X-Git-Url: https://git.creatis.insa-lyon.fr/pubgit/?p=CreaPhase.git;a=blobdiff_plain;f=octave_packages%2Feconometrics-1.0.8%2Fdoc-cache;fp=octave_packages%2Feconometrics-1.0.8%2Fdoc-cache;h=4bc25f5097492f7186ee7b9e8d791bec48ce6217;hp=0000000000000000000000000000000000000000;hb=f5f7a74bd8a4900f0b797da6783be80e11a68d86;hpb=1705066eceaaea976f010f669ce8e972f3734b05 diff --git a/octave_packages/econometrics-1.0.8/doc-cache b/octave_packages/econometrics-1.0.8/doc-cache new file mode 100644 index 0000000..4bc25f5 --- /dev/null +++ b/octave_packages/econometrics-1.0.8/doc-cache @@ -0,0 +1,990 @@ +# Created by Octave 3.6.1, Wed Mar 28 20:33:11 2012 UTC +# name: cache +# type: cell +# rows: 3 +# columns: 33 +# name: +# type: sq_string +# elements: 1 +# length: 15 +average_moments + + +# name: +# type: sq_string +# elements: 1 +# length: 35 + for internal use by gmm_estimate + + + +# name: +# type: sq_string +# elements: 1 +# length: 35 + for internal use by gmm_estimate + + + + +# name: +# type: sq_string +# elements: 1 +# length: 12 +delta_method + + +# name: +# type: sq_string +# elements: 1 +# length: 92 + Computes Delta method mean and covariance of a nonlinear + transformation defined by "func" + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + Computes Delta method mean and covariance of a nonlinear + transformation define + + + +# name: +# type: sq_string +# elements: 1 +# length: 12 +gmm_estimate + + +# name: +# type: sq_string +# elements: 1 +# length: 927 + usage: [theta, obj_value, convergence, iters] = + gmm_estimate(theta, data, weight, moments, momentargs, control, nslaves) + + inputs: + theta: column vector initial parameters + data: data matrix + weight: the GMM weight matrix + moments: name of function computes the moments + (should return nXg matrix of contributions) + momentargs: (cell) additional inputs needed to compute moments. + May be empty ("") + control: (optional) BFGS or SA controls (see bfgsmin and samin). + May be empty (""). + nslaves: (optional) number of slaves if executed in parallel + (requires MPITB) + + outputs: + theta: GMM estimate of parameters + obj_value: the value of the gmm obj. function + convergence: return code from bfgsmin + (1 means success, see bfgsmin for details) + iters: number of BFGS iteration used + + please type "gmm_example" while in octave to see an example + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + usage: [theta, obj_value, convergence, iters] = + gmm_estimate(theta, + + + +# name: +# type: sq_string +# elements: 1 +# length: 11 +gmm_example + + +# name: +# type: sq_string +# elements: 1 +# length: 126 + GMM example file, shows initial consistent estimator, + estimation of efficient weight, and second round + efficient estimator + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + GMM example file, shows initial consistent estimator, + estimation of efficient + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +gmm_obj + + +# name: +# type: sq_string +# elements: 1 +# length: 206 + The GMM objective function, for internal use by gmm_estimate + This is scaled so that it converges to a finite number. + To get the chi-square specification + test you need to multiply by n (the sample size) + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + The GMM objective function, for internal use by gmm_estimate + This is scaled so + + + +# name: +# type: sq_string +# elements: 1 +# length: 11 +gmm_results + + +# name: +# type: sq_string +# elements: 1 +# length: 1145 + usage: [theta, V, obj_value] = + gmm_results(theta, data, weight, moments, momentargs, names, title, unscale, control, nslaves) + + inputs: + theta: column vector initial parameters + data: data matrix + weight: the GMM weight matrix + moments: name of function computes the moments + (should return nXg matrix of contributions) + momentargs: (cell) additional inputs needed to compute moments. + May be empty ("") + names: vector of parameter names + e.g., names = char("param1", "param2"); + title: string, describes model estimated + unscale: (optional) cell that holds means and std. dev. of data + (see scale_data) + control: (optional) BFGS or SA controls (see bfgsmin and samin). May be empty (""). + nslaves: (optional) number of slaves if executed in parallel + (requires MPITB) + + outputs: + theta: GMM estimated parameters + V: estimate of covariance of parameters. Assumes the weight matrix + is optimal (inverse of covariance of moments) + obj_value: the value of the GMM objective function + + please type "gmm_example" while in octave to see an example + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + usage: [theta, V, obj_value] = + gmm_results(theta, data, weight, moments, mome + + + +# name: +# type: sq_string +# elements: 1 +# length: 12 +gmm_variance + + +# name: +# type: sq_string +# elements: 1 +# length: 49 + GMM variance, which assumes weights are optimal + + + +# name: +# type: sq_string +# elements: 1 +# length: 49 + GMM variance, which assumes weights are optimal + + + + +# name: +# type: sq_string +# elements: 1 +# length: 24 +gmm_variance_inefficient + + +# name: +# type: sq_string +# elements: 1 +# length: 53 + GMM variance, which assumes weights are not optimal + + + +# name: +# type: sq_string +# elements: 1 +# length: 53 + GMM variance, which assumes weights are not optimal + + + + +# name: +# type: sq_string +# elements: 1 +# length: 14 +kernel_density + + +# name: +# type: sq_string +# elements: 1 +# length: 1178 + kernel_density: multivariate kernel density estimator + + usage: + dens = kernel_density(eval_points, data, bandwidth) + + inputs: + eval_points: PxK matrix of points at which to calculate the density + data: NxK matrix of data points + bandwidth: positive scalar, the smoothing parameter. The fit + is more smooth as the bandwidth increases. + kernel (optional): string. Name of the kernel function. Default is + Gaussian kernel. + prewhiten bool (optional): default false. If true, rotate data + using Choleski decomposition of inverse of covariance, + to approximate independence after the transformation, which + makes a product kernel a reasonable choice. + do_cv: bool (optional). default false. If true, calculate leave-1-out + density for cross validation + computenodes: int (optional, default 0). + Number of compute nodes for parallel evaluation + debug: bool (optional, default false). show results on compute nodes if doing + a parallel run + outputs: + dens: Px1 vector: the fitted density value at each of the P evaluation points. + + References: + Wand, M.P. and Jones, M.C. (1995), 'Kernel smoothing'. + http://www.xplore-stat.de/ebooks/scripts/spm/html/spmhtmlframe73.html + + + +# name: +# type: sq_string +# elements: 1 +# length: 55 + kernel_density: multivariate kernel density estimator + + + + +# name: +# type: sq_string +# elements: 1 +# length: 22 +kernel_density_cvscore + + +# name: +# type: sq_string +# elements: 1 +# length: 38 + some kernels can assign zero density + + + +# name: +# type: sq_string +# elements: 1 +# length: 38 + some kernels can assign zero density + + + + +# name: +# type: sq_string +# elements: 1 +# length: 20 +kernel_density_nodes + + +# name: +# type: sq_string +# elements: 1 +# length: 87 + kernel_density_nodes: for internal use by kernel_density - does calculations on nodes + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + kernel_density_nodes: for internal use by kernel_density - does calculations on + + + +# name: +# type: sq_string +# elements: 1 +# length: 14 +kernel_example + + +# name: +# type: sq_string +# elements: 1 +# length: 161 + kernel_example: examples of how to use kernel density and regression functions + requires the optim and plot packages from Octave Forge + + usage: kernel_example; + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + kernel_example: examples of how to use kernel density and regression functions + + + + +# name: +# type: sq_string +# elements: 1 +# length: 24 +kernel_optimal_bandwidth + + +# name: +# type: sq_string +# elements: 1 +# length: 309 + kernel_optimal_bandwidth: find optimal bandwith doing leave-one-out cross validation + inputs: + * data: data matrix + * depvar: column vector or empty (""). + If empty, do kernel density, orherwise, kernel regression + * kernel (optional, string) the kernel function to use + output: + * h: the optimal bandwidth + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + kernel_optimal_bandwidth: find optimal bandwith doing leave-one-out cross valid + + + +# name: +# type: sq_string +# elements: 1 +# length: 17 +kernel_regression + + +# name: +# type: sq_string +# elements: 1 +# length: 1100 + kernel_regression: kernel regression estimator + + usage: + fit = kernel_regression(eval_points, depvar, condvars, bandwidth) + + inputs: + eval_points: PxK matrix of points at which to calculate the density + depvar: Nx1 vector of observations of the dependent variable + condvars: NxK matrix of data points + bandwidth (optional): positive scalar, the smoothing parameter. + Default is N ^ (-1/(4+K)) + kernel (optional): string. Name of the kernel function. Default is + Gaussian kernel. + prewhiten bool (optional): default true. If true, rotate data + using Choleski decomposition of inverse of covariance, + to approximate independence after the transformation, which + makes a product kernel a reasonable choice. + do_cv: bool (optional). default false. If true, calculate leave-1-out + fit to calculate the cross validation score + computenodes: int (optional, default 0). + Number of compute nodes for parallel evaluation + debug: bool (optional, default false). show results on compute nodes if doing + a parallel run + outputs: + fit: Px1 vector: the fitted value at each of the P evaluation points. + + + + +# name: +# type: sq_string +# elements: 1 +# length: 48 + kernel_regression: kernel regression estimator + + + + +# name: +# type: sq_string +# elements: 1 +# length: 23 +kernel_regression_nodes + + +# name: +# type: sq_string +# elements: 1 +# length: 93 + kernel_regression_nodes: for internal use by kernel_regression - does calculations on nodes + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + kernel_regression_nodes: for internal use by kernel_regression - does calculati + + + +# name: +# type: sq_string +# elements: 1 +# length: 12 +mle_estimate + + +# name: +# type: sq_string +# elements: 1 +# length: 758 + usage: + [theta, obj_value, conv, iters] = mle_estimate(theta, data, model, modelargs, control, nslaves) + + inputs: + theta: column vector of model parameters + data: data matrix + model: name of function that computes log-likelihood + modelargs: (cell) additional inputs needed by model. May be empty ("") + control: (optional) BFGS or SA controls (see bfgsmin and samin). May be empty (""). + nslaves: (optional) number of slaves if executed in parallel (requires MPITB) + + outputs: + theta: ML estimated value of parameters + obj_value: the value of the log likelihood function at ML estimate + conv: return code from bfgsmin (1 means success, see bfgsmin for details) + iters: number of BFGS iteration used + + please see mle_example.m for examples of how to use this + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + usage: + [theta, obj_value, conv, iters] = mle_estimate(theta, data, model, mode + + + +# name: +# type: sq_string +# elements: 1 +# length: 11 +mle_example + + +# name: +# type: sq_string +# elements: 1 +# length: 42 + Example to show how to use MLE functions + + + +# name: +# type: sq_string +# elements: 1 +# length: 42 + Example to show how to use MLE functions + + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +mle_obj + + +# name: +# type: sq_string +# elements: 1 +# length: 178 + usage: [obj_value, score] = mle_obj(theta, data, model, modelargs, nslaves) + + Returns the average log-likelihood for a specified model + This is for internal use by mle_estimate + + + +# name: +# type: sq_string +# elements: 1 +# length: 77 + usage: [obj_value, score] = mle_obj(theta, data, model, modelargs, nslaves) + + + + +# name: +# type: sq_string +# elements: 1 +# length: 13 +mle_obj_nodes + + +# name: +# type: sq_string +# elements: 1 +# length: 11 + Who am I? + + + +# name: +# type: sq_string +# elements: 1 +# length: 11 + Who am I? + + + + +# name: +# type: sq_string +# elements: 1 +# length: 11 +mle_results + + +# name: +# type: sq_string +# elements: 1 +# length: 918 + usage: [theta, V, obj_value, infocrit] = + mle_results(theta, data, model, modelargs, names, title, unscale, control) + + inputs: + theta: column vector of model parameters + data: data matrix + model: name of function that computes log-likelihood + modelargs: (cell) additional inputs needed by model. May be empty ("") + names: vector of parameter names, e.g., use names = char("param1", "param2"); + title: string, describes model estimated + unscale: (optional) cell that holds means and std. dev. of data (see scale_data) + control: (optional) BFGS or SA controls (see bfgsmin and samin). May be empty (""). + nslaves: (optional) number of slaves if executed in parallel (requires MPITB) + + outputs: + theta: ML estimated value of parameters + obj_value: the value of the log likelihood function at ML estimate + conv: return code from bfgsmin (1 means success, see bfgsmin for details) + iters: number of BFGS iteration used + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + usage: [theta, V, obj_value, infocrit] = + mle_results(theta, data, model, mo + + + +# name: +# type: sq_string +# elements: 1 +# length: 12 +mle_variance + + +# name: +# type: sq_string +# elements: 1 +# length: 122 + usage: [V,scorecontribs,J_inv] = + mle_variance(theta, data, model, modelargs) + + This is for internal use by mle_results + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + usage: [V,scorecontribs,J_inv] = + mle_variance(theta, data, model, modelargs) + + + + +# name: +# type: sq_string +# elements: 1 +# length: 12 +nls_estimate + + +# name: +# type: sq_string +# elements: 1 +# length: 780 + usage: + [theta, obj_value, conv, iters] = nls_estimate(theta, data, model, modelargs, control, nslaves) + + inputs: + theta: column vector of model parameters + data: data matrix + model: name of function that computes the vector of sums of squared errors + modelargs: (cell) additional inputs needed by model. May be empty ("") + control: (optional) BFGS or SA controls (see bfgsmin and samin). May be empty (""). + nslaves: (optional) number of slaves if executed in parallel (requires MPITB) + + outputs: + theta: NLS estimated value of parameters + obj_value: the value of the sum of squared errors at NLS estimate + conv: return code from bfgsmin (1 means success, see bfgsmin for details) + iters: number of BFGS iteration used + + please see nls_example.m for examples of how to use this + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + usage: + [theta, obj_value, conv, iters] = nls_estimate(theta, data, model, mode + + + +# name: +# type: sq_string +# elements: 1 +# length: 11 +nls_example + + +# name: +# type: sq_string +# elements: 1 +# length: 56 + + define arguments for nls_estimate # + + starting values + + + +# name: +# type: sq_string +# elements: 1 +# length: 38 + + define arguments for nls_estimate # + + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +nls_obj + + +# name: +# type: sq_string +# elements: 1 +# length: 185 + usage: [obj_value, score] = nls_obj(theta, data, model, modelargs, nslaves) + + Returns the average sum of squared errors for a specified model + This is for internal use by nls_estimate + + + +# name: +# type: sq_string +# elements: 1 +# length: 77 + usage: [obj_value, score] = nls_obj(theta, data, model, modelargs, nslaves) + + + + +# name: +# type: sq_string +# elements: 1 +# length: 13 +nls_obj_nodes + + +# name: +# type: sq_string +# elements: 1 +# length: 42 + This is for internal use by nls_estimate + + + +# name: +# type: sq_string +# elements: 1 +# length: 42 + This is for internal use by nls_estimate + + + + +# name: +# type: sq_string +# elements: 1 +# length: 12 +parameterize + + +# name: +# type: sq_string +# elements: 1 +# length: 316 + usage: theta = parameterize(theta, otherargs) + + This is an empty function, provided so that + delta_method will work as is. Replace it with + the parameter transformations your models use. + Note: you can let "otherargs" contain the model + name so that this function can do parameterizations + for a variety of models + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + usage: theta = parameterize(theta, otherargs) + + This is an empty function, pro + + + +# name: +# type: sq_string +# elements: 1 +# length: 7 +poisson + + +# name: +# type: sq_string +# elements: 1 +# length: 65 + Example likelihood function (Poisson for count data) with score + + + +# name: +# type: sq_string +# elements: 1 +# length: 65 + Example likelihood function (Poisson for count data) with score + + + + +# name: +# type: sq_string +# elements: 1 +# length: 15 +poisson_moments + + +# name: +# type: sq_string +# elements: 1 +# length: 53 + the form a user-written moment function should take + + + +# name: +# type: sq_string +# elements: 1 +# length: 53 + the form a user-written moment function should take + + + + +# name: +# type: sq_string +# elements: 1 +# length: 11 +prettyprint + + +# name: +# type: sq_string +# elements: 1 +# length: 49 + this prints matrices with row and column labels + + + +# name: +# type: sq_string +# elements: 1 +# length: 49 + this prints matrices with row and column labels + + + + +# name: +# type: sq_string +# elements: 1 +# length: 13 +prettyprint_c + + +# name: +# type: sq_string +# elements: 1 +# length: 59 + this prints matrices with column labels but no row labels + + + +# name: +# type: sq_string +# elements: 1 +# length: 59 + this prints matrices with column labels but no row labels + + + + +# name: +# type: sq_string +# elements: 1 +# length: 10 +scale_data + + +# name: +# type: sq_string +# elements: 1 +# length: 69 + Standardizes and normalizes data matrix, + primarily for use by BFGS + + + +# name: +# type: sq_string +# elements: 1 +# length: 69 + Standardizes and normalizes data matrix, + primarily for use by BFGS + + + + +# name: +# type: sq_string +# elements: 1 +# length: 17 +sum_moments_nodes + + +# name: +# type: sq_string +# elements: 1 +# length: 34 + for internal use by gmm_estimate + + + +# name: +# type: sq_string +# elements: 1 +# length: 34 + for internal use by gmm_estimate + + + + +# name: +# type: sq_string +# elements: 1 +# length: 18 +unscale_parameters + + +# name: +# type: sq_string +# elements: 1 +# length: 86 + Unscales parameters that were estimated using scaled data + primarily for use by BFGS + + + +# name: +# type: sq_string +# elements: 1 +# length: 80 + Unscales parameters that were estimated using scaled data + primarily for use by + + + + +