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Direct, indirect, and total effects from an SLX model

Source: R/effects.R
slx_effects.Rd

For an SLX model the effects decomposition is trivial — no matrix inversion, no simulation. For each variable \(x\) that enters both directly and as a spatial lag, the direct effect is its OLS coefficient \(\hat\beta\) and the indirect effect at order \(k\) is \(\hat\theta_k\). Standard errors come straight from vcov().

Usage

slx_effects(object, by_order = FALSE, conf.level = 0.95)

Arguments

object

An slx object returned by slx().

by_order

Logical; if TRUE, report indirect effects separately by order of W. Default FALSE sums across orders for a single indirect effect per variable–W combination.

conf.level

Confidence level for reported intervals. Default 0.95.

Value

A tibble with columns variable, w_name, order (when by_order = TRUE), type (direct/indirect/total), estimate, std.error, conf.low, conf.high, p.value.

Examples

data(defense_burden)
W <- slx_weights(style = "custom", matrix = defense_burden$W_contig,
                 row_standardize = FALSE)
fit <- slx(ch_milex ~ milex_tm1 + civilwar_tm1,
           data = defense_burden$data, W = W, lag = "civilwar_tm1")
slx_effects(fit)
#> # A tibble: 3 × 8
#>   variable     w_name type     estimate std.error conf.low conf.high p.value
#>   <chr>        <chr>  <chr>       <dbl>     <dbl>    <dbl>     <dbl>   <dbl>
#> 1 civilwar_tm1 NA     direct     -1.18      0.571    -2.30   -0.0495  0.0409
#> 2 civilwar_tm1 W      indirect    0.171     0.836    -1.48    1.82    0.839 
#> 3 civilwar_tm1 W      total      -1.01      1.01     -3.00    0.988   0.321