Consider the following unobserved effects model: Yit =B₁xit1 +...+ ßkitk + ai + Uit You plan on using fixed effects to estimate the model. Suppose you have a panel data set consisting of 10 individuals over 6 years. However, you are missing the last 3 years of data for the third, ż = 3, individual in your data set. Your panel data set has total observations. Suppose the reason individual i is missing data for certain time periods (called attrition) is correlated with the idiosyncratic error, uit. Recall that a; is assumed to be uncorrelated with the idiosyncratic error, uit. This correlation cause the fixed effects estimators to be biased.

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Consider the following unobserved effects model:
Yit =B₁xit1 + ... +ßkäitk +ai + Uit
You plan on using fixed effects to estimate the model.
Suppose you have a panel data set consisting of 10 individuals over 6 years. However, you are missing the last 3 years of data for the third, i = 3,
individual in your data set.
Your panel data set has
total observations.
Suppose the reason individual i is missing data for certain time periods (called attrition) is correlated with the idiosyncratic error, uit. Recall that a; is
assumed to be uncorrelated with the idiosyncratic error, Uit.
This correlation
cause the fixed effects estimators to be biased.
Transcribed Image Text:Consider the following unobserved effects model: Yit =B₁xit1 + ... +ßkäitk +ai + Uit You plan on using fixed effects to estimate the model. Suppose you have a panel data set consisting of 10 individuals over 6 years. However, you are missing the last 3 years of data for the third, i = 3, individual in your data set. Your panel data set has total observations. Suppose the reason individual i is missing data for certain time periods (called attrition) is correlated with the idiosyncratic error, uit. Recall that a; is assumed to be uncorrelated with the idiosyncratic error, Uit. This correlation cause the fixed effects estimators to be biased.
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