, ECS4863 Assignment 3 (COMPLETE ANSWERS) 2025 – DUE 18
August 2025; ;100% trusted ,comprehensive and complete
reliable solution with clear explanation
Question 1: (15 marks) a) Using your own words, explain the reasons
for preferring random effects over pooled Ordinary least squares
method (3)
1. Controls for unobserved differences
In panel data, there are characteristics of each entity (like individuals,
firms, or countries) that do not change over time but may affect the
dependent variable. Examples could include cultural traits, management
style, or geographical location. Random effects models include these
differences as part of the error term structure, treating them as random
variables. This helps avoid omitted variable bias, which happens when
these unobserved factors are correlated with explanatory variables.
Pooled OLS ignores these fixed characteristics, which can make its
estimates biased and unreliable.
2. Recognises panel structure
Panel data consists of repeated observations for the same entities over
time, meaning that some correlation naturally exists within each entity’s
observations. Random effects models allow for this intra-entity
correlation by adjusting the error term, so that standard errors are
correctly estimated. In contrast, pooled OLS treats all observations as
independent and identically distributed, which is often unrealistic in
panel settings. Ignoring the correlation can lead to underestimated
August 2025; ;100% trusted ,comprehensive and complete
reliable solution with clear explanation
Question 1: (15 marks) a) Using your own words, explain the reasons
for preferring random effects over pooled Ordinary least squares
method (3)
1. Controls for unobserved differences
In panel data, there are characteristics of each entity (like individuals,
firms, or countries) that do not change over time but may affect the
dependent variable. Examples could include cultural traits, management
style, or geographical location. Random effects models include these
differences as part of the error term structure, treating them as random
variables. This helps avoid omitted variable bias, which happens when
these unobserved factors are correlated with explanatory variables.
Pooled OLS ignores these fixed characteristics, which can make its
estimates biased and unreliable.
2. Recognises panel structure
Panel data consists of repeated observations for the same entities over
time, meaning that some correlation naturally exists within each entity’s
observations. Random effects models allow for this intra-entity
correlation by adjusting the error term, so that standard errors are
correctly estimated. In contrast, pooled OLS treats all observations as
independent and identically distributed, which is often unrealistic in
panel settings. Ignoring the correlation can lead to underestimated
