Consider the following unobserved effects model: Yit =B₁ xit1 +...+ßk¤itk + 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, ¿ = 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.
Consider the following unobserved effects model: Yit =B₁ xit1 +...+ßk¤itk + 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, ¿ = 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.
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.2: The Least Square Line
Problem 7E
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Transcribed Image Text:Consider the following unobserved effects model:
Yit = ₁xit1 +...+ ßkXitk + Aį + 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 ai is
assumed to be uncorrelated with the idiosyncratic error, uit.
This correlation
cause the fixed effects estimators to be biased.
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