ruspy.estimation.mpec.mpec_loglike_cost_params_derivative_model

ruspy.estimation.mpec.mpec_loglike_cost_params_derivative_model(num_states, num_params, disc_fac, scale, maint_func_dev, p_choice)

generates the derivative of the log likelihood function of mpec depending on the model characteristics.

Parameters

num_statespython:int

The size of the state space.

num_paramspython:int

Length of cost parameter vector.

disc_facnumpy.float

see Discount factor

scalenumpy.float

see Scale

maint_func_devfunc

see Maintenance cost function

p_choice numpy.ndarray

num_states x 2 matrix that contains the calculated conditional choice probabilities.

Returns

derivative_both numpy.ndarray

gives out the derivative of the log likelihood function depending on the model characteristics.