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:

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):

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?

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:

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:

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):

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:

Accepted Answer

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

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:

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:

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:

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?

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):

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?

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?

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) :