Implementation of GMM estimation for Prono's (2014) heteroskedasticity-based identification strategy using GARCH models.
Usage
prono_gmm(
data,
system = "triangular",
y1_var = "Y1",
y2_var = "Y2",
x_vars = NULL,
garch_order = c(1, 1),
fit_garch = TRUE,
add_intercept = TRUE,
gmm_type = "twoStep",
vcov = .hetid_const("VCOV_HAC"),
initial_values = NULL,
compute_se = TRUE,
verbose = TRUE,
...
)Arguments
- data
Data frame containing all variables.
- system
Character. Either "triangular" (default).
- y1_var
Character. Name of the first dependent variable (default: "Y1").
- y2_var
Character. Name of the second dependent variable (default: "Y2").
- x_vars
Character vector. Names of exogenous variables.
- garch_order
GARCH(p,q) order (default: c(1,1)).
- fit_garch
Logical. Whether to fit GARCH model (default: TRUE).
- add_intercept
Logical. Whether to add an intercept (default: TRUE).
- gmm_type
Character. GMM type: "onestep", "twoStep" (default), "iterative", or "cue".
- vcov
Character. Variance-covariance matrix type: "iid", "HAC" (default), or "cluster".
- initial_values
Numeric vector. Initial parameter values (optional).
- compute_se
Logical. Whether to compute standard errors (default: TRUE).
- verbose
Logical. Whether to print progress messages (default: TRUE).
- ...
Additional arguments passed to gmm::gmm.
References
Prono, T. (2014). The Role of Conditional Heteroskedasticity in Identifying and Estimating Linear Triangular Systems, with Applications to Asset Pricing Models That Include a Mismeasured Factor. Journal of Applied Econometrics, 29(5), 800-824. doi:10.1002/jae.2387
See also
prono_triangular_moments for moment conditions.
run_single_prono_simulation for 2SLS estimation.