# dsem model description

#### James T. Thorson

# Dynamic structural equation models

Package dsem (Thorson et al. 2024) involves specifying a dynamic structural equation model (DSEM). This DSEM be viewed either:

1. Weak interpretation: as an expressive interface to parameterize the correlation among variables, using as many or few parameters as might be appropriate; or
2. Strong interpretation: as a structural causal model, allowing predictions about the consequence of counterfactual changes to the system.

We introduce DSEM first from the perspective of a software user (i.e., the interface) and then from the perspective of a statistician (i.e., the equations and their interpretation).

### Viewpoint 1: Software interface

To specify a DSEM, the user uses arrow-and-lag notation, based on arrow notation derived from package `sem` (Fox 2006). For example, to specify a first-order autoregressive process with variable \(x\) this involves:

    x -> x, 1, ar1
    x <-> x, 0, sd

This then estimates a single parameter representing first-order autoregression (represented with a one-headed arrow), as well as the Cholesky decomposition of the exogenous covariance of of model variables (specified with two-headed arrows). See `?make_dsem_ram` Details section for more details about syntax.

If there were four time-intervals (\(T=4\)) this would then result in the path matrix:

\[ \mathbf P_{\mathrm{joint}} = \begin{pmatrix} 0 & 0 & 0 & 0 \\ \rho & 0 & 0 & 0 \\ 0 & \rho & 0 & 0 \\ 0 & 0 & \rho & 0 \end{pmatrix} \] This joint path matrix then represents the partial effect of each variable and time (column) on each other variable and time (row).

DSEM interactions can be as complicated or simple as desired, and can include:

1. Latent variables and loops (i.e., they are not restricted to directed acyclic graphs);
2. Values that are fixed a priori, where the `parameter_name` is provided as `NA` and the starting value that follows is the fixed value;
3. Values that are mirrored among path coefficients, where the same `parameter_name` is provided for multiple rows of the text file.

The user also specifies a distribution for measurement errors for each variable using arguement `family`, and whether each time-series starts from its stationary distribution or from some non-equilibrium initial condition using argument `estimate_delta0`. If the latter is specified, then variables will tend to converge back on the stationary distribution at a rate that is determined by estimated parameters.

### Viewpoint 2: Mathematical details

The DSEM defines a generalized linear mixed model (GLMM) for a \(T \times J\) matrix \(\mathbf{Y}\), where \(y_{tj}\) is the measurement in time \(t\) for variable \(j\). This measurement matrix can include missing values \(y_{tj} = \mathrm{NA}\), and it will estimate a \(T \times J\) matrix of latent states \(\mathbf{X}\) for all modeled times and variables, where \(\mathbf{x}_t\) is the vector of states in time \(t\) and \(\text{vec}(\mathbf{X})\) is a \(TJ\) length vector constructed by stacking columns. DSEM also estimates a \(T \times J\) matrix of process errors \(\mathbf{E}\) where \(\mathbf{\epsilon}_t\) is the vector of process errors in time \(t\), and \(\text{vec}(\mathbf{E})\) is the \(TJ\) vector of stacked columns.

DSEM can be written in structural vector-autoregressive (SVAR) notation as a SVAR model of arbitrary lag, from lag-0 upwards:

\[ \mathbf{x}_t = \underbrace{\mathbf{P}_0 \mathbf{x}_t}_{\text{Simultaneous effects}} + \underbrace{\mathbf{P}_1 \mathbf{x}_{t-1}}_{\text{Lag-1 effects}} + \underbrace{\mathbf{P}_2 \mathbf{x}_{t-2}}_{\text{Lag-2 effects}} + \underbrace{...}_{\text{Higher-order lags}} + \mathbf{\epsilon}_t \] where \(\mathbf{P}_0\) is the \(J \times J\) matrix of simultaneous interactions (the inclusion of which distinguishes an SVAR from a VAR), \(\mathbf{P}_1\) and \(\mathbf{P}_2\) are interactions occurring at lag-1 and lag-2, respectively, and it can also include higher-order lags (not shown here). Additionally, \(\mathbf{\epsilon}_t\) is exogenous covariation in time \(t\):

\[ \mathbf{\epsilon}_t \sim \text{MVN}(\mathbf{0,GG}^T) \] and \(\mathbf{G}\) is the square-root of exogenous covariance \(\mathbf{GG}^T\) that occurs in each time. \(\mathbf{G}\) is then estimated by DSEM, and is identifiable without constraints when specified as a lower-triangle matrix (representing the Cholesky decomposition of the covariance of exogenous process errors occurring in each time).

This VAR notation can also be rewritten as a joint simultaneous equation model (SEM):

\[ \text{vec}(\mathbf{X}) = \mathbf{P}_{\text{joint}} \text{vec}(\mathbf{X}) + \text{vec}(\mathbf{E}) \]

where: \[ \text{vec}(\mathbf{E}) \sim \text{MVN}(\mathbf{0},\mathbf{G}_{\text{joint}} \mathbf{G}_{\text{joint}}^T) \] We sum across the Kronecker product of interaction matrices and lag matrices to obtain the joint path matrix:

\[ \mathbf P_{\mathrm{joint}} = \sum_{g=0}^{T}(\mathbf L_g \otimes \mathbf P_g) \] where \(\otimes\) is the Kronecker product, \(g\) is the lag index, \(T\) is the highest-order lag that can be included, and \(\mathbf{L}_g\) is a matrix representing lag-g (see example below). Similarly, the exogenous covariance is constructed from a Kronecker product, although we assume that all covariance is simultaneous (i.e., no lags are allowed for two-headed arrows):

\[ \mathbf G_{\mathrm{joint}} = \mathbf L_0 \otimes \mathbf G \] where \(\mathbf{L}_0\) is a \(T \times T\) lag-0 (identity) matrix.

For illustration, when \(T=4\) and given a maximum lag of 2, we can write \(\mathbf{P}_{\text{joint}}\) as:

\[ \mathbf{P}_{\text{joint}} = \begin{pmatrix} \mathbf{P}_0 & 0 & 0 & 0\\ \mathbf{P}_1 & \mathbf{P}_0 & 0 & 0\\ \mathbf{P}_2 & \mathbf{P}_1 & \mathbf{P}_0 & 0\\ 0 & \mathbf{P}_2 & \mathbf{P}_1 & \mathbf{P}_0 \\ \end{pmatrix} \] and \(\mathbf{G}_{\text{joint}}\) as:

\[ \mathbf{G}_{\text{joint}} = \begin{pmatrix} \mathbf{G} & 0 & 0 & 0\\ 0 & \mathbf{G} & 0 & 0\\ 0 & 0 & \mathbf{G} & 0\\ 0 & 0 & 0 & \mathbf{G} \\ \end{pmatrix} \] The DSEM then results in Gaussian Markov random field for latent states:

\[ \mathrm{vec}(\mathbf X) \sim \mathrm{GMRF}(\mathbf{0, Q}_{\mathrm{joint}}) \] where \(\mathbf Q_{\mathrm{joint}}\) is a \(TJ \times TJ\) precision matrix such that \(\mathbf{Q_{\mathrm{joint}}}^{-1}\) is the estimated covariance among latent states. This joint precision is itself constructed from the joint path matrix \(\mathbf P_{\mathrm{joint}}\) and the joint exogenous covariance matrix \(\mathbf G_{\mathrm{joint}}\):

\[ \mathbf Q_{\mathrm{joint}} = ({\mathbf{I - P}_{\mathrm{joint}}}^T) (\mathbf G_{\mathrm{joint}} \mathbf G_{\mathrm{joint}}^T)^{-1} (\mathbf{I - P_{\mathrm{joint}}}) \]

Finally, it is convenient to write the joint path matrix by summing across path coefficients \(k\) rather than lags \(g\):

\[ \mathbf P_{\mathrm{joint}} = \sum_{k=1}^{K}(\mathbf L_{g[k]} \otimes \mathbf P_k) \] where \(g[k]\) is the lag associated with path coefficient \(k\), and \(\mathbf{P}_k\) is a \(J \times J\) matrix with only a single non-zero value (with non-zero value equal to path coefficient \(k\)).

Say we specify a model with \(K=2\) two interactions (one-headed arrows). For each one-headed arrow, we define a \(J \times J\) path matrix \(\mathbf P_k\) and a lag matrix \(\mathbf L_{g[k]}\). For example, in a model with \(J=3\) variables \(\mathbf{X = (A,B,C)}\) and \(T=4\) times, and specifying \(K=2\) one-headed arrows:

    A -> B, 0, b_AB
    B -> C, 1, b_BC

this then results two path matrices:

\[ \mathbf P_{k=1} = \begin{pmatrix} 0 & 0 & 0 \\ b_{AB} & 0 & 0 \\ 0 & 0 & 0 \\ \end{pmatrix} \] and \[ \mathbf P_{k=2} = \begin{pmatrix} 0 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & b_{BC} & 0 \\ \end{pmatrix} \] with corresponding lag matrices

\[ \mathbf L_{g[1]} = \begin{pmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{pmatrix} \] and \[ \mathbf L_{g[2]} = \begin{pmatrix} 0 & 0 & 0 & 0 \\ 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \end{pmatrix} \]

## Measurement errors

DSEM includes multiple distribution for measurement errors. For example, if the user specifies `family[[j] = fixed()` then:

\[ y_{tj} = x_{tj} + d_{tj} \] for all years. Alternatively, if the user specifies `family[[j]] = gaussian()` then: \[ y_{tj} \sim \mathrm{Normal}( x_{tj} + d_{tj}, {\sigma_j}^2) \] and \({\sigma_j}^2\) is then included as an estimated parameter. When estimating missing values or masurement errors in \(\mathbf{Y}\), dsem must then marginalize across the latent value of states \(\mathbf{X}\). It does this using the Laplace approximation (Skaug and Fournier 2006), as implemented using the R-package TMB (Kristensen et al. 2016). Computations involving sparse matrices are efficient using the Matrix package (Bates et al. 2023) to interface with the Eigen library (Guennebaud et al. 2010).

These expressions include the \(T \times J\) matrix \(\mathbf D\) representing the ongoing impact of initial conditions \(d_{tj}\) for each variable and year, as explained in detail next.

## Initial conditions and total effects

Imagine we have some exogenous intervention that caused a \(T \times J\) matrix of changes \(\mathbf C\). The total effect of this exogenous intervention would then be \((\mathbf{I - P}_{\mathrm{joint}})^{-1} \mathrm{vec}(\mathbf C)\), and we can calculate any total effect using this matrix inverse \((\mathbf{I - P}_{\mathrm{joint}})^{-1}\) (called the “Leontief matrix”). To see this, consider that the first-order effect of change \(\mathbf C\) is \(\mathbf P_{\mathrm{joint}} \mathrm{vec}(\mathbf C)\), but this response then in turn causes a second-order effect \(\mathbf{P_{\mathrm{joint}}}^2 \mathrm{vec}(\mathbf C)\), and so on. The total effect is therefore:

\[ \sum_{l=0}^{\infty} \mathbf{P_{\mathrm{joint}}}^l = (\mathbf{I - P}_{\mathrm{joint}})^{-1} \] where this power-series of direct and indirect effects then results in the Leontief matrix (as long as the \(\mathbf{I - P}\) is invertible).

We can use this expression to calculate the matrix \(\mathbf D\) represents the ongoing effect of initial conditions. It is constructed from a \(J\) length vector of estimated initial conditions \(\mathbf\delta_1\) in time \(t=1\), and we construct a \(T \times J\) matrix \(\mathbf\Delta\) where the first row (corresponding to year \(t=1\)) is \(\mathbf\delta_1\) and all other elements are \(0\). The ongoing effect of initial conditions can then be calculated as:

\[ \mathrm{vec}(\mathbf D) = (\mathbf{I - P}_{\mathrm{joint}})^{-1} \mathrm{vec}(\mathbf\Delta) \] Calculating the effect of initial conditions is in a sense the total effect of \(\mathbf\delta_1\) in year \(t=1\) on subsequent years. Calculating the effect \(\mathbf D\) of initial conditions involves inverting \(\mathbf{I - P}_{\mathrm{joint}}\), but this is computationally efficient using a sparse LU decomposition.

## Constant conditional vs. marginal variance

We have defined the joint precision for a GMRF based on a path matrix and matrix of exogenous covariances. The exogenous (or conditional) variances are stationary for each variable over time, and some path matrices will result in a nonstationary marginal variance. To see this, consider a first-order autoregressive process

    dsem = "
    x -> x, 1, ar1, 0.8
    x <-> x, 0, sd, 1
    "

We can parse this DSEM and construct the precision using internal functions:

    # Load package
    library(dsem)

    # call dsem without estimating parameters
    out = dsem(
      tsdata = ts(data.frame( x = rep(1,10) )),
      sem = dsem,
      control = dsem_control(
        run_model = FALSE,
        quiet = TRUE
      )
    )

    # Extract covariance
    Sigma1 = solve(as.matrix(out$obj$report()$Q_oo))
    plot( x=1:10, y = diag(Sigma1), xlab="time",
          ylab="Marginal variance", type="l",
          ylim = c(0,max(diag(Sigma1))))

where we can see that the diagonal of this covariance matrix is non-constant.

We therefore derive an alternative specification that preserves a stationary marginal variance by rescaling the exogenous (conditional) variance.

Specifically, we see that the marginal variance is:

\[ \mathrm{Var}(\mathbf X) = \mathrm{diag}(\mathbf\Sigma) = \mathbf{ L L}^T \\ \mathbf L = (\mathbf{I - P}_{\mathrm{joint}})^{-1} \mathbf G \] Given the properties of the Hadamard (elementwise) product, this can be rewritten as:

\[ \mathrm{diag}(\mathbf\Sigma) = \mathbf{(L \circ L) 1} \] Now suppose we have a desired vector of length \(TJ\) for the constant marginal variance \(\mathbf v\). We can solve for the exogenous covariance that would result in that marginal variance:

\[ \mathbf u = \mathbf{(L \circ L)}^{-1} \mathbf v \]

and we can then rescale the exogenous covariance:

\[ \mathrm{diag}(\mathbf G^*) = \mathbf u \] and then using this rescaled exogenous covariance when constructing the precision of the GMRF. We can see this again using our first-order autoregressive example

    # call dsem without estimating parameters
    out = dsem(
      tsdata = ts(data.frame( x = rep(1,10) )),
      sem = dsem,
      control = dsem_control(
        run_model = FALSE,
        quiet = TRUE,
        constant_variance = "marginal"
      )
    )

    # Extract covariance
    Sigma2 = solve(as.matrix(out$obj$report()$Q_oo))
    plot( x=1:10, y = diag(Sigma2), xlab="time",
          ylab="Marginal variance", type="l",
          ylim = c(0,max(diag(Sigma1))))

This shows that the corrected (nonstationary) exogenous variance results in a stationary marginal variance for the AR1 process. This correction can be done in two different ways that are identical when the exogenous covariance is diagonal (as it is in this simple example), but differ when specifying some exogenous covariance. However, we do not discuss this in detail here. Note that this calculating this correction for a constant marginal variance requires the inverse of the squared values of Leontief matrix (which is itself a matrix inverse). It therefore is computationally expensive for large models containing complicated dependencies.

## Reduced rank models

We note that some DSEM specifications will be reduced rank. This arises for example when specifying a dynamic factor analysis, where \(J\) variables are explained by \(F < J\) factors that each follow a random walk:

    #
    dsem = "
      # Factor follows random walk with unit variance
      F <-> F, 0, NA, 1
      F -> F, 1, NA, 1
      # Loadings on two manifest variables
      F -> x, 0, b_x, 1
      F -> y, 0, b_y, 1
      # No residual variance for manifest variables
      x <-> x, 0, NA, 0
      y <-> y, 0, NA, 0
    "
    data = data.frame(
      x = rnorm(10),
      y = rnorm(10),
      F = rep(NA,10)
    )

    # call dsem without estimating parameters
    out = dsem(
      tsdata = ts(data),
      sem = dsem,
      family = list(x = gaussian(), y = gaussian(), F = fixed()),
      control = dsem_control(
        run_model = FALSE,
        quiet = TRUE
      )
    )

We can extract the covariance and inspect the eigenvalues:

    # Extract covariance
    library(Matrix)
    IminusRho_kk = out$obj$report()$IminusRho_kk
    G_kk = out$obj$report()$Gamma_kk
    Q_kk = t(IminusRho_kk) %*% t(G_kk) %*% G_kk %*% IminusRho_kk

    # Display eigenvalues
    eigen(Q_kk)$values
    #>  [1] 3.91114561 3.65247755 3.24697960 2.73068205 2.14946019 1.55495813
    #>  [7] 1.00000000 0.53389626 0.19806226 0.02233835 0.00000000 0.00000000
    #> [13] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
    #> [19] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
    #> [25] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000

where this shows that the precision has a rank of 10 while being a \(30 \times 30\) matrix. We therefore cannot evaluate the probability density of state matrix \(\mathbf X\) using this precision matrix (i.e., the log-determinant is not defined).

To address this circumstance, we can switch to using `gmrf_parameterization = "project"`. This evaluates the probability density of a set of innovations \(\mathbf X^*\) that follow a unit variance:

\[ \mathrm{vec}(\mathbf X)^* \sim \mathrm{GMRF}(\mathbf{0, I}) \] and then projects these full-rank innovations to the reduced rank states:

\[ \mathrm{vec}(\mathbf X) = (\mathbf{I - P}_{\mathrm{joint}})^{-1} \mathbf G_{\mathrm{joint}} \mathrm{vec}(\mathbf X)^* \]

This parameterization allows us to fit DSEM using a rank-deficient structural model.

# Works cited

Bates, Douglas, Martin Maechler, and Mikael Jagan. 2023. Matrix: Sparse and Dense Matrix Classes and Methods. https://CRAN.R-project.org/package=Matrix.
Fox, John. 2006. “Structural Equation Modeling with the Sem Package in R.” Structural Equation Modeling-a Multidisciplinary Journal 13: 465–86.
Guennebaud, G., B. Jacob, et al. 2010. Eigen V3. https://libeigen.gitlab.io/.
Kristensen, Kasper, Anders Nielsen, Casper W. Berg, Hans Skaug, and Bradley M. Bell. 2016. “TMB: Automatic Differentiation and Laplace Approximation.” Journal of Statistical Software 70 (5): 1–21. https://doi.org/10.18637/jss.v070.i05.
Skaug, Hans, and Dave Fournier. 2006. “Automatic Approximation of the Marginal Likelihood in Non-Gaussian Hierarchical Models.” Computational Statistics & Data Analysis 51 (2): 699–709.
Thorson, James T., Alexander G. Andrews III, Timothy E. Essington, and Scott I. Large. 2024. “Dynamic Structural Equation Models Synthesize Ecosystem Dynamics Constrained by Ecological Mechanisms.” Methods in Ecology and Evolution 15 (4): 744–55. https://doi.org/10.1111/2041-210X.14289.