Question 1

Which of the following statements is true?

Accepted Answer

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. 
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.

Question 2

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

Accepted Answer

A) event study. 
B) exponential study. 
C) laboratory study. 
D) comparative study. 
A) event study. 
B) exponential study. 
C) laboratory study. 
D) comparative study.

Question 3

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

Accepted Answer

A) True 
 B)False

Question 4

The short-run elasticity measures the immediate percentage change in a dependent variable given a 1% increase in the independent variables.​

Accepted Answer

A) True 
 B)False

Question 5

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

Accepted Answer

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. 
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.

Question 6

A stochastic process refers to a:

Accepted Answer

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. 
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.

Question 7

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

Accepted Answer

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. 
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.

Which of the following statements is true?

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

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

The short-run elasticity measures the immediate percentage change in a dependent variable given a 1% increase in the independent variables.

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 stochastic process refers to a:

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

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

Which of the following statements is true?

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

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

The short-run elasticity measures the immediate percentage change in a dependent variable given a 1% increase in the independent variables.​

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

A stochastic process refers to a:

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

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 short-run elasticity measures the immediate percentage change in a dependent variable given a 1% increase in the independent variables.

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):