Data Analysis for Economics Practice Questions
Course Introduction
This course introduces students to essential data analysis techniques used in the field of economics. It covers the collection, cleaning, and interpretation of economic data, as well as the application of statistical tools and software to analyze real-world economic problems. Students will explore topics such as hypothesis testing, regression analysis, and visualization of economic trends, developing practical skills for drawing meaningful conclusions from data and supporting economic decision-making. The course emphasizes hands-on experience with datasets and equips students with the analytical skills necessary for both academic research and professional practice in economics.
Recommended Textbook
Practical Econometrics data collection analysis and application 1st Edition by Christiana E. Hilmer
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507 Verified Questions
507 Flashcards
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Chapter 1: An Introduction to Econometrics and Statistical
Inference
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Sample Questions
Q1) A parameter is
A)a value that fluctuates depending on the sample it is computed from.
B)a value that has constant variance.
C)a function that is computed from the sample data.
D)a function that exists within the population.
Answer: D
Q2) 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
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) 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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Page 4
Chapter 3: Summary Statistics
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Sample Questions
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 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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Sample Questions
Q1) How do you calculate the estimated simple linear sample regression function by Ordinary Least Squares (OLS)? Explain.
Q2) Suppose you are given the Excel output in 4.1.You would conclude that number of degrees of freedom of the regression is A)1.
B)199.
C)200. D)201.
Q3) 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.
Q4) The number of degrees of freedom
A)is always n - k - 1.
B)is always n - 1.
C)is the number of values in the final calculation that are free to vary.
D)is the number of values in the final calculation that are not free to vary.
Q5) Why does Ordinary Least Squares (OLS)produce the "best-fit" line? Explain.
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Chapter 5: Hypothesis Testing in Linear Regression Analysis
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Sample Questions
Q1) What is a sampling distribution? Why is it important? Explain.
Q2) What is the intuition behind the confidence interval method of hypothesis testing? Explain.
Q3) A standard error is
A)the variance of the sampling distribution.
B)the standard deviation of the population distribution.
C)the standard deviation of the sampling distribution.
D)the variance of the population distribution.
Q4) What is the intuition behind the critical-value method of hypothesis testing? Explain.
Q5) The logic behind the <i>F</i>-test for the overall significance of the estimated sample regression function is that if the estimated sample regression function explains a significant amount of the variation in the dependent variable,then
A)the regression <i>F</i>-statistic should be small.
B)the MSUnexplained should be large relative to the MSExplained.
C)the MSExplained should be large relative to the MSUnexplained.
D)total sum of squares should be large.
Q6) What is the intuition behind the p-value method of hypothesis testing? Explain.
Q7) Why is hypothesis testing is necessary? Explain.
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Chapter 6: Multiple Linear Regression Analysis
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Sample Questions
Q1) A researcher is interested in estimating how the test scores of elementary schools is related to average class size,parents education level (in years),and percent of English learners at the school.The researcher obtains a sample of 200 California public schools and obtains the following results with standard errors are in parentheses \(\hat {Score} = 332 - 3\) Class Size \(+ 40\) Education Level \(- 10\) Percent English Learners \(~~~~~~~~~~~~~~~~~~\)(45) (.78)\(~~~~~~~~~~~~~~~~~~\)(14)\(~~~~~~~~~~~~~~~~~~\)(2.5) \(\begin{array} { l }
\mathrm { R } ^ { 2 } = .1437 \\ \mathrm { n } = 200 \end{array}\)
a)The F-statistic for this regression is 48.32.Test the overall significance of the regression model at the 5% level.What are the hypothesis,critical value,rejection rule and decision. b)Perform a t-test of for the individual significance for the slopes.
c)Comment on the economic significance of the coefficient estimates.
d)If you are interested in sending your child to a school with high test scores what would factors would you look for?
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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) 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.
Q3) Suppose you estimate the sample multiple linear regression function \(\hat { y } _ { i } = \hat { \beta } _ { 0 } + \hat { \beta } _ { 1 } x _ { 1 i } + \hat { \beta } _ { 2 } x _ { 1 i } ^ { 2 }\)
. What is the estimated marginal effect of x<sub>1</sub>? Explain.
Page 9
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Chapter 8: Model Selection in Multiple Linear Regression Analysis
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Sample Questions
Q1) If you had to either include an irrelevant variable or omit a relevant variable,you would prefer
A)including an irrelevant variable over omitting a relevant variable because omitting a relevant variable leads to biased estimates.
B)including an irrelevant variable over omitting a relevant variable because omitting a relevant variable leads to the standard errors to increase.
C)omitting a relevant variable over including an irrelevant variable because omitting a variable does not affect the estimated coefficients.
D)omitting a relevant variable over including an irrelevant variable because including an irrelevant variable does not lead to biased estimates.
Q2) Omitted variable bias is a potential problem because it
A)prevents accurately estimating true marginal effects.
B)results in estimated standard errors that are too large.
C)results in inefficient parameter estimates.
D)might highlight spurious correlations.
Q3) When would you use the RESET test? What is the null hypothesis for the test? What is the intuition for why it works? Explain.
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Chapter 9: Heteroskedasticity
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Sample Questions
Q1) The Goldfeld-Quandt test statistic is
A) \(G Q = U S S _ { 2 } / U S S _ { 1 }\)
B) \(G Q = U S S _ { 1 } / U S S _ { 2 }\)
C) \(G Q = U S S _ { 1 } \cdot U S S _ { 2 }\)
D) \(F _ { n _ { 1 } - k _ { 1 } , n _ { 2 } - k _ { 2 } , 05 }\)
Q2) Heteroskedasticity occurs when
A)the error variance is constant.
B)the error variance is non-constant.
C)the dependent variable variance is constant.
D)the dependent variable variance is non-constant.
Q3) 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.
Q4) What is the intuition behind the modified White's test for heteroskedasticity? Explain.
Q5) How do you perform Weighted Least Squares? Why should it work? Explain.
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Chapter 10: Time Series Analysis
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Sample Questions
Q1) Distributed lag models control for differences across time periods by
A)estimating the sample regression function by OLS.
B)including lagged dependent variables in the estimated sample regression function.
C)including lagged independent variables in the estimated sample regression function.
D)including both lagged dependent and lagged independent variables in the estimated sample regression function.
Q2) What is forecasting? How do you perform it? How can you test the potential validity of your forecasts? Explain.
Q3) You can perform forecasting by
A)making an educated guess as to what the value of the dependent variable will be at some point in the future.
B)regression future dependent variables of future independent variables.
C)using the results of regression analysis to predict the value of the dependent variable will be at some point in the future.
D)using the results of regression analysis to predict the value of the dependent variable in a past period and comparing the predicted value to the actual value.
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Chapter 11: Auto-Correlation
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Sample Questions
Q1) The first step of the Durbin-Watson test for the presence of autocorrelation is to estimate the model and determine
A)the current period residuals.
B)the residuals lagged one period.
C)the current period residuals and the residuals lagged one period.
D)the current period residuals,the residuals lagged one period,and the residuals lagged two periods.
Q2) An AR(1,6)process is written as
A) \(\varepsilon _ { t } = \rho \varepsilon _ { t - 1 } + u _ { t }\)
B) \(\varepsilon _ { t } = \rho \varepsilon _ { t - 6 } + u _ { i }\)
C) \(\varepsilon _ { t } = \rho _ { 1 } \varepsilon _ { t - 1 } + \rho _ { 2 } \varepsilon _ { t - 2 } + u _ { t }\)
D) \(\varepsilon _ { t } = \rho _ { 1 } \varepsilon _ { t - 1 } + \rho _ { 6 } \varepsilon _ { t - 6 } + u _ { t }\)
Q3) How do you perform Prais-Winsten method for AR(1)processes? Explain.
Q4) How do you perform the Regression test for AR(1)? Explain.
Q5) Why are Newey-West robust standard errors the preferred method for dealing with potential autocorrelation? Explain.
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Chapter 12: Limited Dependent Variables
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Sample Questions
Q1) Suppose you are interested in estimating the effect that getting a flu shot has on the probability of contracting the flu.In this case,your preferred estimator would be A)OLS.
B)WLS.
C)multiple linear regression.
D)a probit model.
Q2) Categorical dependent variables are ones that take on
A)continuous values.
B)only the values 0 and 1.
C)only a few integer values.
D)only non-negative values.
Q3) 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.
Q4) 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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Sample Questions
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)provide identical results if there are only two years of data.
D)do not completely remove the time-invariant component of the error term.
Q2) One can first-difference panel data in a two-period model by subtracting
A)the previous-period observation from the current-period observation for the dependent variable.
B)the previous-period observation from the current-period observation for the dependent variable and each independent variable.
C)the current-period observation from the previous-period observation for the dependent variable and each independent variable.
D)the current-period observation from the previous-period observation for the independent variable.
Q3) What is a first-differenced model? How is it preferable to a pooled cross-section model? Explain.
Q4) What does the error term look like for panel data? Explain each term in detail.
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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) Two-stage least squares can be used to
A)account for the time-invariant component of the error term.
B)control for heteroskedasticity.
C)control for measurement error in a dependent variable.
D)control for the endogeneity of independent variable.
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Chapter 15: Quantile Regression, Count Data, Sample
Selection Bias, and Quasi-Experimental Methods
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Sample Questions
Q1) When performing difference-in-difference estimation,the control group is the group
A)for which the policy shock occurred.
B)for which the policy shock did not occur.
C)of observation in the "before" sample.
D)of observation in the "after" sample.
Q2) Quantile regression
A)estimates marginal effects at the mean values of the independent variables.
B)results in biased estimates for skewed distributions.
C)can be estimated in Excel.
D)results in estimates approximating either the median or other percentiles of the dependent variable.
Q3) In which of the following cases would you want to estimate a Poisson model?
A)When individuals non-randomly select different outcomes of the dependent variable.
B)When you are attempting to replicate a randomized clinical trial.
C)When you are dealing with non-negative count data.
D)When you suspect that the marginal effects are different for different values of the dependent variable.
Q4) What is quantile regression? When might it be preferred to OLS? Explain.
Page 17
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