Question 1

Dummy variables can be used to address the problem of seasonality in regression models.

Accepted Answer

Dummy variables can be used to account for seasonality in a regression model by creating variables that represent each season or time period, allowing the model to capture any effects of seasonality on the outcome variable.

Question 2

A static model is postulated when:
A)a change in the independent variable at time 't' is believed to have an effect on the dependent variable at period 't + 1'.
B)a change in the independent variable at time 't' is believed to have an effect on the dependent variable for all successive time periods.
C)a change in the independent variable at time 't' does not have any effect on the dependent variable.
D)a change in the independent variable at time 't' is believed to have an immediate effect on the dependent variable.

Accepted Answer

A static model is one where a change in the independent variable at time 't' is believed to have an immediate effect on the dependent variable. Option D aligns with this definition.

Question 3

The model: Y_t = β₀ + β₁c_t + u_t,t = 1,2,…….n,is an example of a(n): A)autoregressive conditional heteroskedasticity model. B)static model. C)finite distributed lag model. D)infinite distributed lag model.

Accepted Answer

The model is a static model because it does not include any lagged values of the dependent variable.

Question 4

Which of the following statements is true?
A)The average of an exponential time series is a linear function of time.
B)The average of a linear sequence is an exponential function of time.
C)When a series has the same average growth rate from period to period,it can be approximated with an exponential trend.
D)When a series has the same average growth rate from period to period,it can be approximated with a linear trend.

Accepted Answer

When a series has the same average growth rate from period to period, it is exhibiting exponential growth, and can be approximated with an exponential trend. A linear trend would not accurately reflect the growth rate. Option A is incorrect because exponential time series itself is not linear. Option B is also incorrect because the average of a linear sequence is not an exponential function of time.

Question 5

Refer to the following model. y_t = α₀ + β₀s_t+ β₁s_t-1 + β₂s_t-2+ β₃s_t-3 + u_t Β₀+ β₁+ β₂+ β₃represents: A)the short-run change in y given a temporary increase in s. B)the short-run change in y given a permanent increase in s. C)the long-run change in y given a permanent increase in s. D)the long-run change in y given a temporary increase in s.

Accepted Answer

The sum of the coefficients (β₀ + β₁ + β₂ + β₃) represents the long-run change in y given a permanent increase in s, as it captures the total effect of a sustained change in s over time.

Question 6

The sample size for a time series data set is the number of:
A)variables being measured.
B)time periods over which we observe the variables of interest less the number of variables being measured.
C)time periods over which we observe the variables of interest plus the number of variables being measured.
D)time periods over which we observe the variables of interest.

Accepted Answer

The sample size for a time series data set refers to the number of observations over time for the variables of interest. Therefore, the answer is D, which is the time periods over which we observe the variables of interest. The number of variables being measured is not relevant to the sample size in this context.

Question 7

Refer to the following model. y_t = α₀ + β₀s_t+ β₁s_t-1 + β₂s_t-2+ β₃s_t-3 + u_t This is an example of a(n): A)infinite distributed lag model. B)finite distributed lag model of order 1. C)finite distributed lag model of order 2. D)finite distributed lag model of order 3.

Accepted Answer

The model includes lagged terms up to s_t-3, indicating it is a finite distributed lag model of order 3.

Question 8

A seasonally adjusted series is one which:
A)has had seasonal factors added to it.
B)has qualitative dependent variables representing different seasons.
C)has qualitative explanatory variables representing different seasons.
D)has seasonal factors removed from it.

Accepted Answer

The answer of A seasonally adjusted series is one which:
A)has...

Question 9

In a static model,one or more explanatory variables affect the dependent variable with a lag.

Accepted Answer

In a static model, the dependent variable is explained by the current values of the explanatory variables, without any lagged effects.

Question 10

Adding a time trend can make an explanatory variable more significant if:
A)the dependent and independent variables have similar kinds of trends,but movement in the independent variable about its trend line causes movement in the dependent variable away from its trend line.
B)the dependent and independent variables have similar kinds of trends and movement in the independent variable about its trend line causes movement in the dependent variable towards its trend line.
C)the dependent and independent variables have different kinds of trends and movement in the independent variable about its trend line causes movement in the dependent variable towards its trend line.
D)the dependent and independent variables have different kinds of trends,but movement in the independent variable about its trend line causes movement in the dependent variable away from its trend line.

Accepted Answer

The answer of Adding a time trend can make an...

Question 11

Economic time series are outcomes of random variables.

Accepted Answer

Economic time series are not deterministic and can have unpredictable fluctuations. Therefore, they are modeled as outcomes of random variables.

Question 12

Price indexes are necessary for turning a time series measured in real value into nominal value.

Accepted Answer

Price indexes are used to adjust nominal values into real values, not the other way around. They help to remove the effects of inflation to show the true change in value over time.

Question 13

If an explanatory variable is strictly exogenous it implies that:
A)changes in the lag of the variable does not affect future values of the dependent variable.
B)the variable is correlated with the error term in all future time periods.
C)the variable cannot react to what has happened to the dependent variable in the past.
D)the conditional mean of the error term given the variable is zero.

Accepted Answer

If an explanatory variable is strictly exogenous, it means that it is not affected by past values of the dependent variable. Therefore, it cannot react to what has happened to the dependent variable in the past.

Question 14

A stochastic process refers to a:
A)sequence of random variables indexed by time.
B)sequence of variables that can take fixed qualitative values.
C)sequence of random variables that can take binary values only.
D)sequence of random variables estimated at the same point of time.

Accepted Answer

A stochastic process is a sequence of random variables indexed by time, where the values of each random variable depend on chance or probability. This can also be called a random process. The other choices do not accurately describe a stochastic process.

Question 15

Time series regression is based on series which exhibit serial correlation.

Accepted Answer

The answer of Time series regression is based on series...

Question 16

Which of the following correctly identifies a difference between cross-sectional data and time series data?
A)Cross-sectional data is based on temporal ordering,whereas time series data is not.
B)Time series data is based on temporal ordering,whereas cross-sectional data is not.
C)Cross-sectional data consists of only qualitative variables,whereas time series data consists of only quantitative variables.
D)Time series data consists of only qualitative variables,whereas cross-sectional data does not include qualitative variables.

Accepted Answer

Cross-sectional data refers to data collected at a specific point in time from a group of individuals or entities, whereas time series data involves collecting data over a period of time from a single individual or entity. Thus, time series data is based on temporal ordering, whereas cross-sectional data is not. The other options are incorrect as they do not accurately distinguish between cross-sectional and time series data.

Question 17

A study which observes whether a particular occurrence influences some outcome is referred to as a(n):
A)event study.
B)exponential study.
C)laboratory study.
D)comparative study.

Accepted Answer

An event study observes the impact of a specific event or occurrence on a particular outcome, such as the effect of a company announcing earnings on its stock price. The other options (exponential study, laboratory study, and comparative study) do not necessarily involve observing the impact of a specific occurrence.

Question 18

With base year 1990,the index of industrial production for the year 1999 is 112.What will be the value of the index in 1999,if the base year is changed to 1982 and the index measured 96 in 1982?
A)112.24
B)116.66
C)85.71
D)92.09

Accepted Answer

The index value for 1999 with 1982 as the base year can be calculated using the formula: New Index = (Old Index / Index of base change year with old base) * 100. Here, it's (112 / 96) * 100 = 116.66.

Question 19

Which of the following is an assumption on which time series regression is based? A)A time series process follows a model that is nonlinear in parameters. B)In a time series process,no independent variable is a perfect linear combination of the others. C)In a time series process,at least one independent variable is a constant. D)For each time period,the expected value of the error u_t,given the explanatory variables for all time periods,is positive.

Accepted Answer

Time series regression assumes that no independent variable is a perfect linear combination of the others. This is known as the principle of non-collinearity. The other options are incorrect assumptions for time series regression.

Quiz 10: Basic Regression Analysis With Time Series Data

Dummy variables can be used to address the problem of seasonality in regression models.

A static model is postulated when:

The model: Y_t = β₀ + β₁c_t + u_t,t = 1,2,…….n,is an example of a(n) :

Which of the following statements is true?

Refer to the following model. y_t = α₀ + β₀s_t+ β₁s_t-1 + β₂s_t-2+ β₃s_t-3 + u_t Β₀+ β₁+ β₂+ β₃represents:

The sample size for a time series data set is the number of:

Refer to the following model. y_t = α₀ + β₀s_t+ β₁s_t-1 + β₂s_t-2+ β₃s_t-3 + u_t This is an example of a(n) :

A seasonally adjusted series is one which:

In a static model,one or more explanatory variables affect the dependent variable with a lag.

Adding a time trend can make an explanatory variable more significant if:

Economic time series are outcomes of random variables.

Price indexes are necessary for turning a time series measured in real value into nominal value.

If an explanatory variable is strictly exogenous it implies that:

A stochastic process refers to a:

Time series regression is based on series which exhibit serial correlation.

Which of the following correctly identifies a difference between cross-sectional data and time series data?

A study which observes whether a particular occurrence influences some outcome is referred to as a(n) :

With base year 1990,the index of industrial production for the year 1999 is 112.What will be the value of the index in 1999,if the base year is changed to 1982 and the index measured 96 in 1982?

Which of the following is an assumption on which time series regression is based?

Quiz 10: Basic Regression Analysis With Time Series Data

Dummy variables can be used to address the problem of seasonality in regression models.

A static model is postulated when:

The model: Yt = β0 + β1ct + ut,t = 1,2,…….n,is an example of a(n) :

Which of the following statements is true?

Refer to the following model. yt = α0 + β0st+ β1st-1 + β2st-2+ β3st-3 + ut Β0 + β1 + β2 + β3represents:

The sample size for a time series data set is the number of:

Refer to the following model. yt = α0 + β0st+ β1st-1 + β2st-2+ β3st-3 + ut This is an example of a(n) :

A seasonally adjusted series is one which:

In a static model,one or more explanatory variables affect the dependent variable with a lag.

Adding a time trend can make an explanatory variable more significant if:

Economic time series are outcomes of random variables.

Price indexes are necessary for turning a time series measured in real value into nominal value.

If an explanatory variable is strictly exogenous it implies that:

A stochastic process refers to a:

Time series regression is based on series which exhibit serial correlation.

Which of the following correctly identifies a difference between cross-sectional data and time series data?

A study which observes whether a particular occurrence influences some outcome is referred to as a(n) :

With base year 1990,the index of industrial production for the year 1999 is 112.What will be the value of the index in 1999,if the base year is changed to 1982 and the index measured 96 in 1982?

Which of the following is an assumption on which time series regression is based?

The model: Y_t = β₀ + β₁c_t + u_t,t = 1,2,…….n,is an example of a(n) :

Refer to the following model. y_t = α₀ + β₀s_t+ β₁s_t-1 + β₂s_t-2+ β₃s_t-3 + u_t Β₀+ β₁+ β₂+ β₃represents:

Refer to the following model. y_t = α₀ + β₀s_t+ β₁s_t-1 + β₂s_t-2+ β₃s_t-3 + u_t This is an example of a(n) :