### Archive

Posts Tagged ‘multilevel models’

## Multilevel linear models in Stata, part 2: Longitudinal data

In my last posting, I introduced you to the concepts of hierarchical or “multilevel” data. In today’s post, I’d like to show you how to use multilevel modeling techniques to analyse longitudinal data with Stata’s xtmixed command.

Last time, we noticed that our data had two features. First, we noticed that the means within each level of the hierarchy were different from each other and we incorporated that into our data analysis by fitting a “variance component” model using Stata’s xtmixed command.

The second feature that we noticed is that repeated measurement of GSP showed an upward trend. We’ll pick up where we left off last time and stick to the concepts again and you can refer to the references at the end to learn more about the details.

## The videos

Stata has a very friendly dialog box that can assist you in building multilevel models. If you would like a brief introduction using the GUI, you can watch a demonstration on Stata’s YouTube Channel:

Introduction to multilevel linear models in Stata, part 2: Longitudinal data

## Longitudinal data

I’m often asked by beginning data analysts – “What’s the difference between longitudinal data and time-series data? Aren’t they the same thing?”.

The confusion is understandable — both types of data involve some measurement of time. But the answer is no, they are not the same thing.

Univariate time series data typically arise from the collection of many data points over time from a single source, such as from a person, country, financial instrument, etc.

Longitudinal data typically arise from collecting a few observations over time from many sources, such as a few blood pressure measurements from many people.

There are some multivariate time series that blur this distinction but a rule of thumb for distinguishing between the two is that time series have more repeated observations than subjects while longitudinal data have more subjects than repeated observations.

Because our GSP data from last time involve 17 measurements from 48 states (more sources than measurements), we will treat them as longitudinal data.

## Random intercept models

As I mentioned last time, repeated observations on a group of individuals can be conceptualized as multilevel data and modeled just as any other multilevel data. We left off last time with a variance component model for GSP (Gross State Product, logged) and noted that our model assumed a constant GSP over time while the data showed a clear upward trend.

If we consider a single observation and think about our model, nothing in the fixed or random part of the models is a function of time.

Let’s begin by adding the variable year to the fixed part of our model.

As we expected, our grand mean has become a linear regression which more accurately reflects the change over time in GSP. What might be unexpected is that each state’s and region’s mean has changed as well and now has the same slope as the regression line. This is because none of the random components of our model are a function of time. Let’s fit this model with the xtmixed command:

. xtmixed gsp year, || region: || state:

------------------------------------------------------------------------------
gsp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
year |   .0274903   .0005247    52.39   0.000     .0264618    .0285188
_cons |  -43.71617   1.067718   -40.94   0.000    -45.80886   -41.62348
------------------------------------------------------------------------------

------------------------------------------------------------------------------
Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
region: Identity             |
sd(_cons) |   .6615238   .2038949      .3615664    1.210327
-----------------------------+------------------------------------------------
state: Identity              |
sd(_cons) |   .7805107   .0885788      .6248525    .9749452
-----------------------------+------------------------------------------------
sd(Residual) |   .0734343   .0018737      .0698522    .0772001
------------------------------------------------------------------------------

The fixed part of our model now displays an estimate of the intercept (_cons = -43.7) and the slope (year = 0.027). Let’s graph the model for Region 7 and see if it fits the data better than the variance component model.

predict GrandMean, xb
label var GrandMean "GrandMean"
predict RegionEffect, reffects level(region)
predict StateEffect, reffects level(state)
gen RegionMean = GrandMean + RegionEffect
gen StateMean = GrandMean + RegionEffect + StateEffect

twoway  (line GrandMean year, lcolor(black) lwidth(thick))      ///
(line RegionMean year, lcolor(blue) lwidth(medthick))   ///
(line StateMean year, lcolor(green) connect(ascending)) ///
(scatter gsp year, mcolor(red) msize(medsmall))         ///
if region ==7,                                          ///
ytitle(log(Gross State Product), margin(medsmall))      ///
legend(cols(4) size(small))                             ///
title("Multilevel Model of GSP for Region 7", size(medsmall))

That looks like a much better fit than our variance-components model from last time. Perhaps I should leave well enough alone, but I can’t help noticing that the slopes of the green lines for each state don’t fit as well as they could. The top green line fits nicely but the second from the top looks like it slopes upward more than is necessary. That’s the best fit we can achieve if the regression lines are forced to be parallel to each other. But what if the lines were not forced to be parallel? What if we could fit a “mini-regression model” for each state within the context of my overall multilevel model. Well, good news — we can!

## Random slope models

By introducing the variable year to the fixed part of the model, we turned our grand mean into a regression line. Next I’d like to incorporate the variable year into the random part of the model. By introducing a fourth random component that is a function of time, I am effectively estimating a separate regression line within each state.

Notice that the size of the new, brown deviation u1ij. is a function of time. If the observation were one year to the left, u1ij. would be smaller and if the observation were one year to the right, u1ij.would be larger.

It is common to “center” the time variable before fitting these kinds of models. Explaining why is for another day. The quick answer is that, at some point during the fitting of the model, Stata will have to compute the equivalent of the inverse of the square of year. For the year 1986 this turns out to be 2.535e-07. That’s a fairly small number and if we multiply it by another small number…well, you get the idea. By centering age (e.g. cyear = year – 1978), we get a more reasonable number for 1986 (0.01). (Hint: If you have problems with your model converging and you have large values for time, try centering them. It won’t always help, but it might).

So let’s center our year variable by subtracting 1978 and fit a model that includes a random slope.

gen cyear = year - 1978
xtmixed gsp cyear, || region: || state: cyear, cov(indep)

I’ve color-coded the output so that we can match each part of the output back to the model and the graph. The fixed part of the model appears in the top table and it looks like any other simple linear regression model. The random part of the model is definitely more complicated. If you get lost, look back at the graphic of the deviations and remind yourself that we have simply partitioned the deviation of each observation into four components. If we did this for every observation, the standard deviations in our output are simply the average of those deviations.

Let’s look at a graph of our new “random slope” model for Region 7 and see how well it fits our data.

predict GrandMean, xb
label var GrandMean "GrandMean"
predict RegionEffect, reffects level(region)
predict StateEffect_year StateEffect_cons, reffects level(state)

gen RegionMean = GrandMean + RegionEffect
gen StateMean_cons = GrandMean + RegionEffect + StateEffect_cons
gen StateMean_year = GrandMean + RegionEffect + StateEffect_cons + ///
(cyear*StateEffect_year)

twoway  (line GrandMean cyear, lcolor(black) lwidth(thick))             ///
(line RegionMean cyear, lcolor(blue) lwidth(medthick))          ///
(line StateMean_cons cyear, lcolor(green) connect(ascending))   ///
(line StateMean_year cyear, lcolor(brown) connect(ascending))   ///
(scatter gsp cyear, mcolor(red) msize(medsmall))                ///
if region ==7,                                                  ///
ytitle(log(Gross State Product), margin(medsmall))              ///
legend(cols(3) size(small))                                     ///
title("Multilevel Model of GSP for Region 7", size(medsmall))

The top brown line fits the data slightly better, but the brown line below it (second from the top) is a much better fit. Mission accomplished!

## Where do we go from here?

I hope I have been able to convince you that multilevel modeling is easy using Stata’s xtmixed command and that this is a tool that you will want to add to your kit. I would love to say something like “And that’s all there is to it. Go forth and build models!”, but I would be remiss if I didn’t point out that I have glossed over many critical topics.

In our GSP example, we would still like to consider the impact of other independent variables. I haven’t mentioned choice of estimation methods (ML or REML in the case of xtmixed). I’ve assessed the fit of our models by looking at graphs, an approach important but incomplete. We haven’t thought about hypothesis testing. Oh — and, all the usual residual diagnostics for linear regression such as checking for outliers, influential observations, heteroskedasticity and normality still apply….times four! But now that you understand the concepts and some of the mechanics, it shouldn’t be difficult to fill in the details. If you’d like to learn more, check out the links below.

I hope this was helpful…thanks for stopping by.

Multilevel and Longitudinal Modeling Using Stata, Third Edition
Volume I: Continuous Responses
Volume II: Categorical Responses, Counts, and Survival
by Sophia Rabe-Hesketh and Anders Skrondal

or sign up for our popular public training course Multilevel/Mixed Models Using Stata.

Categories: Statistics Tags:

## Multilevel linear models in Stata, part 1: Components of variance

In the last 15-20 years multilevel modeling has evolved from a specialty area of statistical research into a standard analytical tool used by many applied researchers.

Stata has a lot of multilevel modeling capababilities.

I want to show you how easy it is to fit multilevel models in Stata. Along the way, we’ll unavoidably introduce some of the jargon of multilevel modeling.

I’m going to focus on concepts and ignore many of the details that would be part of a formal data analysis. I’ll give you some suggestions for learning more at the end of the post.

The videos

Stata has a friendly dialog box that can assist you in building multilevel models. If you would like a brief introduction using the GUI, you can watch a demonstration on Stata’s YouTube Channel:

Introduction to multilevel linear models in Stata, part 1: The xtmixed command

Multilevel data

Multilevel data are characterized by a hierarchical structure. A classic example is children nested within classrooms and classrooms nested within schools. The test scores of students within the same classroom may be correlated due to exposure to the same teacher or textbook. Likewise, the average test scores of classes might be correlated within a school due to the similar socioeconomic level of the students.

You may have run across datasets with these kinds of structures in your own work. For our example, I would like to use a dataset that has both longitudinal and classical hierarchical features. You can access this dataset from within Stata by typing the following command:

use http://www.stata-press.com/data/r12/productivity.dta

We are going to build a model of gross state product for 48 states in the USA measured annually from 1970 to 1986. The states have been grouped into nine regions based on their economic similarity. For distributional reasons, we will be modeling the logarithm of annual Gross State Product (GSP) but in the interest of readability, I will simply refer to the dependent variable as GSP.

. describe gsp year state region

storage  display     value
variable name   type   format      label      variable label
-----------------------------------------------------------------------------
gsp             float  %9.0g                  log(gross state product)
year            int    %9.0g                  years 1970-1986
state           byte   %9.0g                  states 1-48
region          byte   %9.0g                  regions 1-9

Let’s look at a graph of these data to see what we’re working with.

twoway (line gsp year, connect(ascending)), ///
by(region, title("log(Gross State Product) by Region", size(medsmall)))

Each line represents the trajectory of a state’s (log) GSP over the years 1970 to 1986. The first thing I notice is that the groups of lines are different in each of the nine regions. Some groups of lines seem higher and some groups seem lower. The second thing that I notice is that the slopes of the lines are not the same. I’d like to incorporate those attributes of the data into my model.

Components of variance

Let’s tackle the vertical differences in the groups of lines first. If we think about the hierarchical structure of these data, I have repeated observations nested within states which are in turn nested within regions. I used color to keep track of the data hierarchy.

We could compute the mean GSP within each state and note that the observations within in each state vary about their state mean.

Likewise, we could compute the mean GSP within each region and note that the state means vary about their regional mean.

We could also compute a grand mean and note that the regional means vary about the grand mean.

Next, let’s introduce some notation to help us keep track of our mutlilevel structure. In the jargon of multilevel modelling, the repeated measurements of GSP are described as “level 1”, the states are referred to as “level 2” and the regions are “level 3”. I can add a three-part subscript to each observation to keep track of its place in the hierarchy.

Now let’s think about our model. The simplest regression model is the intercept-only model which is equivalent to the sample mean. The sample mean is the “fixed” part of the model and the difference between the observation and the mean is the residual or “random” part of the model. Econometricians often prefer the term “disturbance”. I’m going to use the symbol μ to denote the fixed part of the model. μ could represent something as simple as the sample mean or it could represent a collection of independent variables and their parameters.

Each observation can then be described in terms of its deviation from the fixed part of the model.

If we computed this deviation of each observation, we could estimate the variability of those deviations. Let’s try that for our data using Stata’s xtmixed command to fit the model:

. xtmixed gsp

Mixed-effects ML regression                     Number of obs      =       816

Wald chi2(0)       =         .
Log likelihood = -1174.4175                     Prob > chi2        =         .

------------------------------------------------------------------------------
gsp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons |   10.50885   .0357249   294.16   0.000     10.43883    10.57887
------------------------------------------------------------------------------

------------------------------------------------------------------------------
Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
sd(Residual) |   1.020506   .0252613      .9721766    1.071238
------------------------------------------------------------------------------

The top table in the output shows the fixed part of the model which looks like any other regression output from Stata, and the bottom table displays the random part of the model. Let’s look at a graph of our model along with the raw data and interpret our results.

predict GrandMean, xb
label var GrandMean "GrandMean"
twoway  (line GrandMean year, lcolor(black) lwidth(thick))              ///
(scatter gsp year, mcolor(red) msize(tiny)),                    ///
ytitle(log(Gross State Product), margin(medsmall))              ///
legend(cols(4) size(small))                                     ///
title("GSP for 1970-1986 by Region", size(medsmall))

The thick black line in the center of the graph is the estimate of _cons, which is an estimate of the fixed part of model for GSP. In this simple model, _cons is the sample mean which is equal to 10.51. In “Random-effects Parameters” section of the output, sd(Residual) is the average vertical distance between each observation (the red dots) and fixed part of the model (the black line). In this model, sd(Residual) is the estimate of the sample standard deviation which equals 1.02.

At this point you may be thinking to yourself – “That’s not very interesting – I could have done that with Stata’s summarize command”. And you would be correct.

. summ gsp

Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
gsp |       816    10.50885    1.021132    8.37885   13.04882

But here’s where it does become interesting. Let’s make the random part of the model more complex to account for the hierarchical structure of the data. Consider a single observation, yijk and take another look at its residual.

The observation deviates from its state mean by an amount that we will denote eijk. The observation’s state mean deviates from the the regionals mean uij. and the observation’s regional mean deviates from the fixed part of the model, μ, by an amount that we will denote ui... We have partitioned the observation’s residual into three parts, aka “components”, that describe its magnitude relative to the state, region and grand means. If we calculated this set of residuals for each observation, wecould estimate the variability of those residuals and make distributional assumptions about them.

These kinds of models are often called “variance component” models because they estimate the variability accounted for by each level of the hierarchy. We can estimate a variance component model for GSP using Stata’s xtmixed command:

xtmixed gsp, || region: || state:

------------------------------------------------------------------------------
gsp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons |   10.65961   .2503806    42.57   0.000     10.16887    11.15035
------------------------------------------------------------------------------

------------------------------------------------------------------------------
Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
region: Identity             |
sd(_cons) |   .6615227   .2038944       .361566    1.210325
-----------------------------+------------------------------------------------
state: Identity              |
sd(_cons) |   .7797837   .0886614      .6240114    .9744415
-----------------------------+------------------------------------------------
sd(Residual) |   .1570457   .0040071       .149385    .1650992
------------------------------------------------------------------------------

The fixed part of the model, _cons, is still the sample mean. But now there are three parameters estimates in the bottom table labeled “Random-effects Parameters”. Each quantifies the average deviation at each level of the hierarchy.

Let’s graph the predictions from our model and see how well they fit the data.

predict GrandMean, xb
label var GrandMean "GrandMean"
predict RegionEffect, reffects level(region)
predict StateEffect, reffects level(state)
gen RegionMean = GrandMean + RegionEffect
gen StateMean = GrandMean + RegionEffect + StateEffect

twoway  (line GrandMean year, lcolor(black) lwidth(thick))      ///
(line RegionMean year, lcolor(blue) lwidth(medthick))   ///
(line StateMean year, lcolor(green) connect(ascending)) ///
(scatter gsp year, mcolor(red) msize(tiny)),            ///
ytitle(log(Gross State Product), margin(medsmall))      ///
legend(cols(4) size(small))                             ///
by(region, title("Multilevel Model of GSP by Region", size(medsmall)))

Wow – that’s a nice graph if I do say so myself. It would be impressive for a report or publication, but it’s a little tough to read with all nine regions displayed at once. Let’s take a closer look at Region 7 instead.

twoway  (line GrandMean year, lcolor(black) lwidth(thick))      ///
(line RegionMean year, lcolor(blue) lwidth(medthick))   ///
(line StateMean year, lcolor(green) connect(ascending)) ///
(scatter gsp year, mcolor(red) msize(medsmall))         ///
if region ==7,                                          ///
ytitle(log(Gross State Product), margin(medsmall))      ///
legend(cols(4) size(small))                             ///
title("Multilevel Model of GSP for Region 7", size(medsmall))

The red dots are the observations of GSP for each state within Region 7. The green lines are the estimated mean GSP within each State and the blue line is the estimated mean GSP within Region 7. The thick black line in the center is the overall grand mean for all nine regions. The model appears to fit the data fairly well but I can’t help noticing that the red dots seem to have an upward slant to them. Our model predicts that GSP is constant within each state and region from 1970 to 1986 when clearly the data show an upward trend.

So we’ve tackled the first feature of our data. We’ve succesfully incorporated the basic hierarchical structure into our model by fitting a variance componentis using Stata’s xtmixed command. But our graph tells us that we aren’t finished yet.

Next time we’ll tackle the second feature of our data — the longitudinal nature of the observations.

Multilevel and Longitudinal Modeling Using Stata, Third Edition
Volume I: Continuous Responses
Volume II: Categorical Responses, Counts, and Survival
by Sophia Rabe-Hesketh and Anders Skrondal

or sign up for our popular public training course “Multilevel/Mixed Models Using Stata“.

There’s a course coming up in Washington, DC on February 7-8, 2013.

Categories: Statistics Tags:

## Multilevel random effects in xtmixed and sem — the long and wide of it

xtmixed was built from the ground up for dealing with multilevel random effects — that is its raison d’être. sem was built for multivariate outcomes, for handling latent variables, and for estimating structural equations (also called simultaneous systems or models with endogeneity). Can sem also handle multilevel random effects (REs)? Do we care?

This would be a short entry if either answer were “no”, so let’s get after the first question.

Can sem handle multilevel REs?

A good place to start is to simulate some multilevel RE data. Let’s create data for the 3-level regression model

where the classical multilevel regression assumption holds that and are distributed normal and are uncorrelated.

This represents a model of nested within nested within . An example would be students nested within schools nested within counties. We have random intercepts at the 2nd and 3rd levels — , . Because these are random effects, we need estimate only the variance of , , and .

For our simulated data, let’s assume there are 3 groups at the 3rd level, 2 groups at the 2nd level within each 3rd level group, and 2 individuals within each 2nd level group. Or, , , and . Having only 3 groups at the 3rd level is silly. It gives us only 3 observations to estimate the variance of . But with only observations, we will be able to easily see our entire dataset, and the concepts scale to any number of 3rd-level groups.

First, create our 3rd-level random effects — .

. set obs 3
. gen k = _n
. gen Uk = rnormal()

There are only 3 in our dataset.

I am showing the effects symbolically in the table rather than showing numeric values. It is the pattern of unique effects that will become interesting, not their actual values.

Now, create our 2nd-level random effects — — by doubling this data and creating 2nd-level effects.

. expand 2
. by k, sort: gen j = _n
. gen Vjk = rnormal()

We have 6 unique values of our 2nd-level effects and the same 3 unique values of our 3rd-level effects. Our original 3rd-level effects just appear twice each.

Now, create our 1st-level random effects — — which we typically just call errors.

. expand 2
. by k j, sort: gen i = _n
. gen Eijk = rnormal()

There are still only 3 unique in our dataset, and only 6 unique .

Finally, we create our regression data, using ,

. gen xijk = runiform()
. gen yijk = 2 * xijk + Uk + Vjk + Eijk

We could estimate our multilevel RE model on this data by typing,

. xtmixed yijk xijk || k: || j:

xtmixed uses the index variables k and j to deeply understand the multilevel structure of the our data. sem has no such understanding of multilevel data. What it does have is an understanding of multivariate data and a comfortable willingness to apply constraints.

Let’s restructure our data so that sem can be made to understand its multilevel structure.

First some renaming so that the results of our restructuring will be easier to interpret.

. rename Uk U
. rename Vjk V
. rename Eijk E
. rename xijk x
. rename yijk y

We reshape to turn our multilevel data into multivariate data that sem has a chance of understanding. First, we reshape wide on our 2nd-level identifier j. Before that, we egen to create a unique identifier for each observation of the two groups identified by j.

. egen ik = group(i k)
. reshape wide y x E V, i(ik) j(j)

We now have a y variable for each group in j (y1 and y2). Likewise, we have two x variables, two residuals, and most importantly two 2nd-level random effects V1 and V2. This is the same data, we have merely created a set of variables for every level of j. We have gone from multilevel to multivariate.
We still have a multilevel component. There are still two levels of i in our dataset. We must reshape wide again to remove any remnant of multilevel structure.

. drop ik
. reshape wide y* x* E*, i(k) j(i)

I admit that is a microscopic font, but it is the structure that is important, not the values. We now have 4 y’s, one for each combination of 2nd- and 3rd-level identifiers — i and j. Likewise for the x’s and E’s.

We can think of each xji yji pair of columns as representing a regression for a specific combination of j and i — y11 on x11, y12 on x12, y21 on x21, and y22 on x22. Or, more explicitly,

So, rather than a univariate multilevel regression with 4 nested observation sets, () * (), we now have 4 regressions which are all related through and each of two pairs are related through . Oh, and all share the same coefficient . Oh, and the all have identical variances. Oh, and the also have identical variances. Luckily both the sem command and the SEM Builder (the GUI for sem) make setting constraints easy.

There is one other thing we haven’t addressed. xtmixed understands random effects. Does sem? Random effects are just unobserved (latent) variables and sem clearly understands those. So, yes, sem does understand random effects.

Many SEMers would represent this model in a path diagram by drawing.

There is a lot of information in that diagram. Each regression is represented by one of the x boxes being connected by a path to a y box. That each of the four paths is labeled with means that we have constrained the regressions to have the same coefficient. The y21 and y22 boxes also receive input from the random latent variable V2 (representing our 2nd-level random effects). The other two y boxes receive input from V1 (also our 2nd-level random effects). For this to match how xtmixed handles random effects, V1 and V2 must be constrained to have the same variance. This was done in the path diagram by “locking” them to have the same variance — S_v. To match xtmixed, each of the four residuals must also have the same variance — shown in the diagram as S_e. The residuals and random effect variables also have their paths constrained to 1. That is to say, they do not have coefficients.

We do not need any of the U, V, or E variables. We kept these only to make clear how the multilevel data was restructured to multivariate data. We might “follow the money” in a criminal investigation, but with simulated multilevel data is is best to “follow the effects”. Seeing how these effects were distributed in our reshaped data made it clear how they entered our multivariate model.

Just to prove that this all works, here are the results from a simulated dataset ( rather than the 3 that we have been using). The xtmixed results are,

. xtmixed yijk xijk || k: || j: , mle var

(log omitted)

Mixed-effects ML regression                     Number of obs      =       400

-----------------------------------------------------------
|   No. of       Observations per Group
Group Variable |   Groups    Minimum    Average    Maximum
----------------+------------------------------------------
k |      100          4        4.0          4
j |      200          2        2.0          2
-----------------------------------------------------------

Wald chi2(1)       =     61.84
Log likelihood = -768.96733                     Prob > chi2        =    0.0000

------------------------------------------------------------------------------
yijk |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
xijk |   1.792529   .2279392     7.86   0.000     1.345776    2.239282
_cons |    .460124   .2242677     2.05   0.040     .0205673    .8996807
------------------------------------------------------------------------------

------------------------------------------------------------------------------
Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
k: Identity                  |
var(_cons) |   2.469012   .5386108      1.610034    3.786268
-----------------------------+------------------------------------------------
j: Identity                  |
var(_cons) |   1.858889    .332251      1.309522    2.638725
-----------------------------+------------------------------------------------
var(Residual) |   .9140237   .0915914      .7510369    1.112381
------------------------------------------------------------------------------
LR test vs. linear regression:       chi2(2) =   259.16   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.


The sem results are,

sem (y11 <- x11@bx _cons@c V1@1 U@1)
(y12 <- x12@bx _cons@c V1@1 U@1)
(y21 <- x21@bx _cons@c V2@1 U@1)
(y22 <- x22@bx _cons@c V2@1 U@1) ,
covstruct(_lexog, diagonal) cov(_lexog*_oexog@0)
cov( V1@S_v V2@S_v  e.y11@S_e e.y12@S_e e.y21@S_e e.y22@S_e)

(notes omitted)

Endogenous variables

Observed:  y11 y12 y21 y22

Exogenous variables

Observed:  x11 x12 x21 x22
Latent:    V1 U V2

(iteration log omitted)

Structural equation model                       Number of obs      =       100
Estimation method  = ml
Log likelihood     = -826.63615

(constraint listing omitted)
------------------------------------------------------------------------------
|                 OIM             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Structural   |
y11 <-     |
x11 |   1.792529   .2356323     7.61   0.000     1.330698     2.25436
V1 |          1   7.68e-17  1.3e+16   0.000            1           1
U |          1   2.22e-18  4.5e+17   0.000            1           1
_cons |    .460124    .226404     2.03   0.042     .0163802    .9038677
-----------+----------------------------------------------------------------
y12 <-     |
x12 |   1.792529   .2356323     7.61   0.000     1.330698     2.25436
V1 |          1   2.00e-22  5.0e+21   0.000            1           1
U |          1   5.03e-17  2.0e+16   0.000            1           1
_cons |    .460124    .226404     2.03   0.042     .0163802    .9038677
-----------+----------------------------------------------------------------
y21 <-     |
x21 |   1.792529   .2356323     7.61   0.000     1.330698     2.25436
U |          1   5.70e-46  1.8e+45   0.000            1           1
V2 |          1   5.06e-45  2.0e+44   0.000            1           1
_cons |    .460124    .226404     2.03   0.042     .0163802    .9038677
-----------+----------------------------------------------------------------
y22 <-     |
x22 |   1.792529   .2356323     7.61   0.000     1.330698     2.25436
U |          1  (constrained)
V2 |          1  (constrained)
_cons |    .460124    .226404     2.03   0.042     .0163802    .9038677
-------------+----------------------------------------------------------------
Variance     |
e.y11 |   .9140239    .091602                        .75102    1.112407
e.y12 |   .9140239    .091602                        .75102    1.112407
e.y21 |   .9140239    .091602                        .75102    1.112407
e.y22 |   .9140239    .091602                        .75102    1.112407
V1 |   1.858889   .3323379                      1.309402    2.638967
U |   2.469011   .5386202                      1.610021    3.786296
V2 |   1.858889   .3323379                      1.309402    2.638967
-------------+----------------------------------------------------------------
Covariance   |
x11        |
V1 |          0  (constrained)
U |          0  (constrained)
V2 |          0  (constrained)
-----------+----------------------------------------------------------------
x12        |
V1 |          0  (constrained)
U |          0  (constrained)
V2 |          0  (constrained)
-----------+----------------------------------------------------------------
x21        |
V1 |          0  (constrained)
U |          0  (constrained)
V2 |          0  (constrained)
-----------+----------------------------------------------------------------
x22        |
V1 |          0  (constrained)
U |          0  (constrained)
V2 |          0  (constrained)
-----------+----------------------------------------------------------------
V1         |
U |          0  (constrained)
V2 |          0  (constrained)
-----------+----------------------------------------------------------------
U          |
V2 |          0  (constrained)
------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(25)  =     22.43, Prob > chi2 = 0.6110


And here is the path diagram after estimation.

The standard errors of the two estimation methods are asymptotically equivalent, but will differ in finite samples.

Sidenote: Those familiar with multilevel modeling will be wondering if sem can handle unbalanced data. That is to say a different number of observations or subgroups within groups. It can. Simply let reshape create missing values where it will and then add the method(mlmv) option to your sem command. mlmv stands for maximum likelihood with missing values. And, as strange as it may seem, with this option the multivariate sem representation and the multilevel xtmixed representations are the same.

Do we care?

You will have noticed that the sem command was, well, it was really long. (I wrote a little loop to get all the constraints right.) You will also have noticed that there is a lot of redundant output because our SEM model has so many constraints. Why would anyone go to all this trouble to do something that is so simple with xtmixed? The answer lies in all of those constraints. With sem we can relax any of those constraints we wish!

Relax the constraint that the V# have the same variance and you can introduce heteroskedasticity in the 2nd-level effects. That seems a little silly when there are only two levels, but imagine there were 10 levels.

Add a covariance between the V# and you introduce correlation between the groups in the 3rd level.

What’s more, the pattern of heteroskedasticity and correlation can be arbitrary. Here is our path diagram redrawn to represent children within schools within counties and increasing the number of groups in the 2nd level.

We have 5 counties at the 3rd level and two schools within each county at the 2nd level — for a total of 10 dimensions in our multivariate regression. The diagram does not change based on the number of children drawn from each school.

Our regression coefficients have been organized horizontally down the center of the diagram to allow room along the left and right for the random effects. Taken as a multilevel model, we have only a single covariate — x. Just to be clear, we could generalize this to multiple covariates by adding more boxes with covariates for each dependent variable in the diagram.

The labels are chosen carefully. The 3rd-level effects N1, N2, and N3 are for northern counties, and the remaining second level effects S1 and S2 are for southern counties. There is a separate dependent variable and associated error for each school. We have 4 public schools (pub1 pub2, pub3, and pub4); three private schools (prv1 prv2, and prv3); and 3 church-sponsored schools (chr1 chr2, and chr3).

The multivariate structure seen in the diagram makes it clear that we can relax some constraints that the multilevel model imposes. Because the sem representation of the model breaks the 2nd level effect into an effect for each county, we can apply a structure to the 2nd level effect. Consider the path diagram below.

We have correlated the effects for the 3 northern counties. We did this by drawing curved lines between the effects. We have also correlated the effects of the two southern counties. xtmixed does not allow these types of correlations. Had we wished, we could have constrained the correlations of the 3 northern counties to be the same.

We could also have allowed the northern and southern counties to have different variances. We did just that in the diagram below by constraining the northern counties variances to be N and the southern counties variances to be S.

In this diagram we have also correlated the errors for the 4 public schools. As drawn, each correlation is free to take on its own values, but we could just as easily constrain each public school to be equally correlated with all other public schools. Likewise, to keep the diagram readable, we did not correlate the private schools with each other or the church schools with each other. We could have done that.

There is one thing that xtmixed can do that sem cannot. It can put a structure on the residual correlations within the 2nd level groups. xtmixed has a special option, residuals(), for just this purpose.

With xtmixed and sem you get,

• robust and cluster-robust SEs
• survey data

With sem you also get

• endogenous covariates
• estimation by GMM
• missing data — MAR (also called missing on observables)
• heteroskedastic effects at any level
• correlated effects at any level
• easy score tests using estat scoretests
• are the coefficients truly are the same across all equations/levels, whether effects?
• are effects or sets of effects uncorrelated?
• are effects within a grouping homoskedastic?

Whether you view this rethinking of multilevel random-effects models as multivariate structural equation models (SEMs) as interesting, or merely an academic exercise, depends on whether your model calls for any of the items in the second list.

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