Question 1

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

Accepted Answer

A) autoregressive conditional heteroskedasticity model. 
B) static model. 
C) finite distributed lag model. 
D) infinite distributed lag model. 
A) autoregressive conditional heteroskedasticity model. 
B) static model. 
C) finite distributed lag model. 
D) infinite distributed lag model. B

Question 2

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

Accepted Answer

A) True 
 B)False  True

Question 3

Consider the following equation: Log(yt )= 0.7 + 1.2log(st ) + 0.3log(st-1) + 0.2log(st-2) + 0.1log(st-3)
What is the percentage increase in y given a permanent 1% increase in s?

Accepted Answer

A) 1.2 
B) 1.8 
C) 2.5 
D) 0.5 
A) 1.2 
B) 1.8 
C) 2.5 
D) 0.5 B

Question 4

Economic time series are outcomes of random variables.

Accepted Answer

A) True 
 B)False

Question 5

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

Accepted Answer

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. 
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.

Question 6

If an explanatory variable is strictly exogenous it implies that:

Accepted Answer

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. 
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.

Question 7

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

Accepted Answer

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 ut, given the explanatory variables for all time periods, is positive. 
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 ut, given the explanatory variables for all time periods, is positive.

Question 8

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

Accepted Answer

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. 
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.

Question 9

​When a series has the same average growth rate from period to period, then it can be approximated by an exponential trend.

Accepted Answer

A) True 
 B)False

Question 10

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

Accepted Answer

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. 
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.

Question 11

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

Accepted Answer

A) True 
 B)False

Question 12

A static model is postulated when:

Accepted Answer

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. 
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.

Question 13

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

A) 112.24 
B) 116.66 
C) 85.71 
D) 92.09 
A) 112.24 
B) 116.66 
C) 85.71 
D) 92.09

Question 14

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

Accepted Answer

A) ​​Multiple regression 
B) ​Simple regression 
C) ​Time series regression 
D) ​Cross-sectional regression 
A) ​​Multiple regression 
B) ​Simple regression 
C) ​Time series regression 
D) ​Cross-sectional regression

Question 15

A seasonally adjusted series is one which:

Accepted Answer

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. 
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.

Question 16

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

Accepted Answer

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. 
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.

Question 17

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

Accepted Answer

A) ​upward trend. 
B) ​downward trend. 
C) exponential trend. 
D) quadratic trend. 
A) ​upward trend. 
B) ​downward trend. 
C) exponential trend. 
D) quadratic trend.

Question 18

Refer to the following model
yt =   +   +   +   +   + ut.
 
Given a permanent increase in s,   is the long-run propensity.

Accepted Answer

A) True 
 B)False

Question 19

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

Accepted Answer

A) True 
 B)False

Question 20

Supposed that you are interested in estimating country-level maternal mortality rate (mmrt) based just on the gross domestic product per capita (gdppct) and literacy rate (lrt) and you find that countries that have unusually high (for the given levels of gdppc and lr) mmr in one period also have unusually high mmr in the next period. Which of the following assumption for time series analysis does not hold?

Accepted Answer

A) No perfect collinearity. 
B) Zero conditional mean. 
C) Homoskedasticity. 
D) No serial correlation. 
A) No perfect collinearity. 
B) Zero conditional mean. 
C) Homoskedasticity. 
D) No serial correlation.

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

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

Consider the following equation: Log(y_t )= 0.7 + 1.2log(s_t ) + 0.3log(s_t-1) + 0.2log(s_t-2) + 0.1log(s_t_-3) What is the percentage increase in y given a permanent 1% increase in s?

Economic time series are outcomes of random variables.

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

If an explanatory variable is strictly exogenous it implies that:

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

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

When a series has the same average growth rate from period to period, then it can be approximated by an exponential trend.

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

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

A static model is postulated when:

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 rules out perfect collinearity among the regressors?

A seasonally adjusted series is one which:

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:

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

Refer to the following model y_t = + + + + + ut_. Given a permanent increase in s, is the long-run propensity.

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

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis Estimation

Multiple Regression Analysis Inference

Multiple Regression Analysis Ols Asymptotics

Multiple Regression Analysis Further Issues

Multiple Regression Analysis With Qualitative Information

Heteroskedasticity

More on Specification and Data Issues

Further Issues in Using Ols With Time Sries Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Strage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Correctons

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Exam 10: Basic Regression Analysis With Time Series Data

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

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

Consider the following equation: Log(yt )= 0.7 + 1.2log(st ) + 0.3log(st-1) + 0.2log(st-2) + 0.1log(st-3) What is the percentage increase in y given a permanent 1% increase in s?

Economic time series are outcomes of random variables.

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

If an explanatory variable is strictly exogenous it implies that:

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

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

​When a series has the same average growth rate from period to period, then it can be approximated by an exponential trend.

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

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

A static model is postulated when:

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 rules out perfect collinearity among the regressors?

A seasonally adjusted series is one which:

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

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

Refer to the following model yt = + + + + + ut. Given a permanent increase in s, is the long-run propensity.

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

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis Estimation

Multiple Regression Analysis Inference

Multiple Regression Analysis Ols Asymptotics

Multiple Regression Analysis Further Issues

Multiple Regression Analysis With Qualitative Information

Heteroskedasticity

More on Specification and Data Issues

Further Issues in Using Ols With Time Sries Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Strage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Correctons

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

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

Consider the following equation: Log(y_t )= 0.7 + 1.2log(s_t ) + 0.3log(s_t-1) + 0.2log(s_t-2) + 0.1log(s_t_-3) What is the percentage increase in y given a permanent 1% increase in s?

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

When a series has the same average growth rate from period to period, then it can be approximated by an exponential trend.

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

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:

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

Refer to the following model y_t = + + + + + ut_. Given a permanent increase in s, is the long-run propensity.