Question 1

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 is the number of time periods over which we observe the variables of interest.

Question 2

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 a variable is strictly exogenous, it means that it cannot react to what has happened to the dependent variable in the past. In other words, it is not affected by any current or past values of the dependent variable. This implies that it is not correlated with the error term in any future time periods, and changes in the lag of the variable do not affect future values of the dependent variable. However, the conditional mean of the error term given the variable being zero is not a requirement for strict exogeneity. If an explanatory variable is strictly exogenous it implies that the variable cannot react to what has happened to the dependent variable in the past.

Question 3

Economic time series are outcomes of random variables.

Accepted Answer

Economic time series are influenced by various factors such as government policies, consumer behavior, natural disasters, and global events, which are all uncertain outcomes of random variables. Economic time series are outcomes of random variables.

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

This statement is true. When a series has the same average growth rate from period to period, it can be approximated with an exponential trend. This is because the rate of change (percentage increase or decrease) in each period is constant, and exponential functions have a constant growth rate. When a series has the same average growth rate from period to period, it can be approximated with an exponential trend.

Question 5

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

If the base year is changed to 1982, the new index of industrial production for 1999 will equal 100(112/96) = 116.67.

Question 6

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 assumes that a change in the independent variable at time 't' has an immediate effect on the dependent variable, meaning there is no time lag or effect on future time periods. A static model is postulated when a change in the independent variable at time 't' is believed to have an immediate effect on the dependent variable.

Question 7

Refer to the following model y_t = ₀ + ₀s_t + ₁s_t-1 + ₂s_t-2 + ₃s_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: y_t = 11eab06d_255d_3315_beed_1b62e08a3376_TB2133_11 ₀ + 11eab06d_255d_3316_beed_a59281413bd4_TB2133_11 ₀s_t + 11eab06d_255d_5a27_beed_f1169a34d587_TB2133_11 ₁s_t-1 + 11eab06d_255d_5a28_beed_01321b6cb786_TB2133_11 ₂s_t-2 + 11eab06d_255d_8139_beed_e7cf6e7e4464_TB2133_11 ₃s_t-3 + u_t, is an example of a finite distributed lag model of order 3.

Question 8

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 particular event on some outcome, such as stock prices following the announcement of a merger. Exponential studies involve the growth of a variable over time, laboratory studies are conducted in a controlled environment, and comparative studies involve comparing two or more groups or conditions. A study which observes whether a particular occurrence influences some outcome is referred to as an event study.

Question 9

​Which of the following rules out perfect collinearity among the regressors?
A)​​Multiple regression
B)​Simple regression
C) ​Time series regression
D) ​Cross-sectional regression

Accepted Answer

Cross-sectional regression rules out perfect collinearity among the regressors.u200b

Question 10

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

The assumption on which time series regression is based is that in a time series process, no independent variable is a perfect linear combination of the others. This is known as the assumption of no perfect multicollinearity. Time series regression is based on the assumption that no independent variable is constant nor a perfect linear combination of the others.

Question 11

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

Accepted Answer

In a finite distributed lag model, one or more explanatory variables affect the dependent variable with a lag. In a static model, no lags are included.

Question 12

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 from a particular point in time, usually in the form of a survey or experiment. It is often used to compare different groups or variables at a single point in time. Time series data, on the other hand, refers to data collected over time, often at regular intervals. It is used to analyze trends and patterns in a particular variable over time. Therefore, the main difference between the two is that cross-sectional data is not based on temporal ordering, while time series data is. Choice B correctly identifies this difference between the two types of data. Time series data is based on temporal ordering, whereas cross sectional data is not.

Question 13

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

Adding a time trend can make an explanatory variable more significant if 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 away from its trend line.

Question 14

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

Accepted Answer

A seasonally adjusted series is one in which the seasonal factors are removed from it, in order to better analyze underlying trends and patterns in the data. A seasonally adjusted series is one which has seasonal factors removed from it.

Question 15

The model: Y_t = ₀ + ₁c_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: y_t= 11eab06d_2559_d7ae_beed_29061860b333_TB2133_11 ₀ + 11eab06d_2559_febf_beed_ab3725311e9b_TB2133_11 ₁c_t + u_t, t = 1, 2, ……., n is an example of a static model.

Question 16

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 mathematical model used to describe the evolution of a system over time, where the evolution is subject to random fluctuations. It is defined as a sequence of random variables (or random processes) indexed in time. The concept of a stochastic process is used extensively in fields such as engineering, economics, finance, and physics, where the behavior of a system is affected by random factors. A stochastic process refers to a sequence of random variables indexed by time.

Question 17

If ₁ > 0, then y_tin the linear function of time E(y_t) = ₀ + ₁t displays a(n): A)upward trend. B)downward trend. C) exponential trend. D) quadratic trend.

Accepted Answer

The linear function E(y_t) has a positive slope coefficient (11eab06d_255f_a427_beed_7bbbf04747f8_TB2133_11 ₁ > 0), which means that the expected value of y_t will increase as t increases. This indicates an upward trend. If 11eab06d_255f_cb3a_beed_d35099f2d301_TB2133_11 ₁ > 0, then y_t_u200bin the linear function of time E(y_t) = 11eab06d_255f_f24b_beed_39a1b34d196f_TB2133_11 ₀ + 11eab06d_255f_f24c_beed_032acbbfeb79_TB2133_11 ₁t displays anu200b upward trend.u200b

Question 18

The propensity δ₀ + δ₁+ … + δ_k is sometimes called the: A)short-run elasticity, which measures the percentage increase in a dependent variable after k quarters given a permanent 1% increase in the k independent variables. B)long-run elasticity, which measures the percentage increase in a dependent variable after k quarters given a permanent 1% increase in the k independent variables. C) short-run elasticity, which measures the percentage decrease in a dependent variable after k quarters given a permanent 1% decrease in the k independent variables. D) long-run elasticity, which measures the percentage decrease in a dependent variable after k quarters given a permanent 1% decrease in the k independent variables.

Accepted Answer

The propensity δ₀ + δ₁+ … + δ_k measures the long-run elasticity, which measures the percentage increase in a dependent variable after k quarters given a permanent 1% increase in the k independent variables. The other choices are incorrect as they refer to different concepts or measures. u200bThe propensity δ₀ + δ₁+ … + δ_k is sometimes called the long-run elasticity, which measures the percentage increase in a dependent variable after k quarters given a permanent 1% increase in the k independent variables.

Question 19

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

Accepted Answer

One of the assumptions of time series regression is that there should be no serial correlation in the concerned series.

Question 20

Refer to the following model. y_t = ₀ + ₀s_t + ₁s_t-1 + ₂s_t-2 + ₃s_t-3 + u_t ₀ + ₁ + ₂ + ₃ represents: 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

In the model, the sum of the coefficients on current and lagged z, 11eab06d_255e_92b3_beed_1359ab6f8366_TB2133_11 ₀ + 11eab06d_255e_92b4_beed_b9720d8671f7_TB2133_11 ₁ + 11eab06d_255e_b9c5_beed_ffc32544e5db_TB2133_11 ₂ + 11eab06d_255e_b9c6_beed_354678166b3c_TB2133_11 ₃ represents the long-run change in y given a permanent change in s.

Quiz 10: Basic Regression Analysis With Time Series Data

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

If an explanatory variable is strictly exogenous it implies that:

Economic time series are outcomes of random variables.

Which of the following statements is true?

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 static model is postulated when:

Refer to the following model y_t = ₀ + ₀s_t + ₁s_t-1 + ₂s_t-2 + ₃s_t-3 + u_t This is an example of a(n) :

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

Which of the following rules out perfect collinearity among the regressors?

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

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

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

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

A seasonally adjusted series is one which:

The model: Y_t = ₀ + ₁c_t + u_t, t = 1,2,……., n is an example of a(n) :

A stochastic process refers to a:

If ₁ > 0, then y_tin the linear function of time E(y_t) = ₀ + ₁t displays a(n) :

The propensity δ₀ + δ₁+ … + δ_k is sometimes called the:

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

Refer to the following model. y_t = ₀ + ₀s_t + ₁s_t-1 + ₂s_t-2 + ₃s_t-3 + u_t ₀ + ₁ + ₂ + ₃ represents:

Quiz 10: Basic Regression Analysis With Time Series Data

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

If an explanatory variable is strictly exogenous it implies that:

Economic time series are outcomes of random variables.

Which of the following statements is true?

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 static model is postulated when:

Refer to the following model yt = 0 + 0st + 1st-1 + 2st-2 + 3st-3 + ut This is an example of a(n) :

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

​Which of the following rules out perfect collinearity among the regressors?

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

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

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

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

A seasonally adjusted series is one which:

The model: Yt = 0 + 1ct + ut, t = 1,2,……., n is an example of a(n) :

A stochastic process refers to a:

If 1 > 0, then yt ​in the linear function of time E(yt) = 0 + 1t displays a(n) :​

The propensity δ0 + δ1+ … + δk is sometimes called the:​

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

Refer to the following model. yt = 0 + 0st + 1st-1 + 2st-2 + 3st-3 + ut 0 + 1 + 2 + 3 represents:

Refer to the following model y_t = ₀ + ₀s_t + ₁s_t-1 + ₂s_t-2 + ₃s_t-3 + u_t This is an example of a(n) :

Which of the following rules out perfect collinearity among the regressors?

The model: Y_t = ₀ + ₁c_t + u_t, t = 1,2,……., n is an example of a(n) :

If ₁ > 0, then y_tin the linear function of time E(y_t) = ₀ + ₁t displays a(n) :

The propensity δ₀ + δ₁+ … + δ_k is sometimes called the:

Refer to the following model. y_t = ₀ + ₀s_t + ₁s_t-1 + ₂s_t-2 + ₃s_t-3 + u_t ₀ + ₁ + ₂ + ₃ represents: