Question 1

In case of forecasts, the root mean squared error is the:

Accepted Answer

A) average of the forecast errors divided by the variance of the errors. 
B) average of the absolute forecast errors. 
C)  standard deviation of the forecast errors without any degrees of freedom adjustment. 
D)  standard deviation of the forecast errors with a degrees of freedom adjustment. 
A) average of the forecast errors divided by the variance of the errors. 
B) average of the absolute forecast errors. 
C)  standard deviation of the forecast errors without any degrees of freedom adjustment. 
D)  standard deviation of the forecast errors with a degrees of freedom adjustment.

Question 2

Let {(yt, zt): t = …, −2,−1, 0, 1, 2, …} be a bivariate time series process. The model: yt =   +   0​zt +   1zt - 1 +   2zt - 2 + ….. + ut, where t = …..,−2,−1,0,1,2,……, represents a(n):

Accepted Answer

A) moving average model. 
B) ARIMA model. 
C)  finite distributed lag model. 
D)  infinite distributed lag model. 
A) moving average model. 
B) ARIMA model. 
C)  finite distributed lag model. 
D)  infinite distributed lag model.

Question 3

The model: yt =   0 +   0zt +   yt - 1 +   1zt - 1 +vt, where vt = ut -   ut - 1 represents a:

Accepted Answer

A) finite distributed lag model. 
B) simultaneous equations model. 
C)  rational distributed lag model. 
D)  vector error correction model. 
A) finite distributed lag model. 
B) simultaneous equations model. 
C)  rational distributed lag model. 
D)  vector error correction model.

Question 4

Which of the following statements is true of spurious regressions?

Accepted Answer

A) The OLS estimates of the population parameters are efficient and unbiased and the t statistic is valid. 
B) Even if the explanatory variables and the dependent variable are independent times series processes, the R2 can be large. 
C)  Spurious regressions are limited to I(0) processes, and are not possible in case of I(1) processes. 
D)  Spurious regressions are limited to I(1) processes, and are not possible in case of I(0) processes. 
A) The OLS estimates of the population parameters are efficient and unbiased and the t statistic is valid. 
B) Even if the explanatory variables and the dependent variable are independent times series processes, the R2 can be large. 
C)  Spurious regressions are limited to I(0) processes, and are not possible in case of I(1) processes. 
D)  Spurious regressions are limited to I(1) processes, and are not possible in case of I(0) processes.

Question 5

The Koyck distributed lag model is an example of:

Accepted Answer

A) a moving average model. 
B) an autoregressive conditional heteroskedasticity model. 
C)  an infinite distributed lag model. 
D)  a finite distributed lag model. 
A) a moving average model. 
B) an autoregressive conditional heteroskedasticity model. 
C)  an infinite distributed lag model. 
D)  a finite distributed lag model.

In case of forecasts, the root mean squared error is the:

Let {(y_t, z_t): t = …, −2,−1, 0, 1, 2, …} be a bivariate time series process. The model: y_t = + ₀z_t + ₁z_{t -} ₁ + ₂z_{t -} ₂ + ….. + u_t, where t = …..,−2,−1,0,1,2,……, represents a(n):

The model: y_t = ₀ + ₀z_t + yt -₁ + ₁z_{t -} ₁ +v_t, where v_t = u_t - u_{t -} ₁ represents a:

Which of the following statements is true of spurious regressions?

The Koyck distributed lag model is an example 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: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series 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 Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Carrying Out an Empirical Project

Filters

Exam 18: Advanced Time Series Topics

In case of forecasts, the root mean squared error is the:

Let {(yt, zt): t = …, −2,−1, 0, 1, 2, …} be a bivariate time series process. The model: yt = + 0​zt + 1zt - 1 + 2zt - 2 + ….. + ut, where t = …..,−2,−1,0,1,2,……, represents a(n):

The model: yt = 0 + 0zt + yt - 1 + 1zt - 1 +vt, where vt = ut - ut - 1 represents a:

Which of the following statements is true of spurious regressions?

The Koyck distributed lag model is an example 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: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series 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 Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Carrying Out an Empirical Project

Filters

Let {(y_t, z_t): t = …, −2,−1, 0, 1, 2, …} be a bivariate time series process. The model: y_t = + ₀z_t + ₁z_{t -} ₁ + ₂z_{t -} ₂ + ….. + u_t, where t = …..,−2,−1,0,1,2,……, represents a(n):

The model: y_t = ₀ + ₀z_t + yt -₁ + ₁z_{t -} ₁ +v_t, where v_t = u_t - u_{t -} ₁ represents a: