econometrics
Econometrics functions including MLE and GMM based techniques.
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Computes Delta method mean and covariance of a nonlinear transformation defined by "func"
usage: [theta, obj_value, convergence, iters] = gmm_estimate(theta, data, weight, moments, momentargs, control, nslaves)
GMM example file, shows initial consistent estimator, estimation of efficient weight, and second round efficient estimator
The GMM objective function, for internal use by gmm_estimate This is scaled so that it converges to a finite number.
usage: [theta, V, obj_value] = gmm_results(theta, data, weight, moments, momentargs, names, title, unscale, control, nslaves)
GMM variance, which assumes weights are optimal
GMM variance, which assumes weights are not optimal
usage: [theta, obj_value, conv, iters] = mle_estimate(theta, data, model, modelargs, control, nslaves)
Example to show how to use MLE functions
usage: [obj_value, score] = mle_obj(theta, data, model, modelargs, nslaves)
usage: [theta, V, obj_value, infocrit] = mle_results(theta, data, model, modelargs, names, title, unscale, control)
usage: theta = parameterize(theta, otherargs) This is an empty function, provided so that delta_method will work as is.
this prints matrices with row and column labels
Standardizes and normalizes data matrix, primarily for use by BFGS
Unscales parameters that were estimated using scaled data primarily for use by BFGS
usage: [theta, obj_value, conv, iters] = nls_estimate(theta, data, model, modelargs, control, nslaves)
usage: [obj_value, score] = nls_obj(theta, data, model, modelargs, nslaves)
contrib = mle_obj_nodes(theta, data, model, modelargs, nn)
define arguments for nls_estimate #
Example likelihood function (Poisson for count data) with score
the form a user-written moment function should take
this prints matrices with column labels but no row labels
kernel_density: multivariate kernel density estimator
cvscore = kernel_density_cvscore(bandwidth, data, kernel)
kernel_example: examples of how to use kernel density and regression functions requires the optim and plot packages from Octave Forge
kernel_optimal_bandwidth: find optimal bandwith doing leave-one-out cross validation inputs: * data: data matrix * depvar: column vector or empty ("").
kernel_regression: kernel regression estimator
cvscore = kernel_regression_cvscore(bandwidth, data, depvar)
Package: econometrics