HARK.estimation¶
Functions for estimating structural models, including optimization methods and bootstrapping tools.
Functions
bootstrapSampleFromData (data[, weights, seed]) |
Samples rows from the input array of data, generating a new data array with an equal number of rows (records). |
main () |
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minimizeNelderMead (objectiveFunction, …[, …]) |
Minimizes the objective function using the Nelder-Mead simplex algorithm, starting from an initial parameter guess. |
minimizePowell (objectiveFunction, …[, verbose]) |
Minimizes the objective function using a derivative-free Powell algorithm, starting from an initial parameter guess. |
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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 : np.array
A resampled version of input data.
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HARK.estimation.
minimizeNelderMead
(objectiveFunction, parameter_guess, verbose=False, **kwargs)¶ Minimizes the objective function using the Nelder-Mead 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 Nelder-Mead algorithm, which must be a valid input for objectiveFunction.
- verbose : boolean
A flag for the amount of output to print.
Returns: - xopt : [float]
The values that minimize objectiveFunction.
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HARK.estimation.
minimizePowell
(objectiveFunction, parameter_guess, verbose=False)¶ Minimizes the objective function using a derivative-free 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 : [float]
The values that minimize objectiveFunction.