Question 1

Simultaneous causality

Accepted Answer

A) means you must run a second regression of X on Y. 
B) leads to correlation between the regressor and the error term. 
C) means that a third variable affects both Y and X. 
D) cannot be established since regression analysis only detects correlation between variables. 
A) means you must run a second regression of X on Y. 
B) leads to correlation between the regressor and the error term. 
C) means that a third variable affects both Y and X. 
D) cannot be established since regression analysis only detects correlation between variables.

Question 2

The Phillips curve is a relationship in macroeconomics between the inflation rate (inf)and the unemployment rate (ur).Estimating the Phillips curve using quarterly data for the United States from 1962:I to 1995:IV,you find   = 4.08 + 0.118 urt,R2 = 0.003,SER = 3.148
(1.11)(0.176)
(a)Explain why,at first glance,this is a surprising result.
(b)Do you think that there is omitted variable bias in the regression?
(c)What other threats to internal validity may be present?
(d)If you could find a proper specification for the Phillips curve using United States data,what external validity criteria would you suggest?

Accepted Answer

(a)There is supposed to be a negative re

Question 3

The guidelines for whether or not to include an additional variable include all of the following,with the exception of

Accepted Answer

A) providing "full disclosure" representative tabulations of the results. 
B) testing whether additional questionable variables have nonzero coefficients. 
C) determining whether it can be measured in the population of interest. 
D) being specific about the coefficient or coefficients of interest. 
A) providing "full disclosure" representative tabulations of the results. 
B) testing whether additional questionable variables have nonzero coefficients. 
C) determining whether it can be measured in the population of interest. 
D) being specific about the coefficient or coefficients of interest.

Question 4

Explain why the OLS estimator for the slope in the simple regression model is still unbiased,even if there is correlation of the error term across observations.

Accepted Answer

The proof for unbiasedness is presented

Question 5

Your textbook only analyzed the case of an error-in-variables bias of the type   i= Xi + wi.What if the error were generated in the simple regression model by entering data that always contained the same typographical error,say   i= Xi + a or   i= bXi,where a and b are constants.What effect would this have on your regression model?

Accepted Answer

This would have an effect similar to cha

Simultaneous causality

The guidelines for whether or not to include an additional variable include all of the following,with the exception of

Explain why the OLS estimator for the slope in the simple regression model is still unbiased,even if there is correlation of the error term across observations.

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

Regression With Panel Data

Regression With a Binary Dependent Variable

Instrumental Variables Regression

Experiments and Quasi-Experiments

Introduction to Time Series Regression and Forecasting

Estimation of Dynamic Causal Effects

Additional Topics in Time Series Regression

The Theory of Linear Regression With One Regressor

The Theory of Multiple Regression

Filters

Exam 9: Assessing Studies Based on Multiple Regression

Simultaneous causality

The guidelines for whether or not to include an additional variable include all of the following,with the exception of

Explain why the OLS estimator for the slope in the simple regression model is still unbiased,even if there is correlation of the error term across observations.

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

Regression With Panel Data

Regression With a Binary Dependent Variable

Instrumental Variables Regression

Experiments and Quasi-Experiments

Introduction to Time Series Regression and Forecasting

Estimation of Dynamic Causal Effects

Additional Topics in Time Series Regression

The Theory of Linear Regression With One Regressor

The Theory of Multiple Regression

Filters