MGSC 291 Exam 3 Prep Test Study
Questions with Verified Answers Graded A
2025
What is the first step in processing you data for a time series analysis in R?
- make all data factors
- make date variable a factor with the factor() function
-make date variable be recognized as a date in R with the as.Date() function - Correct
Ans-make date variable be recognized as a date in R with the as.Date() function
How does R store date variables?
-as factors
-as characters
-as the number of days relative to Jan 1, 1970 - Correct Ans-as the number of days
relative to Jan 1, 1970
In a time series, regular variation that is repeated within a year is called....
-non-stationary
-seasonal variation
-a random walk - Correct Ans-seasonal variation
Which of the following types of series are useless for modeling?
- random walk
- diverging series
- mean reverting, stationary - Correct Ans-diverging series
Which of the following is the most useful type of series for modeling?
- random walk
- diverging series
- stationary, mean reverting - Correct Ans-stationary, mean reverting
Match the coefficient on the AR(1) term in a regression to the correct series - Correct
Ans-
|beta| = 1
- random walk
- mean reverting
-diverging - Correct Ans-random walk
|beta| < 1
- random walk
- mean-reverting
-diverging - Correct Ans-mean reverting
, |beta| > 1
- random walk
- mean reverting
-diverging - Correct Ans-diverging
if you have a random walk, what should you do?
-use the returns transformation for modeling
- incorporate other important trend variables
- scrap the data - this type is useless - Correct Ans-use the returns transformation for
modeling
(WHICH 2 APPLY) What is true about adding higher than AR(1) lags to a model?
- the simple interrelations of the AR(1) term no longer apply
-this model always works better for prediction than the model with only the AR(1) term
-you almost always need to take a log of the response
- if you need higher lags, you might have missed an important trend or seasonality
- you might have modeled the wrong response - Correct Ans-- the simple interrelations
of the AR(1) term no longer apply
- if you need higher lags, you might have missed an important trend or seasonality
A sales price elasticity greater than -1 implies
- an inelastic good
- a good model fit
- a random walk-
-a diverging series - Correct Ans-an inelastic good
When you have multiple stacks of time series, for example a time series of monthly
sales for 185 stores, you have
- a random walk
- a mean reverting time series
- panel data
- no need for fixed effects - Correct Ans-panel data
(WHICH 2 APPLY) Fixed effects are:
- effects that fix your residuals to be uncorrelated
-are different from random effects which allow for correlations between the error terms
-are just simply a way to include factor variables into the regression
-make it unnecessary to check model fit since they fix errors in the regression - Correct
Ans--are different from random effects which allow for correlations between the error
terms
- are just simply a way to include factor variable into the regression
Dependencies in the data often lead to an
-underestimate of uncertainty
-an overestimate of uncertainty
-larger standard errors - Correct Ans-underestimate of uncertainty
Questions with Verified Answers Graded A
2025
What is the first step in processing you data for a time series analysis in R?
- make all data factors
- make date variable a factor with the factor() function
-make date variable be recognized as a date in R with the as.Date() function - Correct
Ans-make date variable be recognized as a date in R with the as.Date() function
How does R store date variables?
-as factors
-as characters
-as the number of days relative to Jan 1, 1970 - Correct Ans-as the number of days
relative to Jan 1, 1970
In a time series, regular variation that is repeated within a year is called....
-non-stationary
-seasonal variation
-a random walk - Correct Ans-seasonal variation
Which of the following types of series are useless for modeling?
- random walk
- diverging series
- mean reverting, stationary - Correct Ans-diverging series
Which of the following is the most useful type of series for modeling?
- random walk
- diverging series
- stationary, mean reverting - Correct Ans-stationary, mean reverting
Match the coefficient on the AR(1) term in a regression to the correct series - Correct
Ans-
|beta| = 1
- random walk
- mean reverting
-diverging - Correct Ans-random walk
|beta| < 1
- random walk
- mean-reverting
-diverging - Correct Ans-mean reverting
, |beta| > 1
- random walk
- mean reverting
-diverging - Correct Ans-diverging
if you have a random walk, what should you do?
-use the returns transformation for modeling
- incorporate other important trend variables
- scrap the data - this type is useless - Correct Ans-use the returns transformation for
modeling
(WHICH 2 APPLY) What is true about adding higher than AR(1) lags to a model?
- the simple interrelations of the AR(1) term no longer apply
-this model always works better for prediction than the model with only the AR(1) term
-you almost always need to take a log of the response
- if you need higher lags, you might have missed an important trend or seasonality
- you might have modeled the wrong response - Correct Ans-- the simple interrelations
of the AR(1) term no longer apply
- if you need higher lags, you might have missed an important trend or seasonality
A sales price elasticity greater than -1 implies
- an inelastic good
- a good model fit
- a random walk-
-a diverging series - Correct Ans-an inelastic good
When you have multiple stacks of time series, for example a time series of monthly
sales for 185 stores, you have
- a random walk
- a mean reverting time series
- panel data
- no need for fixed effects - Correct Ans-panel data
(WHICH 2 APPLY) Fixed effects are:
- effects that fix your residuals to be uncorrelated
-are different from random effects which allow for correlations between the error terms
-are just simply a way to include factor variables into the regression
-make it unnecessary to check model fit since they fix errors in the regression - Correct
Ans--are different from random effects which allow for correlations between the error
terms
- are just simply a way to include factor variable into the regression
Dependencies in the data often lead to an
-underestimate of uncertainty
-an overestimate of uncertainty
-larger standard errors - Correct Ans-underestimate of uncertainty
