Exam 8: Nonlinear Regression Functions

Assume that you had data for a cross-section of 100 households with data on consumption and personal disposable income. If you fit a linear regression function regressing consumption on disposable income, what prior expectations do you have about the slope and the intercept? The slope of this regression function is called the "marginal propensity to consume." If, instead, you fit a log-log model, then what is the interpretation of the slope? Do you have any prior expectation about its size?

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Question 61

In the case of regression with interactions, the coefficient of a binary variable should be interpreted as follows:

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Question 62

Consider the following least squares specification between testscores and the student-teacher ratio: $\widehat{\text { TestScore }}$ = 557.8 + 36.42 ln (Income). According to this equation, a 1% increase income is associated with an increase in test scores of

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Question 63

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In the case of regression with interactions, the coefficient of a binary variable should be interpreted as follows:

Consider the following least squares specification between testscores and the student-teacher ratio: $\widehat{\text { TestScore }}$ = 557.8 + 36.42 ln (Income). According to this equation, a 1% increase income is associated with an increase in test scores of

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

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

Estimation of Dynamic Causal Effects

Additional Topics in Time Series Regression

The Theory of Linear Regression With One Regressor

The Theory of Multiple Regression

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Exam 8: Nonlinear Regression Functions

In the case of regression with interactions, the coefficient of a binary variable should be interpreted as follows:

Consider the following least squares specification between testscores and the student-teacher ratio: TestScore ^\widehat{\text { TestScore }} TestScore = 557.8 + 36.42 ln (Income). According to this equation, a 1% increase income is associated with an increase in test scores of

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

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

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

Consider the following least squares specification between testscores and the student-teacher ratio: $\widehat{\text { TestScore }}$ = 557.8 + 36.42 ln (Income). According to this equation, a 1% increase income is associated with an increase in test scores of