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ⓘ Random effects model. In statistics, a random effects model, also called a variance components model, is a statistical model where the model parameters are rand ..




Random effects model
                                     

ⓘ Random effects model

In statistics, a random effects model, also called a variance components model, is a statistical model where the model parameters are random variables. It is a kind of hierarchical linear model, which assumes that the data being analysed are drawn from a hierarchy of different populations whose differences relate to that hierarchy. In econometrics, random effects models are used in panel analysis of hierarchical or panel data when one assumes no fixed effects. The random effects model is a special case of the fixed effects model.

Contrast this to the biostatistics definitions, as biostatisticians use "fixed" and "random" effects to respectively refer to the population-average and subject-specific effects and where the latter are generally assumed to be unknown, latent variables.

                                     

1. Qualitative description

Random effect models assist in controlling for unobserved heterogeneity when the heterogeneity is constant over time and not correlated with independent variables. This constant can be removed from longitudinal data through differencing, since taking a first difference will remove any time invariant components of the model.

Two common assumptions can be made about the individual specific effect: the random effects assumption and the fixed effects assumption. The random effects assumption is that the individual unobserved heterogeneity is uncorrelated with the independent variables. The fixed effect assumption is that the individual specific effect is correlated with the independent variables.

If the random effects assumption holds, the random effects model is more efficient than the fixed effects model. However, if this assumption does not hold, the random effects model is not consistent.

                                     

2. Simple example

Suppose m large elementary schools are chosen randomly from among thousands in a large country. Suppose also that n pupils of the same age are chosen randomly at each selected school. Their scores on a standard aptitude test are ascertained. Let Y ij be the score of the j th pupil at the i th school. A simple way to model the relationships of these quantities is

Y i j = μ + U i + W i j, {\displaystyle Y_{ij}=\mu +U_{i}+W_{ij},\,}

where μ is the average test score for the entire population. In this model U i is the school-specific random effect: it measures the difference between the average score at school i and the average score in the entire country. The term W ij is the individual-specific random effect, i.e., its the deviation of the j -th pupil’s score from the average for the i -th school.

The model can be augmented by including additional explanatory variables, which would capture differences in scores among different groups. For example:

Y i j = μ + β 1 S e x i j + β 2 P a r e n t s E d u c i j + U i + W i j, {\displaystyle Y_{ij}=\mu +\beta _{1}\mathrm {Sex} _{ij}+\beta _{2}\mathrm {ParentsEduc} _{ij}+U_{i}+W_{ij},\,}

where Sex ij is the dummy variable for boys/girls and ParentsEduc ij records, say, the average education level of a child’s parents. This is a mixed model, not a purely random effects model, as it introduces fixed-effects terms for Sex and Parents Education.

                                     

2.1. Simple example Variance components

The variance of Y ij is the sum of the variances τ 2 and σ 2 of U i and W ij respectively.

Let

Y ¯ i ∙ = 1 n ∑ j = 1 n Y i j {\displaystyle {\overline {Y}}_{i\bullet }={\frac {1}{n}}\sum _{j=1}^{n}Y_{ij}}

be the average, not of all scores at the i th school, but of those at the i th school that are included in the random sample. Let

Y ¯ ∙ ∙ = 1 m n ∑ i = 1 m ∑ j = 1 n Y i j {\displaystyle {\overline {Y}}_{\\bullet }={\frac {1}{mn}}\sum _{i=1}^{m}\sum _{j=1}^{n}Y_{ij}}

be the grand average.

Let

S W = ∑ i = 1 m ∑ j = 1 n Y i j − Y ¯ i ∙ 2 {\displaystyle SSW=\sum _{i=1}^{m}\sum _{j=1}^{n}Y_{ij}-{\overline {Y}}_{i\bullet }^{2}\,} S B = n ∑ i = 1 m Y ¯ i ∙ − Y ¯ ∙ ∙ 2 {\displaystyle SSB=n\sum _{i=1}^{m}{\overline {Y}}_{i\bullet }-{\overline {Y}}_{\\bullet }^{2}\,}

be respectively the sum of squares due to differences within groups and the sum of squares due to difference between groups. Then it can be shown that

1 m n − 1 E S W = σ 2 {\displaystyle {\frac {1}{mn-1}}ESSW=\sigma ^{2}}

and

1 m − 1 n E S B = σ 2 n + τ 2. {\displaystyle {\frac {1}{m-1n}}ESSB={\frac {\sigma ^{2}}{n}}+\tau ^{2}.}

These "expected mean squares" can be used as the basis for estimation of the "variance components" σ 2 and τ 2.



                                     

3. Unbiasedness

In general, random effects are efficient, and should be used over fixed effects if the assumptions underlying them are believed to be satisfied. For random effects to work in the school example it is necessary that the school-specific effects be uncorrelated to the other covariates of the model. This can be tested by running fixed effects, then random effects, and doing a Hausman specification test. If the test rejects, then random effects is biased and fixed effects is the correct estimation procedure.

                                     
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