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 Please see mle_example for information on how to use this
Package: econometrics