Consider the following regression model: log(y) = 0 + 1x1 + 2x12 + 3x3 + u. This model will suffer from functional form misspecification if _____.

A) 0 is omitted from the model B) u is heteroskedastic C) x12 is omitted from the model D) x3 is a binary variable A) 0 is omitted from the model B) u is heteroskedastic C) x12 is omitted from the model D) x3 is a binary variable

Exam 9: More on Specification and Data Issues

A multiple regression model suffers from functional form misspecification when it does not properly account for the relationship between the dependent and the observed explanatory variables.

(True/False)

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Question 21

Consider the following regression model: log(y) = ₀ + ₁x₁ + ₂x₁² + ₃x₃ + u. This model will suffer from functional form misspecification if _____.

(Multiple Choice)

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Question 22

The classical errors-in-variables (CEV) assumption is that _____.

(Multiple Choice)

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Question 23

The Least Absolute Deviations (LAD) estimators in a linear model minimize the sum of squared residuals.

(True/False)

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Question 24

An explanatory variable is called exogenous if it is correlated with the error term.

(True/False)

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Question 25

How many new variables should be created for a multiple regression model where data are always available for y and x₁, x₂, ..., x_k−₁ but are sometimes missing for the explanatory variable x_k?

(Multiple Choice)

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Question 26

Which of the following is a difference between least absolute deviations (LAD) and ordinary least squares (OLS) estimation?

(Multiple Choice)

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Question 27

Showing 21 - 27 of 27

A multiple regression model suffers from functional form misspecification when it does not properly account for the relationship between the dependent and the observed explanatory variables.

Consider the following regression model: log(y) = ₀ + ₁x₁ + ₂x₁² + ₃x₃ + u. This model will suffer from functional form misspecification if _____.

The classical errors-in-variables (CEV) assumption is that _____.

The Least Absolute Deviations (LAD) estimators in a linear model minimize the sum of squared residuals.

An explanatory variable is called exogenous if it is correlated with the error term.

How many new variables should be created for a multiple regression model where data are always available for y and x₁, x₂, ..., x_k−₁ but are sometimes missing for the explanatory variable x_k?

Which of the following is a difference between least absolute deviations (LAD) and ordinary least squares (OLS) estimation?

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

Basic Regression Analysis With Time Series Data

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 9: More on Specification and Data Issues

A multiple regression model suffers from functional form misspecification when it does not properly account for the relationship between the dependent and the observed explanatory variables.

Consider the following regression model: log(y) = 0 + 1x1 + 2x12 + 3x3 + u. This model will suffer from functional form misspecification if _____.

The classical errors-in-variables (CEV) assumption is that _____.

The Least Absolute Deviations (LAD) estimators in a linear model minimize the sum of squared residuals.

An explanatory variable is called exogenous if it is correlated with the error term.

How many new variables should be created for a multiple regression model where data are always available for y and x1, x2, ..., xk−1 but are sometimes missing for the explanatory variable xk?

Which of the following is a difference between least absolute deviations (LAD) and ordinary least squares (OLS) estimation?

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

Basic Regression Analysis With Time Series Data

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

Consider the following regression model: log(y) = ₀ + ₁x₁ + ₂x₁² + ₃x₃ + u. This model will suffer from functional form misspecification if _____.

How many new variables should be created for a multiple regression model where data are always available for y and x₁, x₂, ..., x_k−₁ but are sometimes missing for the explanatory variable x_k?