HARK.estimation¶
Functions for estimating structural models, including optimization methods and bootstrapping tools.

HARK.estimation.
bootstrapSampleFromData
(data, weights=None, seed=0)¶ Samples rows from the input array of data, generating a new data array with an equal number of rows (records). Rows are drawn with equal probability by default, but probabilities can be specified with weights (must sum to 1).
Parameters:  data (np.array) – An array of data, with each row representing a record.
 weights (np.array) – A weighting array with length equal to data.shape[0].
 seed (int) – A seed for the random number generator.
Returns: new_data – A resampled version of input data.
Return type: np.array

HARK.estimation.
minimizeNelderMead
(objectiveFunction, parameter_guess, verbose=False, which_vars=None, **kwargs)¶ Minimizes the objective function using the NelderMead simplex algorithm, starting from an initial parameter guess.
Parameters:  objectiveFunction (function) – The function to be minimized. It should take only a single argument, which should be a list representing the parameters to be estimated.
 parameter_guess ([float]) – A starting point for the NelderMead algorithm, which must be a valid input for objectiveFunction.
 which_vars (np.array or None) – Array of booleans indicating which parameters should be estimated. When not provided, estimation is performed on all parameters.
 verbose (boolean) – A flag for the amount of output to print.
Returns: xopt – The values that minimize objectiveFunction.
Return type: [float]

HARK.estimation.
minimizePowell
(objectiveFunction, parameter_guess, verbose=False)¶ Minimizes the objective function using a derivativefree Powell algorithm, starting from an initial parameter guess.
Parameters:  objectiveFunction (function) – The function to be minimized. It should take only a single argument, which should be a list representing the parameters to be estimated.
 parameter_guess ([float]) – A starting point for the Powell algorithm, which must be a valid input for objectiveFunction.
 verbose (boolean) – A flag for the amount of output to print.
Returns: xopt – The values that minimize objectiveFunction.
Return type: [float]