# Dynamic Factor Model

#### Greg Veramendi

## Overview

An example of how to estimate the dynamics of a latent factor. Assume that indicators are observed at two time points and you want to estimate how the period-2 value depends on the period-1 value. You need two things:

1. A measurement system that is invariant across time (same loadings, thresholds / residual SDs across the two periods) so the factor scale is comparable, and
2. A structural equation linking the period-2 factor to the period-1 factor: `f₂ = α + β·f₁ + ε`.

factorana supports this with a two-stage workflow, and two helper functions make the setup a few lines:

- `define_dynamic_measurement()` builds the Stage 1 measurement model: a 2-factor independent system (or k-factor, for k periods) with loadings and residual sigmas tied across periods via equality constraints, but measurement intercepts left period-specific.
- `build_dynamic_previous_stage()` converts the Stage 1 estimation result into a `previous_stage` object for Stage 2, carrying the anchor period’s intercepts into every factor slot. This anchors the measurement level under `E[f₁] = 0` and lets the observed mean shift between periods identify the structural intercept α.

This vignette walks through a simulated example where `α`, `β`, and `σ²_ε` are known, shows the recovery, and explains one subtlety of the workflow (why the anchor-period intercepts are the right ones to carry into Stage 2).

## Simulate data

One latent construct, three linear indicators per period, two periods. The DGP:

\[ f_1 \sim N(0,\sigma^2_{f_1}), \qquad f_2 = \alpha + \beta\,f_1 + \varepsilon, \quad \varepsilon \sim N(0,\sigma^2_\varepsilon) \]

\[ Y_{t,m} = \tau_m + \lambda_m\,f_t + u_{t,m}, \quad u_{t,m} \sim N(0,\sigma_m^2) \]

with measurement parameters `τ_m`, `λ_m`, `σ_m` shared across the two periods.

    library(factorana)

    set.seed(41)
    n <- 1500

    # Structural parameters (what we want to recover)
    true_var_f1   <- 1.0
    true_alpha    <- 0.4
    true_beta     <- 0.6
    true_sigma_e2 <- 0.5

    # Shared measurement parameters
    item_int   <- c(1.5, 1.0, 0.8)
    item_load  <- c(1.0, 0.9, 1.1)   # first loading fixed to 1 in the model
    item_sigma <- c(0.7, 0.75, 0.65)

    f1  <- rnorm(n, 0, sqrt(true_var_f1))
    eps <- rnorm(n, 0, sqrt(true_sigma_e2))
    f2  <- true_alpha + true_beta * f1 + eps

    gen_Y <- function(f, i) {
      item_int[i] + item_load[i] * f + rnorm(length(f), 0, item_sigma[i])
    }

    dat <- data.frame(
      intercept = 1,
      eval      = 1L,
      Y_t1_m1 = gen_Y(f1, 1), Y_t1_m2 = gen_Y(f1, 2), Y_t1_m3 = gen_Y(f1, 3),
      Y_t2_m1 = gen_Y(f2, 1), Y_t2_m2 = gen_Y(f2, 2), Y_t2_m3 = gen_Y(f2, 3)
    )

The wide-format data has columns named `<prefix><item>`: `Y_t1_m1`, `Y_t1_m2`, `Y_t1_m3` for wave 1, and `Y_t2_m1`, … for wave 2.

## Stage 1: `define_dynamic_measurement()`

One function call builds the measurement model: a 2-factor independent system (one factor per period) with loadings and sigmas tied across periods, intercepts left free per period.

    dyn <- define_dynamic_measurement(
      data                 = dat,
      items                = c("m1", "m2", "m3"),
      period_prefixes      = c("Y_t1_", "Y_t2_"),
      model_type           = "linear",
      evaluation_indicator = "eval"
    )
    # The wrapper generates 5 equality constraints: 2 for loadings (items
    # m2, m3; item m1's loading is fixed to 1 on its factor slot) and 3
    # for sigmas.
    length(dyn$equality_constraints)
    #> [1] 5

Estimate Stage 1 with `estimate_model_rcpp()` exactly as for any model system:

    ctrl <- define_estimation_control(n_quad_points = 8, num_cores = 1)
    result_stage1 <- estimate_model_rcpp(
      model_system = dyn$model_system,
      data         = dat,
      control      = ctrl,
      optimizer    = "nlminb",
      parallel     = FALSE,
      verbose      = FALSE
    )
    result_stage1$convergence
    #> [1] 0

Inspect the intercepts: the wave-1 intercepts should match the DGP `τ_m` (because `E[f₁] = 0` by convention). The wave-2 intercepts are shifted by `λ_m · E[f₂]`, which is the mean-drift artefact we are about to discard.

    est <- result_stage1$estimates
    tab <- data.frame(
      m        = 1:3,
      DGP_tau  = item_int,
      wave_1   = round(c(est["Y_t1_m1_intercept"],
                         est["Y_t1_m2_intercept"],
                         est["Y_t1_m3_intercept"]), 3),
      wave_2   = round(c(est["Y_t2_m1_intercept"],
                         est["Y_t2_m2_intercept"],
                         est["Y_t2_m3_intercept"]), 3)
    )
    knitr::kable(tab, row.names = FALSE)

m | DGP_tau | wave_1 | wave_2
--- | --- | --- | ---
1 | 1.5 | 1.488 | 1.905
2 | 1.0 | 1.000 | 1.332
3 | 0.8 | 0.778 | 1.234

## Why we do not pool the intercepts

A natural alternative is to stack period-1 and period-2 rows into one long-format dataset and fit a single-factor CFA. The resulting intercepts would be the pooled means of `Y` across both periods, biased by `λ_m · E[f₂]/2` (because the pooled mean averages the period-1 and period-2 factor means). Plugging those into Stage 2 under-estimates `α` by roughly `E[f₂]/2`.

The wrapper avoids that: Stage 1 estimates period-specific intercepts, then `build_dynamic_previous_stage()` carries only the anchor period’s intercepts into Stage 2.

## Stage 2: `build_dynamic_previous_stage()` + `SE_linear`

    dummy <- build_dynamic_previous_stage(
      dyn           = dyn,
      stage1_result = result_stage1,
      data          = dat,
      anchor_period = 1L
    )

    fm_stage2 <- define_factor_model(
      n_factors        = 2,
      n_types          = 1,
      factor_structure = "SE_linear"
    )
    ms_stage2 <- define_model_system(
      components     = list(),       # measurement components prepended from previous_stage
      factor         = fm_stage2,
      previous_stage = dummy
    )
    #> Two-stage SE estimation: Stage 1 (independent) -> Stage 2 (SE_linear)
    #>   Measurement parameters (loadings, thresholds) will be fixed from Stage 1
    #>   Factor distribution parameters will use Stage 2 structure
    #>   Fixing 16 measurement parameters, ignoring 2 factor distribution parameters

    init_s2 <- initialize_parameters(ms_stage2, dat, verbose = FALSE)
    init_s2$init_params["factor_var_1"]    <- unname(dummy$estimates["factor_var_1"])
    init_s2$init_params["se_intercept"]    <- 0.0
    init_s2$init_params["se_linear_1"]     <- 0.5
    init_s2$init_params["se_residual_var"] <- 0.5

    result_stage2 <- estimate_model_rcpp(
      model_system = ms_stage2,
      data         = dat,
      init_params  = init_s2$init_params,
      control      = ctrl,
      optimizer    = "nlminb",
      parallel     = FALSE,
      verbose      = FALSE
    )
    result_stage2$convergence
    #> [1] 0

## Recovery

    est <- result_stage2$estimates
    se  <- result_stage2$std_errors
    ps  <- c("factor_var_1", "se_intercept", "se_linear_1", "se_residual_var")

    tab <- data.frame(
      param = ps,
      true  = c(true_var_f1, true_alpha, true_beta, true_sigma_e2),
      est   = round(unname(est[ps]), 4),
      se    = round(unname(se[ps]),  4)
    )
    tab$z <- round((tab$est - tab$true) / tab$se, 2)
    knitr::kable(tab, row.names = FALSE)

param | true | est | se | z
--- | --- | --- | --- | ---
factor_var_1 | 1.0 | 0.9104 | 0.0325 | -2.76
se_intercept | 0.4 | 0.4036 | 0.0217 | 0.17
se_linear_1 | 0.6 | 0.5880 | 0.0236 | -0.51
se_residual_var | 0.5 | 0.5051 | 0.0261 | 0.20

`se_intercept` (the structural α) recovers essentially exactly, which is the whole point of the workflow. The slope β, residual variance σ²_ε, and input factor variance σ²_f₁ also recover within their standard errors.

## Cluster-robust standard errors

The standard errors above are model-based (inverse observed information). When individuals are grouped (for example pupils within schools, or siblings within households) and that grouping induces dependence the model does not capture, `vcov_factorana()` provides cluster-robust standard errors. Pass `cluster` as the name of a grouping column in the data (or a vector of length `nrow(dat)`).

Here we attach a synthetic school id (50 schools) and cluster on it:

    set.seed(7)
    dat$school <- sample(1:50, n, replace = TRUE)

    se_cluster <- robust_se(result_stage2, dat, type = "cluster", cluster = "school")

    ps <- c("factor_var_1", "se_intercept", "se_linear_1", "se_residual_var")
    data.frame(
      param      = ps,
      se_model   = round(unname(result_stage2$std_errors[ps]), 4),
      se_cluster = round(unname(se_cluster[ps]),                4),
      row.names  = NULL
    )
    #>             param se_model se_cluster
    #> 1    factor_var_1   0.0325     0.0328
    #> 2    se_intercept   0.0217     0.0212
    #> 3     se_linear_1   0.0236     0.0256
    #> 4 se_residual_var   0.0261     0.0302

Because the schools were assigned at random here, the cluster-robust and model-based standard errors are close; with genuine within-cluster dependence the cluster-robust values would typically be larger.

Two-stage caveat. These standard errors are computed on the Stage 2 fit and treat the Stage 1 measurement parameters (loadings, sigmas, intercepts) as known. They therefore understate the total uncertainty (the generated-regressor problem). For honest two-stage inference, resample clusters and re-run both stages in a bootstrap; a single-stage robust or cluster-robust covariance is exact only when the measurement system is not itself estimated.

## Bootstrap for honest two-stage standard errors

A cluster bootstrap that re-runs both stages on each resample accounts for the first-stage uncertainty. `bootstrap_factorana_multistage()` drives this in one call: it resamples clusters, and for each replicate fits Stage 1 and then Stage 2 chained on that replicate’s own Stage 1 fit. Each `(stage, sample)` fit is saved to disk and skipped if it already exists, so the run is restartable and the per-sample work can be scattered across a compute cluster with `bootstrap_fit_sample()`.

    stage_builders <- list(
      # Stage 1: the measurement system (prev_fits is empty for the first stage)
      function(prev_fits, data) dyn$model_system,
      # Stage 2: SE_linear, chaining on THIS replicate's Stage 1 fit
      function(prev_fits, data) {
        prev <- build_dynamic_previous_stage(
          dyn = dyn, stage1_result = prev_fits[[1]], data = data, anchor_period = 1L)
        fm2 <- define_factor_model(n_factors = 2, factor_structure = "SE_linear")
        define_model_system(components = list(), factor = fm2, previous_stage = prev)
      }
    )

    boot <- bootstrap_factorana_multistage(
      stage_builders, dat, R = 500, cluster = "person",
      dir = "boot_run", control = ctrl,
      optimizer = "nlminb", parallel = FALSE, verbose = FALSE
    )

    boot$final$boot_se   # Stage 2 bootstrap standard errors (include Stage 1 uncertainty)
    boot$final$ci        # percentile intervals

For a multi-node run, generate the samples once with `generate_bootstrap_samples()` and call `bootstrap_fit_sample()` from an array job (one task per sample, per stage), then summarise with `collect_bootstrap()`.

## Notes on generalisation

- More than two periods. `period_prefixes` can have length 3, 4, or more. The wrapper builds a k-factor independent Stage 1 with all `k · (n_items - 1)` loadings and `k · n_items` sigmas tied across periods. For Stage 2 SE modelling, pick any two factors (e.g., periods t-1 and t) and build a 2-factor SE model with `previous_stage`.
- Ordered probit measures. Pass `model_type = "oprobit"` and `n_categories = K`. The wrapper ties the `K-1` cutpoints of each item across periods (in place of sigma).
- Additional covariates. Pass `covariates = c("intercept", "x1", ...)`. Covariate coefficients on items are estimated per-period in Stage 1 (not tied by this wrapper); the anchor-period values are carried into Stage 2.

## Where to go next

- `?define_factor_model`: `factor_structure = "SE_quadratic"` adds a quadratic term `γ·f_1²` to the structural equation (trap/threshold dynamics).
- `?define_model_system`: the underlying `equality_constraints` argument, used by the wrapper.
- `vignette("measurement_system", package = "factorana")`: basics of measurement-system estimation.
- `vignette("roy_model", package = "factorana")`: a different structural setting (discrete sector choice plus continuous outcomes sharing a latent ability).