Question 1

When Xt is strictly exogenous,the following estimator(s)of dynamic causal effects are available:

Accepted Answer

A) estimating an ADL model and calculating the dynamic multipliers from the estimated ADL coefficients 
B) using GLS to estimate the coefficients of the distributed lag model 
C) neither (a)or (b) 
D) (a)and (b) 
A) estimating an ADL model and calculating the dynamic multipliers from the estimated ADL coefficients 
B) using GLS to estimate the coefficients of the distributed lag model 
C) neither (a)or (b) 
D) (a)and (b)

Question 2

Your textbook used a distributed lag model with only current and past values of Xt-1 coupled with an AR(1)error model to derive a quasi-difference model,where the error term was uncorrelated.
(a)Instead use a static model Yt = β0 + β1Xt + ut here,where the error term follows an AR(1).Derive the quasi difference form.Explain why in the case of the infeasible GLS estimators you could easily estimate the βs by OLS.
(b)Since φ1 (the autocorrelation parameter for ut)is unknown,describe the Cochrane-Orcutt estimation procedure.
(c)Explain how the iterated Cochrane-Orcutt estimator works in this situation.Iterations stop when there is "convergence" in the estimates.What do you think is meant by that?
(d)Your textbook has pointed out that the iterated Cochrane-Orcutt GLS estimator is in fact the nonlinear least squares estimator of the model.Given that -1 < φ1 < 1,suggest a "grid search" or some strategy to "nail down" the value of   1 which minimizes the sum of squared residuals.This is the so-called Hildreth-Lu method.

Accepted Answer

(a)The quasi-difference model is derived

Question 3

The Cochrane-Orcutt iterative method is

Accepted Answer

A) a special case of GLS estimation. 
B) a method to compute HAC standard errors. 
C) a special case of maximum likelihood estimation. 
D) a grid search for the autoregressive parameters on the error process. 
A) a special case of GLS estimation. 
B) a method to compute HAC standard errors. 
C) a special case of maximum likelihood estimation. 
D) a grid search for the autoregressive parameters on the error process.

Question 4

The interpretation of the coefficients in a distributed lag regression as causal dynamic effects hinges on

Accepted Answer

A) the assumption that X is exogenous 
B) not having more than four lags when using quarterly data 
C) using GLS rather than OLS 
D) the use of monthly rather than annual data 
A) the assumption that X is exogenous 
B) not having more than four lags when using quarterly data 
C) using GLS rather than OLS 
D) the use of monthly rather than annual data

Question 5

GLS

Accepted Answer

A) results in smaller variances of the estimator than OLS if the regressors are strictly exogenous. 
B) is the same as OLS using HAC standard errors. 
C) can be used even if the regressors are not strictly exogenous. 
D) can be used for time-series estimation,but not in cross-sectional data. 
A) results in smaller variances of the estimator than OLS if the regressors are strictly exogenous. 
B) is the same as OLS using HAC standard errors. 
C) can be used even if the regressors are not strictly exogenous. 
D) can be used for time-series estimation,but not in cross-sectional data.

Question 6

The concepts of exogeneity,strict exogeneity,and predeterminedness

Accepted Answer

A) are defined in such a way that strict exogeneity implies exogeneity. 
B) can be used interchangeably. 
C) are defined in such a way that exogeneity implies strict exogeneity. 
D) correspond to endogeneity,strict endogeneity,and lagged endogenous variables. 
A) are defined in such a way that strict exogeneity implies exogeneity. 
B) can be used interchangeably. 
C) are defined in such a way that exogeneity implies strict exogeneity. 
D) correspond to endogeneity,strict endogeneity,and lagged endogenous variables.

Question 7

A seasonal binary (or indicator or dummy)variable,in the case of monthly data,

Accepted Answer

A) is a binary variable that take on the value of 1 for a given month and is 0 otherwise. 
B) is a variable that has values of 1 to 12 in a given year. 
C) is a variable that contains 1s during a given year and is 0 otherwise. 
D) does not exist,since a month is not a season. 
A) is a binary variable that take on the value of 1 for a given month and is 0 otherwise. 
B) is a variable that has values of 1 to 12 in a given year. 
C) is a variable that contains 1s during a given year and is 0 otherwise. 
D) does not exist,since a month is not a season.

Question 8

The 95% confidence interval for the dynamic multipliers should be computed by using the estimated coefficient ±

Accepted Answer

A) 1.96 times the RMSFE. 
B) 1.96 times the HAC standard errors. 
C) 1.96,since the HAC errors are standardized. 
D) 1.64 times the HAC standard errors since the alternative hypothesis is one-sided. 
A) 1.96 times the RMSFE. 
B) 1.96 times the HAC standard errors. 
C) 1.96,since the HAC errors are standardized. 
D) 1.64 times the HAC standard errors since the alternative hypothesis is one-sided.

Question 9

GLS is consistent and BLUE if

Accepted Answer

A) X is predetermined. 
B) the error process is AR(1). 
C) X is strictly exogenous. 
D) all the roots are inside the unit circle. 
A) X is predetermined. 
B) the error process is AR(1). 
C) X is strictly exogenous. 
D) all the roots are inside the unit circle.

Question 10

In time series,the definition of causal effects

Accepted Answer

A) says that one variable helps predict another variable. 
B) does not make much sense since there are not multiple subjects. 
C) assumes that the same subject is being given different treatments at different points in time. 
D) requires panel data. 
A) says that one variable helps predict another variable. 
B) does not make much sense since there are not multiple subjects. 
C) assumes that the same subject is being given different treatments at different points in time. 
D) requires panel data.

When Xt is strictly exogenous,the following estimator(s)of dynamic causal effects are available:

The Cochrane-Orcutt iterative method is

The interpretation of the coefficients in a distributed lag regression as causal dynamic effects hinges on

GLS

The concepts of exogeneity,strict exogeneity,and predeterminedness

A seasonal binary (or indicator or dummy)variable,in the case of monthly data,

The 95% confidence interval for the dynamic multipliers should be computed by using the estimated coefficient ±

GLS is consistent and BLUE if

In time series,the definition of causal effects

Economic Questions and Data

Review of Probability

Review of Statistics

Linear Regression With One Regressor

Regression With a Single Regressor: Hypothesis Tests and Confidence Intervals

Linear Regression With Multiple Regressors

Hypothesis Tests and Confidence Intervals in Multiple Regression

Nonlinear Regression Functions

Assessing Studies Based on Multiple Regression

Regression With Panel Data

Regression With a Binary Dependent Variable

Instrumental Variables Regression

Experiments and Quasi-Experiments

Introduction to Time Series Regression and Forecasting

Additional Topics in Time Series Regression

The Theory of Linear Regression With One Regressor

The Theory of Multiple Regression

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Exam 15: Estimation of Dynamic Causal Effects

When Xt is strictly exogenous,the following estimator(s)of dynamic causal effects are available:

The Cochrane-Orcutt iterative method is

The interpretation of the coefficients in a distributed lag regression as causal dynamic effects hinges on

GLS

The concepts of exogeneity,strict exogeneity,and predeterminedness

A seasonal binary (or indicator or dummy)variable,in the case of monthly data,

The 95% confidence interval for the dynamic multipliers should be computed by using the estimated coefficient ±

GLS is consistent and BLUE if

In time series,the definition of causal effects

Economic Questions and Data

Review of Probability

Review of Statistics

Linear Regression With One Regressor

Regression With a Single Regressor: Hypothesis Tests and Confidence Intervals

Linear Regression With Multiple Regressors

Hypothesis Tests and Confidence Intervals in Multiple Regression

Nonlinear Regression Functions

Assessing Studies Based on Multiple Regression

Regression With Panel Data

Regression With a Binary Dependent Variable

Instrumental Variables Regression

Experiments and Quasi-Experiments

Introduction to Time Series Regression and Forecasting

Additional Topics in Time Series Regression

The Theory of Linear Regression With One Regressor

The Theory of Multiple Regression

Filters