reg_als {callback} | R Documentation |
Asymptotic least squares estimation
Description
Asymptotic least squares estimation
Usage
reg_als(x, y, omega, ols = FALSE)
Arguments
x |
the matrix of the right-hand variables (incl. the constant term when needed). |
y |
the vector of the left-hand variable. |
omega |
the covariance matrix of the disturbances. |
ols |
logical indicating whether to perform OLS (TRUE) or FGLS (FALSE). The default is FALSE. |
Value
a list with class reg_als
containing "config"
for the definition of the estimation method and "reg"
for the
estimation output.
The "config"
data frame includes the following elements:
family: "als" (for Asymptotic least squares).
method: "ols" for Ordinary least square, or "fgls" for Feasible generalized least squares.
The list "reg"
includes the following elements (when relevant):
estim:a data frame with
c_names
for the component names,coef
, the estimated coefficients,std_coef
, the estimated standard errors,student
the Student statistics for the equality of the coefficient to 0,p_value
, the p-values of the asymptotic Student test.cova: the estimated covariance matrix of the estimator.
over_test:a data frame with the output of the overidentification test (FGLS only). The statistic is given by
stat
, the degrees of freedom bydf
and the p-value byp_value
References
Chamberlain, G. (1982). Multivariate regression models for panel data. Journal of econometrics, 18(1), 5-46. Gourieroux, C., Monfort, A., & Trognon, A. (1985). Moindres carrés asymptotiques. Annales de l'INSEE, 91-122. Kodde, D. A., Plam, F. C., & Pfann, G. A. (1990). Asymptotic least‐squares estimation efficiency considerations and applications. Journal of Applied Econometrics, 5(3), 229-243.
Examples
model <- list(c("license"),c("woman"),c("woman","license","inter"))
comp <- callback_comp(mobility1,"offer",c("gender","licenses"),"callback",model)
x <- comp$aux_boole
y <- comp$aux_coef
omega <- comp$aux_vcov
str(reg_als(x,y,omega))