Introduction to Econometrics
Textbook Exam Questions
Course Introduction
Introduction to Econometrics offers students a comprehensive foundation in the methods and application of statistical techniques to economic data. The course covers key concepts such as regression analysis, hypothesis testing, and model specification, enabling students to analyze real-world economic problems and interpret empirical results. By learning how to estimate economic relationships and test economic theories using data, students gain critical skills necessary for research, policy evaluation, and informed decision making in both academic and professional contexts.
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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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Q1) What is an empirical research project? Why is it potentially valuable? Explain.
Answer: An empirical research project is a project that applies empirical analysis to observed data to provide insight into questions of theoretical interest.They are potentially valuable because economic theories lack value unless they are proven to be consistent with observed human behavior:empirical research projects attempt to determine whether observed behavior is consistent with economic theory.
Q2) In econometrics,a population is
A)a large number of observations drawn from a larger number of observations.
B)typically the total number of people in a city or country.
C)a subset or part of the population and it is what is used to perform statistical inference.
D)the entire group of entities that we are interested in learning about.
Answer: D
Q3) A sampling distribution is
A)the distribution of a sample statistic such as the sample mean.
B)the normal distribution.
C)constant.
D)equal to the distribution of an individual in the sample.
Answer: A
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Chapter 2: Collection and Management of Data
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Q1) Time-series data is data collected
A)without replacement.
B)for a given individual,country,firm,etc.over many different time periods.
C)for a number of individuals,countries,firms,etc.over many different time periods.
D)for many different individuals,countries,firms,etc.in a given time-period.
Answer: B
Q2) Personal survey data can be obtained through
A)the internet or through formal Freedom of Information Act (FOIA)requests from the appropriate agency.
B)data that is available through the Bureau of Labor Statistics or similar agency.
C)formal request and/or having the appropriate connections.
D)personally conducting a survey asking people for information and recording their responses.
Answer: D
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Chapter 3: Summary Statistics
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Q1) When is the median most appropriate for summarizing a data set?
A)Always.
B)When the data set contains outliers.
C)When the data set is symmetric.
D)When the data are closely distributed to each other.
Answer: B
Q2) If a data set is skewed then
A)the mean is the most appropriate measure of central tendency.
B)the mean is the most appropriate measure of dispersion.
C)the median is the most appropriate measure of central tendency.
D)the median is the most appropriate measure of dispersion.
Answer: C
Q3) The median is the
A)middle number in an ordered data set.
B)most frequently observed value in a data set.
C)sum of the individual observation divided by the number of observations.
D)only relevant measure of central tendency.
Answer: A
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Chapter 4: Simple Linear Regression
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Q1) In general,a larger R<sup>2</sup> tends to suggest that
A)the estimated sample regression function explains a greater percentage of the total variation in y.
B)the estimated sample regression function is more accurate.
C)the estimated sample regression function explains a greater percentage of the explained variation in y.
D)the estimated slope coefficient is more likely to equal the population slope coefficient.
Q2) Suppose you determine the estimated sample regression function to be \(\hat { y } _ { i } = 1,016.82 + 473.65 \cdot x _ { i } \text {. }\)
You would conclude that
A)y<sub>i</sub> is estimated to equal 473.65 when x<sub>i</sub> = 0.
B)y<sub>i</sub> is estimated to increase by 473.65 for every one unit increase in x<sub>i</sub>.
C)y<sub>i</sub> is estimated to decrease by 473.65 for every one unit increase in x<sub>i</sub>.
D)y<sub>i</sub> is estimated to increase by 1,016.82 for every one unit increase in x<sub>i</sub>.
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Chapter 5: Hypothesis Testing in Linear Regression Analysis
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Q1) A confidence interval is constructed
A)to bracket the sample mean.
B)to bracket the sample statistic.
C)to bracket the population parameter.
D)to bracket the margin of error.
Q2) The <i>t</i>-statistic for the individual significance of the estimated slope coefficient\(\hat { \beta } _ { 1 }\)Is
A)the standard error of the estimated slope coefficient.
B)the estimated slope coefficient.
C)the ratio of the estimated slope coefficient to the standard error of the estimated slope coefficient.
D)the ratio of the estimated slope coefficient to the variance of the estimated slope coefficient.
Q3) If we find that it is unlikely to observe the sample statistic that is actually observed if the null hypothesis is true,then we should
A)fail to reject the null hypothesis.
B)reject the null hypothesis.
C)reject the alternative hypothesis.
D)calculate a new sample statistic.
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Chapter 6: Multiple Linear Regression Analysis
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Q1) Based on the estimates in Figure 6.1,you should conclude that,holding all other independent variables constant,a $1,000 increase in GDP Per Capita is estimated to
A)increase Total Medals Won by a statistically significant 0.2865 because 0 is not included in the 95% confidence interval.
B)increase Total Medals Won by a statistically insignificant 0.2865 because 0 is included in the 95% confidence interval.
C)increase Total Medals Won by a statistically insignificant 0.2865 because 0.2865 is included in the 95% confidence interval.
D)increase Total Medals Won by a statistically significant 0.2865 because 0.2865 is included in the 95% confidence interval.
Q2) The appropriate critical value for the Chow test in Figure 6.4 is
A)1.927.
B)2.032.
C)2.463.
D)2.978.
Q3) Why is the "all other independent variables constant" condition important? Explain.
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Chapter 7: Qualitative Variables and Non-Linearities in
Multiple Linear Regression Analysis
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Q1) Suppose you wish to estimate the elasticity between solar power systems sales and the tax on solar power systems using state-level data on solar system sales and solar system taxes.You could estimate this effect by regressing the
A)total number of solar systems sold on solar system tax rates.
B)total number of solar systems sold on the log of solar system tax rates.
C)log of the total number of solar systems sold on the log of solar system tax rates. D)log of the total number of solar systems sold on the solar system tax rate.
Q2) Suppose you estimate the sample regression function \[ Midt\widehat {erm ~Sc}ore = 62.33 + 4.62 \cdot \text { Time Taken } - 0.038 \cdot \text { Time Taken } { } ^ { 2 }\]
You should conclude that the marginal effect of time taken on midterm score is A)4.62.
B)-0.038.
C)4.62 - 0.038.
D)4.62 - 0.076 Time Taken.
Q3) When is it appropriate to estimate log-log multiple linear regression models? Provide an example and discuss how the relevant estimates would be correctly interpreted.
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Chapter 8: Model Selection in Multiple Linear Regression Analysis
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Q1) What is the potential shortcoming of using a strict cutoff to determine statistical significance? Explain.
Q2) One can deal with missing data by
A)dropping observations with missing data if the missing data is random and therefore will not affect the resulting coefficient estimates.
B)making up values for missing data.
C)ignoring the potential impact of the missing data in the analysis.
D)running a regression with the number 6 put in lieu of the missing data.
Q3) The Eye test is used to
A)test for the inclusion of higher-order polynomials.
B)test for choosing between non-nested models.
C)critically assess the regression results instead of taking all results at face value.
D)test for omitted variable bias.
Q4) What is an outlier? Why are they a potential problem? What can you do to deal with outliers? Why does this approach work? Explain.
Q5) Why is missing data a potential problem? What are two ways to deal with it? Which approach do you prefer and why?
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Chapter 9: Heteroskedasticity
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Q1) What is the intuition behind the Goldfeld-Quandt test for heteroskedasticity? Explain.
Q2) The second step in the Goldfeld-Quandt test is to
A)omit the middle c observations from the ordered data set.
B)omit the bottom c observations from the ordered data set.
C)regress the squared residuals on the independent variables from the original OLS regression.
D)regress the squared residuals on the predicted value of the dependent variable from the original OLS regression.
Q3) What is heteroskedasticity? Why is it problematic? Explain.
Q4) Heteroskedasticity is a problem because it results in
A)biased parameter estimates.
B)estimated standard errors that are incorrect.
C)estimated standard errors that are always too small.
D)incorrect estimated slope coefficients.
Q5) The null hypothesis for the Goldfeld-Quandt test is
A) \(H _ { 0 } : U S S _ { 2 } / U S S _ { 1 } = 0\)
B) \(H _ { 0 } : U S S _ { 2 } / U S S _ { 1 } \neq 0\)
C) \(H _ { 0 } : U S S _ { 2 } / U S S _ { 1 } = 1\)
D) \(H _ { 0 } : U S S _ { 2 } / U S S _ { 1 } \neq 1\)
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Chapter 10: Time Series Analysis
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Q1) Suppose you wish to estimate the marginal effect that income has on consumption.The distributed lag model would be specified as
A) \(\text { Consumption } _ { i } = \beta _ { 0 } + \beta _ { 1 } \text { Income } _ { i } + \beta _ { 2 } \text { Income } _ { i - 1 } + \varepsilon _ { t } \text {. }\)
B) \(\text { Consumption } _ { t } = \beta _ { 0 } + \beta _ { 1 } \text { Income } _ { t } + \varepsilon _ { t }\)
C) \(\text { Consumption } _ { i } = \beta _ { 0 } + \beta _ { 1 } \text { Income } _ { i } + \varepsilon _ { i } \text {. }\)
D) \(\text { Consumption } _ { t } = \beta _ { 0 } + \beta _ { 1 } \text { Income } _ { t } + \beta _ { 2 } \text { Income } _ { t - 1 } + \varepsilon _ { t } \text {. }\)
Q2) What is a distributed lag model? Why is it preferable to a static time-series model? Explain.
Q3) What does it mean for a time-series to be weakly dependent? Why is this desirable? Explain.
Q4) How does time-series data differ from cross-section data? Why is this difference important? Explain.
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Chapter 11: Auto-Correlation
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Q1) If positive autocorrelation is not present,then the Durbin-Watson test statistic will be
A)closer to 0.
B)closer to 2.
C)closer to 4.
D)greater than 3.
Q2) How do you perform the Regression test for AR(1)? Explain.
Q3) Autocorrelation violates the time-series assumption
A)T3.
B)T4.
C)T5.
D)T6.
Q4) Write out the model for an AR(1)process.Explain what it means.Repeat for an AR(2)process.
Q5) 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) \(y _ { t } = \rho x _ { t } + u _ { t }\)
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Chapter 12: Limited Dependent Variables
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Q1) What do the coefficient estimates from the multinomial logit model represent? What do you need to do to determine true estimated marginal effects? Explain.
Q2) What do the coefficient estimates from the logit model represent? What do you need to do to determine true estimated marginal effects? Explain.
Q3) A binary dependent variable is one that takes on
A)continuous values.
B)only the values 0 and 1.
C)only integer values.
D)only non-negative values.
Q4) The coefficient estimates from the probit 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.
Q5) What is a multinomial logit model? Why is it more appropriate than OLS? Explain.
Q6) What is a probit model? Why is it more appropriate than OLS? Explain.
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Chapter 13: Panel Data
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Q1) Pooled cross-section models are not the preferred estimators for panel data models because they
A)are heteroskedastic.
B)are quasi-differenced.
C)do nothing to account for the time constant component of the error term.
D)create a dummy variable for each individual in the data set.
Q2) What is a random-effects model? How do you estimate such a model? When is it a preferred estimator? Why? Explain.
Q3) What does the error term look like for panel data? Explain each term in detail.
Q4) Random-effects models are
A)almost never the appropriate model in economics because the unobserved heterogeneity is typically correlated with the independent variables.
B)typically more appropriate than fixed effects models in economics.
C)preferred when the error term is random.
D)difficult to interpret because the coefficient estimates are not true marginal effects.
Q5) What is a pooled cross-section model? For what type of data would you estimate such a model? Are there any potential empirical issues associated with this approach? Explain.
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Chapter 14: Instrumental Variables for Simultaneous
Equations, Endogenous Independent Variables, and
Measurement Error
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Q1) The second-stage in an instrumental variable approach to controlling for endogenous independent variables is to
A)regress the endogenous independent variable on the instrument and all of the remaining independent variables and using those estimates to calculate predicted values of the endogenous variable.
B)estimate the original population regression model with the residuals included as an independent variable.
C)estimate the original population regression model with the predicted values of the endogenous right hand side variable substituted for the observed values of the endogenous right hand side variables.
D)regress the endogenous variable on the instrument and all of the remaining independent variables and using those estimates to calculate the residuals.
Q2) What is measurement error in an independent variable? How can instrumental variables be used to control for measurement error in independent variables? Why? Explain.
Q3) What are simultaneous equations? How can instrumental variables be used to control for simultaneous equations? Why? Explain.
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Chapter 15: Quantile Regression, Count Data, Sample
Selection Bias, and Quasi-Experimental Methods
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Q1) The second-stage in the Heckman selection correction is including the _____ in the second-stage regression to control for the potential sample-selection bias.
A)estimated residuals
B)calculated inverse Mills ratios
C)predicted value of the dependent variable
D)predicted value of the self-selection variable
Q2) Suppose you wish to determine factors affecting the number of surfers observed surfing at a given surf spot,an appropriate model to estimate the model would be A)OLS.
B)the logit.
C)the ordered probit.
D)the Poisson model.
Q3) What is a difference-in-difference estimator? When is it appropriate to use one? How do you do so? Explain.
Q4) What is non-negative count data? Why does it present a concern for OLS? How might you control for non-negative count data in the estimation process? Explain.
Q5) What is quantile regression? When might it be preferred to OLS? Explain.
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