snowdrop.src.numeric.estimation package

Submodules

snowdrop.src.numeric.estimation.estimate module

Created on Wed Jan 23 12:52:44 2019

@author: AGoumilevski

snowdrop.src.numeric.estimation.estimate.run(y0, model, T, Qm=None, Hm=None, obs=None, steady_state=None, ind_non_missing=None, fit_data_only=False, estimate_Posterior=True, estimate_ML=False, algorithm='SLSQP', linearized=True)[source]

Estimates linear/nonlinear model parameters given measurement data.

Parameters:
param y0:

Starting values of endogenous variables.

type y0:

numpy.array

param model:

Model object.

type model:

Model.

param T:

Time span.

type T:

int.

param Qm:

Covariance matrix of errors of endogenous variables.

type Qm:

numpy.array.

param Hm:

Covariance matrix of errors of measurement variables.

type Hm:

numpy.array.

param obs:

List of measurement variables.

type obs:

list.

param ss:

List of steady states.

type s:

list.

param ind_non_missing:

Indices of non-missing observations.

type ind_non_missing:

list.

param fit_data_only:

If True calibrate model parameters by minimizing sum of squared errors of model fit to data. Otherwise, calibrate model by maximizing sum of the prior likelihood of model parameters and the likelihood of model fit to data.

type fit_data_only:

bool.

param estimate_ML:

If True estimate maximum likelihood only.

type estimate_ML:

bool.

param linearized:

If True model transition matrices are computed at steady state. Use this option to speed up calculations.

type linearized:

bool.

returns:

Numerical solution.

snowdrop.src.numeric.estimation.test_grad module

snowdrop.src.numeric.estimation.test_grad.a_dfdx(x, y)
snowdrop.src.numeric.estimation.test_grad.a_dfdy(x, y)
snowdrop.src.numeric.estimation.test_grad.datagen(N, beta, sigma)[source]

Generates data for OLS regression. Inputs: N: Number of observations beta: K x 1 true parameter values sigma: std dev of error

snowdrop.src.numeric.estimation.test_grad.f(x, y)[source]
snowdrop.src.numeric.estimation.test_grad.neg_loglike(theta)[source]
snowdrop.src.numeric.estimation.test_grad.neg_loglike_(theta)[source]
snowdrop.src.numeric.estimation.test_grad.test()[source]

snowdrop.src.numeric.estimation.test_mle module

snowdrop.src.numeric.estimation.test_mle.test()[source]

Module contents