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What is the effect of an increase of living room surface of 5 sqm on the price, according to the estimated linear regression model?
What is the effect of an increase of living room surface of 5 sqm on the price, according to the estimated linear regression model?
Which of the following is the correct interpretation of the house price elasticity of 0.3%?
Which of the following is the correct interpretation of the house price elasticity of 0.3%?
If the OLS assumption E(u|X)=0 is violated, which of the following is true?
If the OLS assumption E(u|X)=0 is violated, which of the following is true?
Which of the following is not a solution to the problem of multicollinearity?
Which of the following is not a solution to the problem of multicollinearity?
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What is the correct test of significance for the linear regression model Y=b0 + b1X1 + b2X2?
What is the correct test of significance for the linear regression model Y=b0 + b1X1 + b2X2?
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Which of the following is the correct interpretation of the statement that the logarithm of house price increases by 5%?
Which of the following is the correct interpretation of the statement that the logarithm of house price increases by 5%?
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What does the beta1 coefficient represent in the context of a simple linear model of Y=scores in math vs the binary variable D=gender?
What does the beta1 coefficient represent in the context of a simple linear model of Y=scores in math vs the binary variable D=gender?
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Which statement about R-squared is incorrect?
Which statement about R-squared is incorrect?
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How can the variance of the regression errors be estimated?
How can the variance of the regression errors be estimated?
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What is the first assumption of OLS called?
What is the first assumption of OLS called?
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Under assumptions A1 to A4, what can be said about the OLS estimator of the beta coefficient?
Under assumptions A1 to A4, what can be said about the OLS estimator of the beta coefficient?
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In a logit model, what does the linear regression function $x^T\beta$ represent?
In a logit model, what does the linear regression function $x^T\beta$ represent?
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What does the beta coefficient associated with an independent variable $X$ in a logit model represent?
What does the beta coefficient associated with an independent variable $X$ in a logit model represent?
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Which of the following correctly describes the fraction of correctly predicted cases in a logit model?
Which of the following correctly describes the fraction of correctly predicted cases in a logit model?
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In a logit model, what transformation is applied to the linear regression function to obtain the ODDS of the event $Y=1$?
In a logit model, what transformation is applied to the linear regression function to obtain the ODDS of the event $Y=1$?
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What does the probability that $Y=1$, conditional on the regressors, represent in a logit model?
What does the probability that $Y=1$, conditional on the regressors, represent in a logit model?
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In a logit model, what transformation is applied to the ODDS of the event $Y=1$ to obtain the linear regression function?
In a logit model, what transformation is applied to the ODDS of the event $Y=1$ to obtain the linear regression function?
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Which assumption is NOT a standard assumption of the fixed effect model (within estimation)?
Which assumption is NOT a standard assumption of the fixed effect model (within estimation)?
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When is a panel considered balanced?
When is a panel considered balanced?
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What does the term 'attrition' refer to in panel data analysis?
What does the term 'attrition' refer to in panel data analysis?
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What does the expression Cov(u(i,t), u(i,s)| X(i,t), X(i,s))=0 for t≠s imply?
What does the expression Cov(u(i,t), u(i,s)| X(i,t), X(i,s))=0 for t≠s imply?
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Which of the following is NOT a requirement for the fixed effect model (within estimation)?
Which of the following is NOT a requirement for the fixed effect model (within estimation)?
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What is a potential issue associated with the term 'attrition' in panel data analysis?
What is a potential issue associated with the term 'attrition' in panel data analysis?
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What is the main advantage of using panel data over cross-sectional data?
What is the main advantage of using panel data over cross-sectional data?
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In the fixed effects regression model, what does the coefficient of the binary variable for an entity indicate?
In the fixed effects regression model, what does the coefficient of the binary variable for an entity indicate?
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What is true about the fixed effects regression model?
What is true about the fixed effects regression model?
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Which statement is true about the division of errors by regressors in different time periods?
Which statement is true about the division of errors by regressors in different time periods?
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What is the assumption about the errors in panel data models?
What is the assumption about the errors in panel data models?
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Which statement about the residuals in panel data models is true?
Which statement about the residuals in panel data models is true?
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Study Notes
Logit Model
- The probability of Y=1, conditional on the regressors, is a Gaussian transformation of the data.
- The log(ODDS) of the event Y=1 is modelled by a linear regression function xTb.
- The ODDS of the event Y=1 are modelled by a linear regression function.
- The exp(ODDS) of the event Y=1 are modelled by a linear regression function.
Logit Coefficients
- The beta coefficient associated with an independent variable X in a logit model represents the effect of a unit increment of X on the log-odds of the dependent variable.
- It is not the effect of a percent increment of X on the dependent variable.
- It is not equal to the log-odds ratio of Y and X.
Predictive Accuracy
- The fraction of correctly predicted cases is not the total number of cases where both the predicted and observed value of Y is equal to 1.
- It is not the sum of the first column in the cross tabulation of the predicted and the observed Y.
- It is not the sum of the first row in the cross tabulation of the predicted and the observed Y.
- It is the sum of the diagonal elements in the cross tabulation of the predicted and the observed Y.
House Price Elasticity
- If house price per Sqm increases by 5%, the logarithm of house price increases by 5%.
- If house price increases by 50$ per sqm, house price elasticity is not 0.01%.
- If house price increases by 50$ per sqm, the effect on the logarithm of house price is not 50%.
Linear Regression Model
- The OLS estimators are biased if the OLS assumption A1 (E(u|X)=0) fails.
- The OLS estimators are not unbiased, but inefficient if the OLS assumption A1 (E(u|X)=0) fails.
- A test F on the null hypothesis b0=b1=b2=0 is used to test the significance of the linear regression model.
- A chi-squared test is not used to test the significance of the linear regression model.
Multicollinearity
- Using the Ridge estimator as an alternative to the OLS is a solution to the problem of multicollinearity.
- Robust estimation of the standard errors is not a solution to the problem of multicollinearity.
- Identification of the variables that cause multicollinearity and estimation of a model without those variables is a solution to the problem of multicollinearity.
- Principal component analysis is a solution to the problem of multicollinearity.
Panel Data
- The main advantage of using a panel data over cross-sectional data is that it allows controlling for some types of omitted variables without actually observing them.
- Panel data allows analyzing behavior across time, but not across entities.
- Division of errors by regressors in different time periods is not always zero.
- Conditional on the regressors, the error are not always uncorrelated over time.
Fixed Effects Regression Model
- The coefficient of the binary variable in the fixed effects regression model indicates the level of the fixed effect of the i-th entity.
- The fixed effects regression model is such that the slope coefficients are allowed to differ across entities, but the intercept is “fixed” (remains unchanged).
- In a log-log model, the fixed effects regression model may include logs of binary variables, which control for the fixed effects.
- The fixed effects regression model has n different intercepts.
R-Squared
- The R-squared is always between 0 and 1.
- The R-squared is a measure of the goodness of fit of the linear model.
- The R-squared is not always non-decreasing.
Variance of Regression Errors
- The variance of the regression errors can be estimated by the sum of the squares of the residuals divided by (n-k-1).
- It can also be estimated by the HC estimator.
- It cannot be estimated by the R-squared.
Assumptions of OLS
- The first assumption of the OLS is called Homoscedasticity of errors.
- Under the assumptions from A1 to A4, the OLS estimator of the beta coefficient is unbiased and normally distributed for large n.
- It is not normally distributed for every sample of size n.
More Topics
- In a simple linear model of Y=scores in math vs the binary variable D= gender, the beta1 coefficient is the average difference in scores between males and females.
- In a panel data model, attrition refers to the problem of units leaving or being added to the cross-sectional dimension for the whole length of the panel.
- A panel is balanced if the number of units n remains constant through time.
- Cov(u(i,t), u(i,s)| X(i,t), X(i,s))=0 for t≠s means that there is no autocorrelation in the errors.
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Test your knowledge on interpreting beta coefficients in linear regression models and understanding the properties of R-squared. Questions cover topics such as average score differences between genders and the range and interpretation of R-squared values.