dsem (version 1.6.0)

dsemRTMB: Fit dynamic structural equation model

Description

Fits a dynamic structural equation model

Usage

dsemRTMB(
  sem,
  tsdata,
  family = rep("fixed", ncol(tsdata)),
  estimate_delta0 = FALSE,
  log_prior = function(p) 0,
  control = dsem_control(),
  covs = colnames(tsdata)
)

Value

An object (list) of class `dsem`, fitted using RTMB

Arguments

sem

Specification for time-series structural equation model structure including lagged or simultaneous effects. See Details section in make_dsem_ram for more description

tsdata

time-series data, as outputted using ts, with NA for missing values.

family

Character-vector listing the distribution used for each column of tsdata, where each element must be fixed (for no measurement error), normal for normal measurement error using an identity link, gamma for a gamma measurement error using a fixed CV and log-link, bernoulli for a Bernoulli measurement error using a logit-link, or poisson for a Poisson measurement error using a log-link. family="fixed" is default behavior and assumes that a given variable is measured exactly. Other options correspond to different specifications of measurement error.

estimate_delta0

Boolean indicating whether to estimate deviations from equilibrium in initial year as fixed effects, or alternatively to assume that dynamics start at some stochastic draw away from the stationary distribution

log_prior

A user-provided function that takes as input the list of parameters out$obj$env$parList() where out is the output from dsemRTMB(), and returns the log-prior probability. For example log_prior = function(p) dnorm( p$beta_z[1], mean=0, sd=0.1, log=TRUE) specifies a normal prior probability for the first path coefficient with mean of zero and sd of 0.1. Note that the user must load RTMB using library(RTMB) prior to running the model.

control

Output from dsem_control, used to define user settings, and see documentation for that function for details.

covs

optional: a character vector of one or more elements, with each element giving a string of variable names, separated by commas. Variances and covariances among all variables in each such string are added to the model. Warning: covs="x1, x2" and covs=c("x1", "x2") are not equivalent: covs="x1, x2" specifies the variance of x1, the variance of x2, and their covariance, while covs=c("x1", "x2") specifies the variance of x1 and the variance of x2 but not their covariance. These same covariances can be added manually via argument `sem`, but using argument `covs` might save time for models with many variables.

Details

dsemRTMB is interchangeable with dsem, but uses RTMB instead of TMB for estimation. Both are provided for comparison and real-world comparison. See ?dsem for more details

Examples

# Define model
sem = "
  # Link, lag, param_name
  cprofits -> consumption, 0, a1
  cprofits -> consumption, 1, a2
  pwage -> consumption, 0, a3
  gwage -> consumption, 0, a3
  cprofits -> invest, 0, b1
  cprofits -> invest, 1, b2
  capital -> invest, 0, b3
  gnp -> pwage, 0, c2
  gnp -> pwage, 1, c3
  time -> pwage, 0, c1
"

# Load data
data(KleinI, package="AER")
TS = ts(data.frame(KleinI, "time"=time(KleinI) - 1931))
tsdata = TS[,c("time","gnp","pwage","cprofits",'consumption',
               "gwage","invest","capital")]

# Fit model
fit = dsemRTMB( sem=sem,
            tsdata = tsdata,
            estimate_delta0 = TRUE,
            control = dsem_control(quiet=TRUE) )