Consider the following panel model to examine the effect of retirement on consumption expenditure, consit, of individual i over years t=1,…,3:(B1) log⁡(consit) = β0 + β1retiredit + β2ageit + β3marriedit + β4healthit + δ1Yr2t + δ2Yr3t + ai + uitWhere:retiredit is a dummy variable equal to 1 if individual i is retired on year t and 0 otherwiseageit is the individual's age in yearsmarriedit is an indicator variable for whether the individual is married (1) or not (0) in year thealthit is an indicator variable equal to 1 if the individual is in 'good health' and 0 otherwiseYr2 is a dummy variable equal to 1 in year t=2 and 0 otherwiseYr3 is a dummy variable equal to 1 in year t=3 and 0 otherwiseUsing the information above, answer the following 3 questions.[i] Give two (2) examples of the kind of variables captured by the term ai in Model (B1). [ii] What is the crucial assumption we must make so that the random effects (RE) estimator is consistent? Under this assumption, why is RE more preferred to pooled OLS? [iii] Outline the key idea of the fixed effects (FE) transformation underlying the FE estimator. Why is this also called a 'within' estimator?

A random effects (RE) model is one in which the model parameters are random variables. This model…

Answered: Consider the following panel model to…

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.

Chapter2: Exponential, Logarithmic, And Trigonometric Functions

Section2.CR: Chapter 2 Review

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Consider the following panel model to examine the effect of retirement on consumption expenditure, consit, of individual i over years t=1,…,3:

(B1) log⁡(cons_it) = β₀ + β₁retired_it + β₂age_it + β₃married_it + β₄health_it + δ₁Yr2_t + δ₂Yr3_t + a_i + u_it

Where:

retired_it is a dummy variable equal to 1 if individual i is retired on year t and 0 otherwise
age_it is the individual's age in years
married_it is an indicator variable for whether the individual is married (1) or not (0) in year t
health_it is an indicator variable equal to 1 if the individual is in 'good health' and 0 otherwise
Yr2 is a dummy variable equal to 1 in year t=2 and 0 otherwise
Yr3 is a dummy variable equal to 1 in year t=3 and 0 otherwise

Using the information above, answer the following 3 questions.

[i] Give two (2) examples of the kind of variables captured by the term ai in Model (B1).

[ii] What is the crucial assumption we must make so that the random effects (RE) estimator is consistent? Under this assumption, why is RE more preferred to pooled OLS?

[iii] Outline the key idea of the fixed effects (FE) transformation underlying the FE estimator. Why is this also called a 'within' estimator?

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