feedback_ml {SIRE}

Testing for Feedback Effects in a Simultaneous Equation Model

Description

Testing for Feedback Effects in a Simultaneous Equation Model

Usage

feedback_ml(data, out.decompose, eq.id, lb = -200, ub = 200,
  nrestarts = 10, nsim = 20000, seed.in = 1)

Arguments

data

the data frame containing the data

out.decompose

the decomposition object resulting from causal_decompose()

eq.id

the equation to be tested for feedback effects

lb

lower bound of the parameter space required for gosolnp

ub

upper bound of the parameter space required for gosolnp

nrestarts

number of solver restarts (as in gosolnp)

nsim

number of random parameters to generate for every restart of the solver (as in gosolnp)

seed.in

seed number for gosolnp routine

Value

A list with components

• rho.est: a data frame with the maximum likelihood estimate of rho and the equations with which each element is involved in feedback-like mechanisms

• loglik: the value of the log-likelihood of the model

• theta.hessian: the hessian matrix for the estimated parameters

• rho.jacobian: the Jacobian matrix of \rho with respect to the entire set of parameters

• wald: the resulting Wald test statistic

Examples

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

c.dec = causal_decompose(data = macroIT,
                         eq.system = eq.system,
                         resid.est = "noDfCor",
                         instruments = instruments)

feedback_ml(data = macroIT,
              out.decompose = c.dec,
              eq.id = 5,
              lb = -200,
              ub = 200,
              nrestarts = 10,
              nsim = 20000,
              seed.in = 1)