econometrics

Econometrics functions including MLE and GMM based techniques.

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Econometrics

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

Package: econometrics