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MIIVsem is an R package for estimating structural equation models using model-implied instrumental variables.
Version 0.5.2 includes the following features:
In R you can install MIIVsem from CRAN as follows:
install.packages("MIIVsem")MIIVsem uses a subset of the model syntax employed by lavaan (Rosseel, 2012) for model specification. The following model syntax operators are currently supported:
| Operators | |
|---|---|
| =~ | Used for expressing measurement relations, read as ‘measured by.’ |
| ~ | Used For expressing regression relations, read as ‘regressed on.’ |
| ~~ | For specifying variances and covariances, read as ‘covaries with.’ |
| * | For assigning equality or numerical constraints. |
Example using Syntax Operators
In the model below, L1 =~ Z1 + Z2 + Z3 indicates the
latent variable L1 is measured by 3 indicators, Z1,
Z2, and Z3. Likewise, L2 is
measured by 3 indicators, Z4, Z5, and
Z6. The statement L1 ~ L2 specifies latent
variable L1 is regressed on latent variable
L2. Z1 ~~ Z2 indicates the error of
Z2 is allowed to covary with the error of Z3.
The label LA3 prepended to Z3 and
Z6 in the measurement model equations constrains the factor
loadings for Z3 and Z6 to equality.
model <- '
L1 =~ Z1 + Z2 + LA3*Z3
L2 =~ Z4 + Z5 + LA3*Z6
L1 ~ L2
Z2 ~~ Z3
' Scaling Indicators
Following the lavaan model syntax, latent variables are defined using
the =~ operator. For first order factors, the scaling
indicator chosen is the first observed variable on the RHS of an
equation. For the model below Z1 would be chosen as the
scaling indicator for L1 and Z4 would be
chosen as the scaling indicator for L2.
model <- '
L1 =~ Z1 + Z2 + Z3
L2 =~ Z4 + Z5 + Z6
'Equality Constraints and Parameter Restrictions
Within- and across-equation equality constraints on the factor
loading and regression coefficients can be imposed directly in the model
syntax. To specify equality constraints between different parameters
equivalent labels should be prepended to the variable name using the
* operator. For example, we could constrain the factor
loadings for two non-scaling indicators of latent factor L1
to equality using the following model syntax.
model <- '
L1 =~ Z1 + LA2*Z2 + LA2*Z3
L2 =~ Z4 + Z5 + Z6
'Researchers also can constrain the factor loading and regression
coefficients to specific numeric values in a similar fashion. Below we
constrain the regression coefficient of L1 on
L2 to 1.
model <- '
L1 =~ Z1 + Z2 + Z3
L2 =~ Z4 + Z5 + Z6
L3 =~ Z7 + Z8 + Z9
L1 ~ 1*L2 + L3
'Higher Order Factor Model
In the model below, the scaling indicator for the higher-order factor
H1 is taken to be Z1, the scaling indicator
that would have been assigned to the first lower-order factor
L1. The intercepts for lower-order latent variables are set
to zero, by default
model <- '
H1 =~ L1 + L2 + L3
L1 =~ Z1 + Z2 + Z3
L2 =~ Z4 + Z5 + Z6
L3 =~ Z7 + Z8 + Z9
'Model Defaults
In addition to those relationships specified in the model syntax
MIIVsem will automatically include the intercepts of any observed or
latent endogenous variable. The intercepts for any scaling indicators
and lower-order latent variables are set to zero. Covariances among
exogenous latent and observed variables are included by default. Where
appropriate the covariances of the errors of latent and observed
dependent variables are also included in the model specification. These
defaults correspond to those used by lavaan and
auto = TRUE, except that endogenous latent variable
intercepts are estimated by default, and the intercepts of scaling
indicators are fixed to zero.
MIIV Search
Researchers typically search for instrumental variables external to the model. The key property of valid instruments is that they are uncorrelated with equation error. The MIIV approach proposed in Bollen (1996) finds instruments among observed variables already in the model. Here, the model specification itself implies which observed variables are uncorrelated with the equation disturbance.
Using the industrialization-democracy example from Bollen (1989) we illustrate the MIIV Search:
library(MIIVsem)
model <- '
Eta1 =~ y1 + y2 + y3 + y4
Eta2 =~ y5 + y6 + y7 + y8
Xi1 =~ x1 + x2 + x3
Eta1 ~ Xi1
Eta2 ~ Xi1
Eta2 ~ Eta1
y1 ~~ y5
y2 ~~ y4
y2 ~~ y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8
'miivs(model)
#> Model Equation Information
#>
#> LHS RHS MIIVs
#> y2 y1 y3, y7, y8, x1, x2, x3
#> y3 y1 y2, y4, y6, y8, x1, x2, x3
#> y4 y1 y3, y6, y7, x1, x2, x3
#> y6 y5 y3, y4, y7, x1, x2, x3
#> y7 y5 y2, y4, y6, y8, x1, x2, x3
#> y8 y5 y2, y3, y7, x1, x2, x3
#> x2 x1 y1, y2, y3, y4, y6, y5, y7, y8, x3
#> x3 x1 y1, y2, y3, y4, y6, y5, y7, y8, x2
#> y1 x1 x2, x3
#> y5 x1, y1 y2, y3, y4, x2, x3MIIV-2SLS Estimation
We can also estimate the industrialization-democracy model using MIIV-2SLS:
miive(model, bollen1989a)
#> MIIVsem (0.5.2) results
#>
#> Number of observations 75
#> Number of equations 10
#> Estimator MIIV-2SLS
#> Standard Errors standard
#> Missing listwise
#>
#>
#> Parameter Estimates:
#>
#>
#> STRUCTURAL COEFFICIENTS:
#> Estimate Std.Err z-value P(>|z|) Sargan df P(Chi)
#> Eta1 =~
#> y1 1.000
#> y2 1.139 0.179 6.371 0.000 8.409 5 0.135
#> y3 0.969 0.140 6.924 0.000 5.874 6 0.437
#> y4 1.210 0.139 8.713 0.000 4.276 5 0.510
#> Eta2 =~
#> y5 1.000
#> y6 1.051 0.165 6.377 0.000 8.712 5 0.121
#> y7 1.180 0.151 7.814 0.000 9.538 6 0.146
#> y8 1.203 0.154 7.798 0.000 2.795 5 0.731
#> Xi1 =~
#> x1 1.000
#> x2 2.078 0.128 16.171 0.000 8.301 8 0.405
#> x3 1.751 0.149 11.782 0.000 8.738 8 0.365
#>
#> Eta1 ~
#> Xi1 1.261 0.426 2.962 0.003 0.503 1 0.478
#> Eta2 ~
#> Xi1 1.123 0.312 3.598 0.000 0.801 3 0.849
#> Eta1 0.724 0.101 7.140 0.000
#>
#> INTERCEPTS:
#> Estimate Std.Err z-value P(>|z|)
#> Eta1 -0.909 2.170 -0.419 0.675
#> Eta2 -4.499 1.424 -3.160 0.002
#> x1 0.000
#> x2 -5.711 0.654 -8.727 0.000
#> x3 -5.292 0.758 -6.985 0.000
#> y1 0.000
#> y2 -1.969 1.044 -1.886 0.059
#> y3 1.265 0.814 1.553 0.120
#> y4 -2.160 0.814 -2.654 0.008
#> y5 0.000
#> y6 -2.418 0.909 -2.659 0.008
#> y7 0.135 0.830 0.163 0.870
#> y8 -2.137 0.853 -2.505 0.012Estimation from Sample Moments
sample.cov <- cov(bollen1989a)
sample.mean <- colMeans(bollen1989a)
sample.nobs <- nrow(bollen1989a)
miive(model, sample.cov = sample.cov, sample.mean = sample.mean, sample.nobs = sample.nobs)Bootstrap Standard Errors (Version 0.5.2)
microbenchmark::microbenchmark(
fit <- miive(model, bollen1989a, se = "boot", bootstrap = 100L),
times = 100L
)
#> Unit: milliseconds
#> expr min
#> fit <- miive(model, bollen1989a, se = "boot", bootstrap = 100L) 193.4475
#> lq mean median uq max neval
#> 203.1075 209.0176 207.8951 212.088 340.4677 100Categorical Endogenous Variables (Bollen & Maydeu-Olivares (2007))
model <- '
female.access =~ access1 + access2 + access3
male.access =~ access4 + access5 + access6
'
miive(model, bollen1996, ordered = c("access1", "access2","access3", "access4", "access5", "access6"))Following Henningsen and Hamann (2007) we replicate textbook results from
Klein’s Model I (Greene, 2003, p.381)
data("KleinI", package = "systemfit")
model <- '
consump ~ corpProf + corpProfLag + wages
invest ~ corpProf + corpProfLag + capitalLag
privWage ~ gnp + gnpLag + trend
'
instruments <- '
consump ~ govExp + taxes + govWage + trend + capitalLag + corpProfLag + gnpLag
invest ~ govExp + taxes + govWage + trend + capitalLag + corpProfLag + gnpLag
privWage ~ govExp + taxes + govWage + trend + capitalLag + corpProfLag + gnpLag
'
fit <- miive(model, KleinI, instruments, miiv.check = FALSE)
estimatesTable(fit)
#> lhs op rhs est se z pvalue
#> 1 consump ~ corpProf 0.01730221 0.11804941 0.1465675 8.834734e-01
#> 2 consump ~ corpProfLag 0.21623404 0.10726796 2.0158306 4.381770e-02
#> 3 consump ~ wages 0.81018270 0.04024971 20.1289055 4.119943e-90
#> 4 invest ~ corpProf 0.15022182 0.17322929 0.8671849 3.858407e-01
#> 5 invest ~ corpProfLag 0.61594358 0.16278539 3.7837767 1.544664e-04
#> 6 invest ~ capitalLag -0.15778764 0.03612624 -4.3676741 1.255767e-05
#> 7 privWage ~ gnp 0.43885907 0.03563192 12.3164596 7.386658e-35
#> 8 privWage ~ gnpLag 0.14667382 0.03883613 3.7767360 1.588970e-04
#> 9 privWage ~ trend 0.13039569 0.02914098 4.4746500 7.653659e-06
#> 10 consump ~1 16.55475577 1.32079242 12.5339573 4.867207e-36
#> 11 invest ~1 20.27820894 7.54270590 2.6884528 7.178398e-03
#> 12 privWage ~1 1.50029689 1.14778020 1.3071291 1.911689e-01These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.