/**
* Generate random samples chosen from the multivariate Gaussian
* distribution with mean MU and covariance SIGMA.
*
* X ~ N(u, Lambda) => Y = B * X + v ~ N(B * u + v, B * Lambda * B')
* Therefore, if X ~ N(0, Lambda),
* then Y = B * X + MU ~ N(MU, B * Lambda * B').
* We only need to do the eigen decomposition: SIGMA = B * Lambda * B'.
*
* @param MU a 1D {@code double} array holding the mean vector
*
* @param d dimensionality of the mean vector
*
* @param SIGMA a 2D {@code double} array holding the covariance matrix
*
* @param cases number of d dimensional random samples
*
* @return cases-by-d sample matrix subject to the multivariate
* Gaussian distribution N(MU, SIGMA)
*
*/
Matrix& mvnrnd(double* MU, int d, double** SIGMA, int cases) {
return mvnrnd(*new DenseMatrix(MU, d, 2), *new DenseMatrix(SIGMA, d, d), cases);
}
bool estimation_init(float param[], float husbands_2[][HUSBANDS_COL], float husbands_3[][HUSBANDS_COL], float husbands_4[][HUSBANDS_COL], float husbands_5[][HUSBANDS_COL],
float wives[][WIVES_COL], float wage_moments[][WAGE_MOM_COL], float marr_fer_moments[][MARR_MOM_COL], float emp_moments[][EMP_MOM_COL], float general_moments[][GEN_MOM_COL],
float* epsilon_b_arr, float* epsilon_f_arr, float* h_draws_b_arr, float* w_draws_b_arr, uint16_t* w_ability_draw_arr, float* h_draws_arr, float* w_draws_arr)
{
timeval tv = tic();
std::cout << "Loading files..." << std::endl;
if (!load<HUSBANDS_COL>("husbands_2.out", husbands_2, HUSBANDS_ROWS))
{
return false;
}
if (!load<HUSBANDS_COL>("husbands.out", husbands_3, HUSBANDS_ROWS))
{
return false;
}
if (!load<HUSBANDS_COL>("husbands_4.out", husbands_4, HUSBANDS_ROWS))
{
return false;
}
if (!load<HUSBANDS_COL>("husbands_5.out", husbands_5, HUSBANDS_ROWS))
{
return false;
}
if (!load<WIVES_COL>("wives.out", wives, WIVES_ROWS))
{
return false;
}
if (!load<WAGE_MOM_COL>("wage_moments.txt", wage_moments, WAGE_MOM_ROWS))
{
return false;
}
if (!load<MARR_MOM_COL>("marr_fer_moments.txt", marr_fer_moments, MARR_MOM_ROWS))
{
return false;
}
if (!load<EMP_MOM_COL>("emp_moments.txt", emp_moments, EMP_MOM_ROWS))
{
return false;
}
if (!load<GEN_MOM_COL>("general_moments.txt", general_moments, GEN_MOM_ROWS))
{
return false;
}
if (!load<INIT_PARAM_COL>("init_parameters.txt", param, INIT_PARAM_ROWS))
{
return false;
}
// some validations on override parameters
if (PARAM_ROWS != PARAM_IDX_ROWS || INIT_PARAM_ROWS < PARAM_IDX_ROWS)
{
std::cerr << "Invalid number of override parameters (#initial=" << INIT_PARAM_ROWS << ",#override=" << PARAM_ROWS << ",#index=" << PARAM_IDX_ROWS << ")" << std::endl;
std::cerr << "Not overriding initial parameters" << std::endl;
}
else
{
float override_param[PARAM_ROWS];
if (!load<PARAM_COL>("parameters.txt", override_param, PARAM_ROWS))
{
std::cerr << "Not overriding initial parameters" << std::endl;
}
else
{
uint16_t override_index[PARAM_IDX_ROWS];
if (!load<PARAM_IDX_COL, uint16_t>("parameters_idx.txt", override_index, PARAM_IDX_ROWS))
{
std::cerr << "Not overriding initial parameters" << std::endl;
}
else
{
for (uint16_t i = 0; i < PARAM_ROWS; ++i)
{
if (override_index[i] < INIT_PARAM_ROWS)
{
param[override_index[i]] = override_param[i];
}
else
{
std::cerr << "Invalid parameter index " << override_index[i] << " at row " << i << ", parameter override is skipped" << std::endl;
}
}
}
}
}
print_toc(tv);
tv = tic();
std::cout << "Initializing random shocks..." << std::endl;
// start with the same random shoks
srand(1234);
// random shocks' mean and variance
const float myu = 0;
const float sig = 1;
// random draw from normal distribution, for EMAX calculation
for (uint16_t i = 0; i < DRAW_B; ++i)
{
for (uint16_t j = 0; j < T_MAX; ++j)
{
for (uint16_t k = 0; k < SCHOOL_GROUPS; ++k)
{
for (uint16_t s = 0; s < 2; ++s)
{
epsilon_b(i,j,k,s) = mvnrnd(myu, sig);
}
}
}
}
// TODO PERF reorder the nested loops and put draws in inner most and merge the F with the B loops
// random draw from normal distribution, for shocks realizations in forward solution
for (uint16_t i = 0; i < DRAW_F; ++i)
{
for (uint16_t j = 0; j < T_MAX; ++j)
{
for (uint16_t k = 0; k < SCHOOL_GROUPS; ++k)
{
for (uint16_t s = 0; s < 2; ++s)
{
epsilon_f(i,j,k,s) = mvnrnd(myu, sig);
}
}
}
}
// random draw from uniform distribution, for backward solution
for (uint16_t i = 0; i < DRAW_B; ++i)
{
for (uint16_t j = 0; j < T_MAX; ++j)
{
for (uint16_t k = 0; k < SCHOOL_GROUPS; ++k)
{
// 0-HUSBAND; 1-HUSBAND EXP+SCHOOLING; 2-HUSBAND ABILITY; 3-INITIAL MATCH QUALITY;
h_draws_b(i,j,k,h_b_HUSBAND) = rand01();
h_draws_b(i,j,k,h_b_HUSBAND_EXP_SCHOOLING) = rand01();
h_draws_b(i,j,k,h_b_HUSBAND_ABILITY) = (float)randi(MAX_ABILITY_IDX);
h_draws_b(i,j,k,h_b_INITIAL_MATCH_QUALITY) = (float)randi(MAX_Q_IDX);
}
}
}
for (uint16_t i = 0; i < DRAW_B; ++i)
{
for (uint16_t j = 0; j < T_MAX; ++j)
{
for (uint16_t k = 0; k < SCHOOL_GROUPS; ++k)
{
// 0- wife; 1-wife EXP+SCHOOLING+PREV_STATE; 2-wife ABILITY; 3-INITIAL MATCH QUALITY; 4-JOB OFFER FOR MARRIED MEN AND WOMEN EMAX; 5-JOB OFFER FOR SINGLE MEN EMAX
w_draws_b(i,j,k,w_b_WIFE) = rand01();
w_draws_b(i,j,k,WIFE_EXP_SCHOOLING_PREV_STATE) = rand01();
w_draws_b(i,j,k,WIFE_ABILITY) = (float)randi(MAX_ABILITY_IDX);
w_draws_b(i,j,k,w_b_INITIAL_MATCH_QUALITY) = (float)randi(MAX_Q_IDX);
w_draws_b(i,j,k,JOB_OFFER_MARRIED_MEN_WOMEN) = rand01();
w_draws_b(i,j,k,JOB_OFFER_SINGLE_MEN) = rand01();
}
}
}
// random draw from uniform distribution, for forward solution
for (uint16_t i = 0; i < DRAW_F; ++i)
{
for (uint16_t k = 0; k < SCHOOL_GROUPS; ++k)
{
// wife ability draw, constant by period
w_ability_draw(i,k) = randi(MAX_ABILITY_IDX);
}
}
for (uint16_t i = 0; i < DRAW_F; ++i)
{
for (uint16_t j = 0; j < T_MAX; ++j)
{
for (uint16_t k = 0; k < SCHOOL_GROUPS; ++k)
{
// 0- HUSBAND; 1-HUSBAND EXP+SCHOOLING; 2-HUSBAND ABILITY; 3-INITIAL MATCH QUALITY; 4-MATCH UALITY CHANGE PROBABILITY
for (uint16_t s = 0; s < 2; ++s)
{
h_draws(i,j,k,h_HUSBAND) = rand01();
h_draws(i,j,k,h_HUSBAND_EXP_SCHOOLING) = rand01();
h_draws(i,j,k,h_HUSBAND_ABILITY) = (float)randi(MAX_ABILITY_IDX);
h_draws(i,j,k,h_INITIAL_MATCH_QUALITY) = (float)randi(MAX_Q_IDX);
h_draws(i,j,k,MATCH_UALITY_CHANGE_PROBABILITY) = rand01();
}
}
}
}
for (uint16_t i = 0; i < DRAW_F; ++i)
{
for (uint8_t j = 0; j < T_MAX; ++j)
{
for (uint8_t k = 0; k < SCHOOL_GROUPS; ++k)
{
for (uint8_t s = 0; s < 2; ++s)
{
// 0 - JOB OFFER, 1 - NEW BORN
w_draws(i,j,k,s) = rand01();
}
}
}
}
print_toc(tv);
return true;
}
