Advanced Econometrics
Test Preparation
Course Introduction
Advanced Econometrics is a higher-level course focused on equipping students with sophisticated quantitative techniques for empirical economic analysis. Building on foundational econometric methods, this course delves into topics such as instrumental variables, panel data analysis, limited dependent variable models, time series econometrics, and advanced estimation techniques. Emphasis is placed on the application of these methods to real-world economic data, model specification, identification, and inference. Students gain hands-on experience through practical exercises, allowing them to critically assess econometric studies and conduct independent research using advanced tools.
Recommended Textbook
Practical Econometrics data collection analysis and application 1st Edition by Christiana E. Hilmer
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507 Verified Questions
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2
Chapter 1: An Introduction to Econometrics and Statistical
Inference
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Sample Questions
Q1) A point estimate is
A)typically equal to the population parameter.
B)single valued statistic that is the best guess of a population parameter.
C)an interval that contains the population parameter.
D)computed from the population.
Answer: B
Q2) What is a sampling distribution? How do you construct one? Why is it important to understand what a sampling distribution is? Explain.
Answer: A sampling distribution is the distribution of a sample statistic,such as the sample mean.It is constructed by drawing every possible sample of size n from the population of size N,calculating the value of a sample statistic for each sample,and placing all of those calculated values in order on a number line.It is important to understand this definition because statistics and hypothesis testing is based on principles learned from our knowledge of what sampling distributions look like.
Q3) What is a statistic? A parameter? How are the two related? Explain.
Answer: A statistic is a function that is computed from the sample data.A parameter is a function that exists within the population.A sample statistic serves as a point estimate of the likely value of an unobserved parameter.
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Chapter 2: Collection and Management of Data
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Sample Questions
Q1) Why do we recommend saving one master file while performing calculations in another file? Explain.
Answer: We recommend saving one master file so that if we make a mistake and overwrite or otherwise change our data,we can easily go back and reconstruct our correct data without having to start at square one with our internet search,data downloading,and so on.
Q2) Suppose you have identified a good question of interest but you cannot locate appropriate data on the internet.What could you do to collect appropriate data? Explain. Answer: You could perform your own survey using an internet site such as surveymonkey.com.
Q3) Why is it potentially valuable to perform calculations in a new worksheet? Explain. Answer: Calculations often require changing the values of existing data.Unfortunately,when making such changes,it is possible to change the values of the initial data and when doing so,it is possible to make mistakes that cannot be easily undone.If we make such mistakes in a new worksheet,we can easily go back to the initial data and start over again,making sure not to make the same calculation mistake again.
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Page 4
Chapter 3: Summary Statistics
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Sample Questions
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) 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.
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Page 5
Chapter 4: Simple Linear Regression
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Sample Questions
Q1) A probabilistic relationship is one for which
A)the value of the dependent variable is perfectly determined by the value of the independent variable.
B)all data points fall on the same line.
C)there is no random error component.
D)the value of the dependent variable is related to the value of the independent variable but the data points do not fall on the same line.
Q2) The explained variation in y is
A)the distance between the best-fit line and the data points.
B)the distance between the mean and the predicted value of y.
C)the distance between the mean and the data points.
D)the distance between the observed and predicted values of y.
Q3) The estimated intercept
A)is the estimated marginal effect of x on y.
B)is the estimated value of y when x equals 0.
C)is equal to the population intercept.
D)is \(\hat { \beta } _ { 1 }\) .
Q4) What are the Explained Sum of Squares (ESS),the Unexplained Sum of Squares (USS),and the Total Sum of Squares (TSS)? Explain.
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Chapter 5: Hypothesis Testing in Linear Regression Analysis
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Sample Questions
Q1) Based on the Excel output in Figure 5.1,the appropriate critical value for 95% confidence is
A)1.96.
B)t<sub>.05,70</sub> = 1.67.
C)1.645.
D)t<sub>.025,70</sub> = 2.00.
Q2) Why is hypothesis testing is necessary? Explain.
Q3) An estimator is unbiased if
A)the standard error is small.
B)the expected value of the estimator is equal to the true population parameter.
C)the <i>p</i>-value is less than .05.
D)the expected value of the estimator is equal to the sample statistic.
Q4) 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.
Q5) What is the intuition behind the p-value method of hypothesis testing? Explain.
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Chapter 6: Multiple Linear Regression Analysis
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Sample Questions
Q1) How do you perform a test of the joint significance of a subset of slope coefficients? What are the null and alternative hypothesis for this test? What is the rejection rule? What is the intuition for why the test works? Explain.
Q2) Suppose you are estimating salary as a function of age,education,hours of work and the number of young children and you are concerned that the salary functions differ for men and women.You could test this possibility by performing a A)t-test for individual significance.
B)t-test for joint significance.
C)Chow test.
D)subset test.
Q3) Based on the estimates in Figure 6.2,you should conclude that,holding all other independent variables constant,each additional family member is estimated to be associated with
A)a statistically significant decrease of 0.093 cigarettes smoked per day.
B)a statistically insignificant decrease of 0.093 cigarettes smoked per day.
C)a statistically significant increase of 0.093 cigarettes smoked per day.
D)a statistically insignificant increase of 0.093 cigarettes smoked per day.
Q4) 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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Sample Questions
Q1) Suppose you estimate the sample regression function \[\begin{array} { l } viol\widehat {ent~c}rime _ { i } = 7.23 - 4.87 \cdot \text { Suburb } _ { i } + 6.11 \cdot \text { Unemployment } _ { i } \\ - 1.30 \cdot ( \text { Suburb* Unemployment } ) _ { i } \\ \end{array}\]
You should conclude that the marginal effect of unemployment on violent crime in suburbs is A)-4.87.
B)6.11. C)-1.30. D)4.81.
Q2) Suppose you estimate the sample multiple linear regression function \(\hat
i } = \hat { \beta } _ { 0
^ { 2 }\)
+ \hat { \beta } _
What is the estimated marginal effect of x<sub>1</sub>? Explain.
Q3) When is it appropriate to include quadratic terms in a multiple linear regression? Provide an example and discuss the quadratic term that you would include and how the relevant estimates would be correctly interpreted.
Page 9
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Chapter 8: Model Selection in Multiple Linear Regression Analysis
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Sample Questions
Q1) One can deal with missing data by
A)making up values for missing data.
B)creating a dummy variable for observations with non-missing values,setting the missing value equal to 1,and including the dummy variable in the regression analysis.
C)creating a dummy variable for observations with missing values,setting the missing values equal to 0,and including the dummy variable in the regression analysis.
D)creating a dummy variable for observations with missing values,setting the missing value equal to 1,and including the dummy variable in the regression analysis.
Q2) What is omitted variable bias? Why is it a problem? How do you try to prevent it? Explain.
Q3) Omitted variable bias occurs when one does not include
A)an independent variable that is correlated with the dependent variable only.
B)an independent variable that is correlated with the dependent variable and an included independent variable.
C)an independent variable that is correlated with an included independent variable only.
D)a dependent variable that is correlated with an included independent variable.
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Page 10
Chapter 9: Heteroskedasticity
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Sample Questions
Q1) Heteroskedasticity violates assumption
A)M3.
B)M4.
C)M5.
D)M6.
Q2) How do you perform the Breusch-Pagan test for heteroskedasticity? Explain.
Q3) The final step in the Breusch-Pagan test is to perform
A)an <i>F</i>-test of the overall significance of the squared residuals regression. B)an <i>F</i>-test of the overall significance of the original OLS regression.
C)a <i>t</i>-test of the overall significance of the squared residuals regression.
D)an <i>F</i>-test of the individual significance of the suspect independent variable in the squared residuals regression.
Q4) What is heteroskedasticity? Why is it problematic? Explain.
Q5) The alternative 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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Sample Questions
Q1) Seasonality occurs when
A)time-series data move upward or downward over time.
B)time-series data follows similar patterns during specific seasons of the year.
C)an unexpected shift occurs in time-series data.
D)an independent variable is correlated with the dependent variable but there is no theoretical justification for the relationship.
Q2) How does time-series data differ from cross-section data? Why is this difference important? Explain.
Q3) What is a structural break in the data? Why is it important to control for a structural break if one occurs? How can you test and control for a structural break in your data? Explain.
Q4) What is forecasting? How do you perform it? How can you test the potential validity of your forecasts? Explain.
Q5) Time-series data are data collected for a
A)given individual or individuals for a number of different time-periods.
B)given individual for one time-period.
C)number of individuals for one time-period.
D)number of individuals for a number of different time-periods.
Q6) What does it mean to de-trend your data? How can you do so? Explain.
Page 12
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Chapter 11: Auto-Correlation
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Sample Questions
Q1) You can determine whether a unit root exists by performing A)a Newey-West test.
B)a Cochrane-Orcutt test.
C)a Prais-Winsten test.
D)a Dickey-Fuller test.
Q2) Write out the model for an AR(1)process.Explain what it means.Repeat for an AR(2)process.
Q3) An AR(2)process is written as
A) \(\varepsilon _ { t } = \rho \varepsilon _ { t - 1 } + u _ { t }\)
B) \(\varepsilon _ { i } = \rho \varepsilon _ { i - 1 } + u _ { i }\)
C) \(\varepsilon _ { t } = \rho _ { 1 } \varepsilon _ { t - 1 } + \rho _ { 2 } \varepsilon _ { t - 2 } + u _ { t }\)
D) \(\varepsilon _ { i } = \rho _ { 1 } \varepsilon _ { i - 1 } + \rho _ { 2 } \varepsilon _ { i - 2 } + u _ { i } .\)
Q4) Iterations in the Cochrane-Orcutt method for AR(1)should be continued until
A) \(\hat { \rho } = 0 .\)
B) \(\hat { \rho } = \rho .\)
C) \(\hat { \rho }\) changes by .05 between iterations.
D) \(\hat { \rho }\) is stable between iterations.
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Chapter 12: Limited Dependent Variables
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Sample Questions
Q1) Suppose you are interested in explaining Student Evaluations of Teaching (which are calculated on the following 4-point scale: (1)Poor, (2)Fair, (3)Good,and (4)Excellent)in a sample of 15,239 college students and that using the multinomial logit you obtain the following coefficient estimates (standard errors in parentheses) \[\begin{array} { l l l lll }
\widehat { S E T _ { i } } = &2.48 +& 0.97 \text { Avg. GPA } _ { i } - &1.52 \% \text { Freshmen } _ { i } - &0.24 \% \text { Male } _ { i } +& 2.04 \text { \% Majors } \\
&( 0.83 ) &( 0.45 ) & ( 0.66 ) & ( 0.15 ) & ( 0.77 )
\end{array}\]
a)How should you interpret the estimated coefficient on Average GPA? Does this make economic sense? Explain.
b)How should you interpret the estimated coefficient on % Freshmen? Does this make economic sense? Explain.
c)Do these estimates provide all of the information that you desire? If not,how can you obtain that information? Explain.
Q2) What is a multinomial logit model? Why is it more appropriate than OLS? Explain.
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14
Chapter 13: Panel Data
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Sample Questions
Q1) Fixed-effects models are more appropriate than random-effects models when A)the error terms are autocorrelated.
B)the independent variables are constant over time.
C)the time invariant component of the error term is correlated with any of the independent variables.
D)the error term is not constant.
Q2) Suppose that in an effort to explain variation in wages,you collect a panel data set with 12,300 total observations over 3 different years with the independent variables as experience,education,married (dummy variable is 1 if married and 0 if not married)and male (dummy variable is 1 if male and 0 if female).
a)How do you estimate a random-effects model for these data? Explain in as much detail as possible.
b)What are the advantages of estimating a random-effects model?
c)What are the drawbacks of estimating a random-effects model for this scenario?
Q3) Random-effects models improve on pooled cross-section models because they
A)do not take advantage of the panel data nature of the data.
B)have more degrees of freedom available.
C)improve the efficiency of the estimated coefficients.
D)correct for heteroskedasticity.
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Page 15
Chapter 14: Instrumental Variables for Simultaneous
Equations, Endogenous Independent Variables, and
Measurement Error
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Sample Questions
Q1) The first-stage in an instrumental variable approach to control for measurement error in an independent variable is
A)regressing the mis-measured variable on only the exogenous variables in the original regression equation and using those estimates to calculate predicted values of the mis-measured variable.
B)regressing the mis-measured variable on only the alternative value of the mis-measured variable.
C)estimating the original population regression model with the predicted values of the mis-measured variable substituted for the observed values of the endogenous variable.
D)regressing the mis-measured variable on both the alternative measure of the mis-measured variable and all of the remaining exogenous variables and using those estimates to calculate predicted values of the mis-measured variable.
Q2) Measurement error in an independent variable presents a challenge because
A)the independent variable is not correlated with the dependent variable.
B)the OLS estimates are biased.
C)the OLS estimates are unbiased but no longer BLUE.
D)the independent variable is not correlated with the error term.
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Chapter 15: Quantile Regression, Count Data, Sample
Selection Bias, and Quasi-Experimental Methods
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Sample Questions
Q1) Difference-in-difference estimators attempt to
A)attempt to replicate randomized clinical trials by comparing treatment and control groups before and after a treatment is imposed to estimate the impact of a given policy intervention.
B)take differences of both the dependent and independent variables.
C)only difference the dependent variables and regress the differences on the independent variables.
D)obtain estimates at points in the distribution of the dependent variable aside from the mean.
Q2) You can control for sample-selection bias by performing
A)two-stage least squares.
B)weighted least squares.
C)a Heckman selection correction.
D)difference-in-difference estimation.
Q3) What is quantile regression? When might it be preferred to OLS? Explain.
Q4) Non-negative count data presents a challenge because OLS estimates
A)are biased.
B)cannot be calculated.
C)are heteroskedastic.
D)are the BLUE.
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