SIRE (version 1.1.0)

causal_decompose: Estimation and decomposition of simultaneous equation model

Description

Estimate and/or decompose a Simultaneous Equation Model into its recursive and Interdependent sub-systems

Usage

causal_decompose(data, eq.system, resid.est = "noDfCor", instruments,
  sigma.in = NULL)

Arguments

data

the data frame containing the data

eq.system

the system of equations (a list of formula objects, e.g. as in pkg systemfit)

resid.est

the estimation methods for the residual covariance matrix (as in systemfit)

instruments

the intruments used to estimate the model via 3-SLS (as in systemfit)

sigma.in

the \(\Sigma\) matrix, if the user wants to simulate a particular structure at stochastic level. Overrides 3SLS estimation if specified.

Value

A list with components

• eq.system: the system of equations given as input

• Gamma: the 3-SLS estimate of \(\Gamma'\)

• C: the matrix highlighting the interdependent mechanisms at deterministic level.

• Psi1: the matrix highlighting the interdependent mechanisms at stochastic level.

• Psi0: the matrix highlighting the causal mechanisms.

• A: the 3-SLS estimate of \(A\)

• Sigma: the 3-SLS estimate of \(Sigma\)

• systemfit: the output from the systemfit function used to estimate the model

• all.graph: the path diagram of the model, using the package igraph

• dec.graph: the path diagram of the decomposed model, with color coding for each vertex

• type.out: the type of analysis performed, either 'simulation' or 'empirical'

Examples

# NOT RUN {
data("macroIT")
eq.system = list(
               eq1 = C ~  CP  + I + CP_1,
               eq2 = I ~ K + CP_1,
               eq3 = WP ~ I + GDP + GDP_1,
               eq4 = GDP ~ C + I + GDP_1,
               eq5 = CP ~ WP + T,
               eq6 = K ~ I + K_1)

instruments = ~ T + CP_1 + GDP_1 + K_1

causal_decompose(data = macroIT,
               eq.system = eq.system,
               resid.est = "noDfCor",
               instruments = instruments,
               sigma.in = NULL)
# }