# Created by Octave 3.6.1, Wed Mar 28 20:33:11 2012 UTC <root@brouzouf>
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average_moments


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 for internal use by gmm_estimate 



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 for internal use by gmm_estimate 




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delta_method


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 Computes Delta method mean and covariance of a nonlinear
 transformation defined by "func"



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 Computes Delta method mean and covariance of a nonlinear
 transformation define



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gmm_estimate


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 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



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 usage: [theta, obj_value, convergence, iters] =
           gmm_estimate(theta, 



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gmm_example


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 GMM example file, shows initial consistent estimator,
 estimation of efficient weight, and second round
 efficient estimator



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 GMM example file, shows initial consistent estimator,
 estimation of efficient 



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gmm_obj


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 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)



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 The GMM objective function, for internal use by gmm_estimate
 This is scaled so



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gmm_results


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 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



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 usage: [theta, V, obj_value] =
  gmm_results(theta, data, weight, moments, mome



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gmm_variance


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 GMM variance, which assumes weights are optimal



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 GMM variance, which assumes weights are optimal




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gmm_variance_inefficient


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 GMM variance, which assumes weights are not optimal



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 GMM variance, which assumes weights are not optimal




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kernel_density


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 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



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 kernel_density: multivariate kernel density estimator




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kernel_density_cvscore


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 some kernels can assign zero density



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 some kernels can assign zero density




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kernel_density_nodes


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 kernel_density_nodes: for internal use by kernel_density - does calculations on nodes



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 kernel_density_nodes: for internal use by kernel_density - does calculations on



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kernel_example


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 kernel_example: examples of how to use kernel density and regression functions
 requires the optim and plot packages from Octave Forge

 usage: kernel_example;



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 kernel_example: examples of how to use kernel density and regression functions




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kernel_optimal_bandwidth


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 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



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 kernel_optimal_bandwidth: find optimal bandwith doing leave-one-out cross valid



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kernel_regression


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 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.




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 kernel_regression: kernel regression estimator




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kernel_regression_nodes


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 kernel_regression_nodes: for internal use by kernel_regression - does calculations on nodes



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 kernel_regression_nodes: for internal use by kernel_regression - does calculati



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mle_estimate


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 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



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 usage:
 [theta, obj_value, conv, iters] = mle_estimate(theta, data, model, mode



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mle_example


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 Example to show how to use MLE functions



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 Example to show how to use MLE functions




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mle_obj


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 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



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 usage: [obj_value, score] = mle_obj(theta, data, model, modelargs, nslaves)




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mle_obj_nodes


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 Who am I?



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 Who am I?




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mle_results


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 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



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 usage: [theta, V, obj_value, infocrit] =
    mle_results(theta, data, model, mo



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mle_variance


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 usage: [V,scorecontribs,J_inv] =
  mle_variance(theta, data, model, modelargs)

 This is for internal use by mle_results



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 usage: [V,scorecontribs,J_inv] =
  mle_variance(theta, data, model, modelargs)




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nls_estimate


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 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



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 usage:
 [theta, obj_value, conv, iters] = nls_estimate(theta, data, model, mode



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nls_example


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 define arguments for nls_estimate #

 starting values



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 define arguments for nls_estimate #




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nls_obj


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 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



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 usage: [obj_value, score] = nls_obj(theta, data, model, modelargs, nslaves)




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nls_obj_nodes


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 This is for internal use by nls_estimate



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 This is for internal use by nls_estimate




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parameterize


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 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



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 usage: theta = parameterize(theta, otherargs)
 
 This is an empty function, pro



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poisson


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 Example likelihood function (Poisson for count data) with score



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 Example likelihood function (Poisson for count data) with score




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poisson_moments


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 the form a user-written moment function should take



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 the form a user-written moment function should take




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prettyprint


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 this prints matrices with row and column labels



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 this prints matrices with row and column labels




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prettyprint_c


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 this prints matrices with column labels but no row labels



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 this prints matrices with column labels but no row labels




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scale_data


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 Standardizes and normalizes data matrix,
 primarily for use by BFGS



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 Standardizes and normalizes data matrix,
 primarily for use by BFGS




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sum_moments_nodes


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 for internal use by gmm_estimate



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 for internal use by gmm_estimate




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unscale_parameters


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 Unscales parameters that were estimated using scaled data
 primarily for use by BFGS



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 Unscales parameters that were estimated using scaled data
 primarily for use by