel.
D)the result of performing the Prais-Winsten method.
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Chapter 12: Limited Dependent Variables
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Q1) The coefficient estimates from the multinomial logit model are
A)non-negative.
B)not true marginal effects.
C)true marginal effects.
D)biased.
Q2) The coefficient estimates from the logit model
A)indicate the estimated marginal effects that the independent variables have on the dependent variable.
B)indicate the general degree to which the independent variables are correlated with the dependent variable.
C)are constrained to be positive.
D)must fall between 0 and 1.
Q3) What do the coefficient estimates from the logit model represent? What do you need to do to determine true estimated marginal effects? Explain.
Q4) What is a binary dependent variable? Why does it present a challenge to estimating the population multiple linear regression model by OLS? Explain.
Q5) What is a multinomial logit model? Why is it more appropriate than OLS? Explain.
Q6) What is a logit model? Why is it more appropriate than OLS? Explain.
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Chapter 13: Panel Data
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Q1) Fixed-effects models and first-differenced models
A)assume that the time-invariant component of the error term is not correlated with the independent variables.
B)both add dummy variables to control for individual observations.
C)do not provide identical results if there are more than two years of data.
D)do not completely remove the time-invariant component of the error term.
Q2) Panel data differs from cross-section data in that panel data is observed
A)for a number of different individuals in a given time-period.
B)for a given individual in a number of different time-periods.
C)for a given individual in a given time-period.
D)for a number of different individuals in a number of different time-periods.
Q3) Fixed-effects models
A)remove the time-variant component of the error term.
B)remove the time-invariant component of the error term.
C)work by first-differencing the data.
D)remove the heteroskedasticity in the data.
Q4) What is panel data? How does it differ from cross-section data? Time-series data? Explain.
Q5) What is a first-differenced model? How is it preferable to a pooled cross-section model? Explain.
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Chapter 14: Instrumental Variables for Simultaneous
Equations, Endogenous Independent Variables, and
Measurement Error
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Q1) For an instrument to be valid it must be
A)correlated with both the dependent variable and the endogenous variable.
B)uncorrelated with both the dependent variable and the endogenous variable.
C)correlated with the endogenous variable but uncorrelated with the error term.
D)correlated with the error term but uncorrelated with the endogenous variable.
Q2) Measurement error in an independent variable occurs when
A)the dependent variable is uncorrelated with the error term.
B)the true value of the independent variable is observed.
C)the true value of the left hand side variable is not known.
D)the variable \(x _ { 1 i }\) is measured with error such that \(x _ { 1 i } = x _
}\) is the true value of the variable.
Q3) Endogeneity of an independent variable presents a challenge because A)OLS estimates are biased and inconsistent.
B)it causes the regression to be perfectly collinear.
C)it leads to heteroskedasticity.
D)OLS estimates remained unbiased but they are no longer BLUE.
Page 16
Q4) What is measurement error in an independent variable? How can instrumental variables be used to control for measurement error in independent variables? Why? Explain.
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Chapter 15: Quantile Regression, Count Data, Sample
Selection Bias, and Quasi-Experimental Methods
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Q1) Suppose you are interested in explaining the number of surfers surfing at your favorite spot on a given day.After collecting data on the number of surfers,the height of the waves (in feet),the water temperature,and whether the day was a weekend on a sample of 92 days,you estimate the following marginal effects for the Poisson model \[\begin{array} { lll l} \text { Surfers } _ { i } = & 13.653 + 6.029 \text { Height } _ { i } +& 0.596 \text { Temperature } _ { i } +& 4.076 \text { Weekend } _ { i } \\ &( 3.824 ) ( 2.137 ) & ( 0.202 ) & ( 1.382 ) \end{array}\]
a)Why is OLS not appropriate in this circumstance? How does a Poisson model improve on OLS?
b)Discuss the results.
c)What assumption is necessary for Poisson to be the appropriate model? How would you test this assumption? If this assumption fails,what alternative model could you estimate?
Q2) What is sample-selection bias? Why does it present a problem for OLS? How can you control for its presence? Explain.
Q3) What is quantile regression? When might it be preferred to OLS? Explain.
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