I don’t understand why any researcher would choose not to use panel/multilevel methods on panel/hierarchical data. Let’s take the following linear regression as an example:
where 
Let’s have a look at the consequences of this inefficiency using a simulation. I will simulate the following model:
with 
The graph below shows the importance of using panel methods over pooled OLS. It shows boxplots of the 100 simulated estimates. Even when the random effects assumption is violated, the random effects estimator (RE) is far superior to simple pooled OLS. Both the CRE and FE estimators perform well. Both have lowest root mean square errors, even though the model satisfies the random effects assumption. Please see my R code below.
# clear ws
rm(list=ls())
# load packages
library(lme4)
library(plyr)
library(lfe)
library(reshape)
library(ggplot2)
# from this:
### set number of individuals
n = 200
# time periods
t = 5
### model is: y=beta0_{i} +beta1*x_{it} + e_{it}
### average intercept and slope
beta0 = 1.0
beta1 = 5.0
### set loop reps
loop = 100
### results to be entered
results1 = matrix(NA, nrow=loop, ncol=4)
results2 = matrix(NA, nrow=loop, ncol=4)
for(i in 1:loop){
# basic data structure
data = data.frame(t = rep(1:t,n),
n = sort(rep(1:n,t)))
# random effect/intercept to add to each
rand = data.frame(n = 1:n,
a = rnorm(n,0,3))
data = join(data, rand, match="first")
# random error
data$u = rnorm(nrow(data), 0, 1)
# regressor x
data$x = runif(nrow(data), 0, 1)
# outcome y
data$y = beta0 + beta1*data$x + data$a + data$u
# make factor for i-units
data$n = as.character(data$n)
# group i mean's for correlated random effects
data$xn = ave(data$x, data$n, FUN=mean)
# pooled OLS
a1 = lm(y ~ x, data)
# random effects
a2 = lmer(y ~ x + (1|n), data)
# correlated random effects
a3 = lmer(y ~ x + xn + (1|n), data)
# fixed effects
a4 = felm(y ~ x | n, data)
# gather results
results1[i,] = c(coef(a1)[2],
coef(a2)$n[1,2],
coef(a3)$n[1,2],
coef(a4)[1])
### now let random effects assumption be false
### ie E[xa]!=0
data$x = runif(nrow(data), 0, 1) + 0.2*data$a
# the below is like above
data$y = beta0 + beta1*data$x + data$a + data$u
data$n = as.character(data$n)
data$xn = ave(data$x, data$n, FUN=mean)
a1 = lm(y ~ x, data)
a2 = lmer(y ~ x + (1|n), data)
a3 = lmer(y ~ x + xn + (1|n), data)
a4 = felm(y ~ x | n, data)
results2[i,] = c(coef(a1)[2],
coef(a2)$n[1,2],
coef(a3)$n[1,2],
coef(a4)[1])
}
# calculate rmse
apply(results1, 2, function(x) sqrt(mean((x-5)^2)))
apply(results2, 2, function(x) sqrt(mean((x-5)^2)))
# shape data and do ggplot
model.names = data.frame(X2 = c("1","2","3","4"),
estimator = c("OLS","RE","CRE","FE"))
res1 = melt(results1)
res1 = join(res1, model.names, match="first")
res2 = melt(results2)
res2 = join(res2, model.names, match="first")
res1$split = "RE Valid"
res2$split = "RE Invalid"
res1 = rbind(res1, res2)
res1$split = factor(res1$split, levels = c("RE Valid", "RE Invalid"))
res1$estimator = factor(res1$estimator, levels = c("OLS","RE","CRE","FE"))
number_ticks = function(n) {function(limits) pretty(limits, n)}
ggplot(res1, aes(estimator, value)) +
geom_boxplot(fill="lightblue") +
#coord_flip() +
facet_wrap( ~ split, nrow = 2, scales = "free_y") +
geom_hline(yintercept = 5) +
scale_x_discrete('') +
scale_y_continuous(bquote(beta==5), breaks=number_ticks(3)) +
theme_bw() +
theme(axis.text=element_text(size=16),
axis.title=element_text(size=16),
legend.title = element_blank(),
legend.text = element_text(size=16),
strip.text.x = element_text(size = 16),
axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("remc.pdf", width=8, height=6)
, we can modify this equation in a number of ways to account for the spatial structure of the data. In this blog post, I will concentrate on the spatial lag model. I intend to examine spatial error models in a future blog post.
, where the term 
measures the potential spill-over effect that occurs in the outcome variable if this outcome is influenced by other unit’s outcomes, where the location or distance to other observations is a factor in for this spill-over. In other words, the neighbours for each observation have greater (or in some cases less) influence to what happens to that observation, independent of the other explanatory variables 
. The 
matrix is a matrix of spatial weights, and the 
parameter measures the degree of spatial correlation. The value of 
matrix, where the diagonal consists of 3 zeros (you are not a neighbour with yourself). Typically, this matrix will also be symmetric. It is also at the user’s discretion to choose the weights in 
is endogenous. The endogeneity results from what Charles Manski calls the ‘
coefficient values due to omitted variable bias.
) of the explanatory variables are used as instruments for 
. Alongside these two models, I also estimate the misspecified OLS both without (OLS1) and with (OLS2) the spatially lagged dependent variable.
lattice grid. Second, we assume that neighbours are defined by what is known as the Rook contiguity, so a neighbour exists if they are in the grid space either above or below or on either side. Once we create the spatial weight matrix we row-standardize.
, where we set 
, 
, 
, 
. The explanatory variables are themselves spatially autocorrelated, so our simulation procedure first simulates a random normal variable for both 
and 
from: 
, then assuming a autocorrelation parameter of 
, generates both variables such that: 
for 
. In the next step we simulate the error term 
. We introduce endogeneity into the spatial lag by assuming that the error term 
so 
where 
and 
, and that the spatial lag term includes 
. From this we can calculate the reduced form model: 
, and simulate values for our dependent variable 
.
DiffusePrioR 