Podcast
Questions and Answers
What is a common source of bias for the OLS estimate of β1 in the presence of unobserved family effects?
What is a common source of bias for the OLS estimate of β1 in the presence of unobserved family effects?
- Serial correlation in the error terms
- Heteroscedasticity in the data
- Measurement error in the dependent variable
- Omitted variable bias (correct)
How can we eliminate family effects when estimating β1 using within-twin-pair estimation?
How can we eliminate family effects when estimating β1 using within-twin-pair estimation?
- Including an instrumental variable in the model
- Applying a kernel density estimation
- Adding a new control variable to the regression model
- Differencing the log wages within each family (correct)
What does the 'Nickell-bias' refer to in fixed effects models with lagged dependent variables?
What does the 'Nickell-bias' refer to in fixed effects models with lagged dependent variables?
- Bias that is negligible for small values of T
- Bias that is always positive regardless of the value of ρ
- Bias that affects the coefficient on the lagged dependent variable (correct)
- Bias introduced due to heteroscedasticity in the lagged dependent variable
In panel data settings, what is meant by 'modeling persistence'?
In panel data settings, what is meant by 'modeling persistence'?
How does the Nickell-bias change as the number of time periods (T) in a fixed effects model increases?
How does the Nickell-bias change as the number of time periods (T) in a fixed effects model increases?
What is the main difference in bias between OLS and fixed effects models with lagged dependent variables?
What is the main difference in bias between OLS and fixed effects models with lagged dependent variables?
What type of bias can be eliminated by including time effects in a panel data model?
What type of bias can be eliminated by including time effects in a panel data model?
In the context of panel data models, what is the purpose of including fixed effects?
In the context of panel data models, what is the purpose of including fixed effects?
In the model $Y_{it} = \lambda_t + \beta_1 X_{it} + u_{it}$, what does $\lambda_t$ represent?
In the model $Y_{it} = \lambda_t + \beta_1 X_{it} + u_{it}$, what does $\lambda_t$ represent?
Which of the following estimation methods can be used to account for fixed effects in a panel data model?
Which of the following estimation methods can be used to account for fixed effects in a panel data model?
In the context of panel data models, what is the purpose of including dynamic panel models?
In the context of panel data models, what is the purpose of including dynamic panel models?
In a panel data model with fixed effects, which of the following assumptions is violated?
In a panel data model with fixed effects, which of the following assumptions is violated?
Which of the following is true about fixed effects (FE) or first differences estimation techniques?
Which of the following is true about fixed effects (FE) or first differences estimation techniques?
Which of the following is an example of a variable that could potentially cause omitted variable bias in the context discussed in the text?
Which of the following is an example of a variable that could potentially cause omitted variable bias in the context discussed in the text?
In the context of panel data analysis, what is the purpose of including fixed effects or time effects in the regression model?
In the context of panel data analysis, what is the purpose of including fixed effects or time effects in the regression model?
What is the alternative estimation approach mentioned in the text for panel data analysis?
What is the alternative estimation approach mentioned in the text for panel data analysis?
If a variable changes at different rates across units in a panel data analysis, what is the recommended approach according to the text?
If a variable changes at different rates across units in a panel data analysis, what is the recommended approach according to the text?
Which of the following statements is true about dynamic panel models, according to the text?
Which of the following statements is true about dynamic panel models, according to the text?
Study Notes
Unobserved Family Effects
- Unobserved family effects (αf) can bias the OLS estimate of β1.
- The within-twin-pair estimator can be used to eliminate family effects by differencing within family.
Modeling Persistence
- Persistence is an important feature in many panel settings, meaning that today's behavior is partly a function of previous periods.
- To capture persistence, lagged-dependent variables can be introduced into models.
- However, in a fixed effects model, the lagged-dependent variable is biased downwards (Nickell-bias) by approximately −(1 + ρ)/(T − 1), where ρ < 1 is the coefficient on the lagged dependent variable.
Time Effects
- Fixed effects can eliminate biases deriving from omitted variables that do not change over time.
- Panel data also allow for the elimination of biases deriving from variables that change over time but have the same value in all states.
- Time effects can be eliminated by including time dummies or using a within-transformation.
Further Explanatory Variables
- Fixed effects (or first differences) and time effects are not a panacea against omitted variable bias.
- They can only avoid omitted variable bias where the omitted variables do not change over time or have the same effect on all units of observation within one period.
- Including additional variables that change at different rates in different states (e.g., income per-capita, unemployment rate, local measures for traffic safety) can help address omitted variable bias.
Random Effects
- An alternative to the fixed effect model is the random effect (RE) model.
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Description
Explore the concepts of time effects and fixed effects in panel data analysis. Learn how including time-effects can help eliminate biases from omitted variables that change over time, while fixed effects can eliminate biases from variables that do not change over time.
