コード例 #1
0
ファイル: household.py プロジェクト: jdclawson/dynamic
def euler_labor_leisure_func(w, r, e, n_guess, b_s, b_splus1, BQ, factor, T_H, chi_n, params, theta, tau_bq, lambdas):
    """
    This function is usually looped through over J, so it does one ability group at a time.
    Inputs:
        w = wage rate (scalar)
        r = rental rate (scalar)
        e = ability levels (Sx1 array)
        n_guess = labor distribution (Sx1 array)
        b_s = wealth holdings at the start of a period (Sx1 array)
        b_splus1 = wealth holdings for the next period (Sx1 array)
        BQ = aggregate bequests for a certain ability (scalar)
        factor = scaling factor to convert to dollars (scalar)
        T_H = lump sum tax (scalar)
        chi_n = chi^n_s (Sx1 array)
        params = parameter list (list)
        theta = replacement rate for a certain ability (scalar)
        tau_bq = bequest tax rate (scalar)
        lambdas = ability weight (scalar)
    Output:
        euler = Value of labor leisure euler error (Sx1 array)
    """
    J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire, mean_income_data, a_tax_income, b_tax_income, c_tax_income, d_tax_income, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = (
        params
    )
    tax1 = tax.total_taxes(r, b_s, w, e, n_guess, BQ, lambdas, factor, T_H, None, "SS", False, params, theta, tau_bq)
    cons = get_cons(r, b_s, w, e, n_guess, BQ, lambdas, b_splus1, params, tax1)
    income = (r * b_s + w * e * n_guess) * factor
    deriv = (
        1
        - tau_payroll
        - tax.tau_income(r, b_s, w, e, n_guess, factor, params)
        - tax.tau_income_deriv(r, b_s, w, e, n_guess, factor, params) * income
    )
    euler = marg_ut_cons(cons, params) * w * deriv * e - marg_ut_labor(n_guess, chi_n, params)
    return euler
コード例 #2
0
ファイル: household.py プロジェクト: evan-magnusson/dynamic
def euler_savings_func(w, r, e, n_guess, b_s, b_splus1, b_splus2, BQ, factor, T_H, chi_b, params, theta, tau_bq, rho, lambdas):
    '''
    This function is usually looped through over J, so it does one ability group at a time.
    Inputs:
        w = wage rate (scalar)
        r = rental rate (scalar)
        e = ability levels (Sx1 array)
        n_guess = labor distribution (Sx1 array)
        b_s = wealth holdings at the start of a period (Sx1 array)
        b_splus1 = wealth holdings for the next period (Sx1 array)
        b_splus2 = wealth holdings for 2 periods ahead (Sx1 array)
        BQ = aggregate bequests for a certain ability (scalar)
        factor = scaling factor to convert to dollars (scalar)
        T_H = lump sum tax (scalar)
        chi_b = chi^b_j for a certain ability (scalar)
        params = parameter list (list)
        theta = replacement rate for a certain ability (scalar)
        tau_bq = bequest tax rate (scalar)
        rho = mortality rate (Sx1 array)
        lambdas = ability weight (scalar)
    Output:
        euler = Value of savings euler error (Sx1 array)
    '''
    J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire, mean_income_data, \
    a_tax_income, b_tax_income, c_tax_income, d_tax_income, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = params
    # In order to not have 2 savings euler equations (one that solves the first S-1 equations, and one that solves the last one),
    # we combine them.  In order to do this, we have to compute a consumption term in period t+1, which requires us to have a shifted
    # e and n matrix.  We append a zero on the end of both of these so they will be the right size.  We could append any value to them,
    # since in the euler equation, the coefficient on the marginal utility of consumption for this term will be zero (since rho is one).
    e_extended = np.array(list(e) + [0])
    n_extended = np.array(list(n_guess) + [0])
    tax1 = tax.total_taxes(r, b_s, w, e, n_guess, BQ, lambdas, factor, T_H, None, 'SS', False, params, theta, tau_bq)
    tax2 = tax.total_taxes(r, b_splus1, w, e_extended[1:], n_extended[1:], BQ, lambdas, factor, T_H, None, 'SS', True, params, theta, tau_bq)
    cons1 = get_cons(r, b_s, w, e, n_guess, BQ, lambdas, b_splus1, params, tax1)
    cons2 = get_cons(r, b_splus1, w, e_extended[1:], n_extended[1:], BQ, lambdas, b_splus2, params, tax2)
    income = (r * b_splus1 + w * e_extended[1:] * n_extended[1:]) * factor
    deriv = (
        1 + r*(1-tax.tau_income(r, b_splus1, w, e_extended[1:], n_extended[1:], factor, params)-tax.tau_income_deriv(
            r, b_splus1, w, e_extended[1:], n_extended[1:], factor, params)*income)-tax.tau_w_prime(b_splus1, params)*b_splus1-tax.tau_wealth(b_splus1, params))
    savings_ut = rho * np.exp(-sigma * g_y) * chi_b * b_splus1 ** (-sigma)
    # Again, not who in this equation, the (1-rho) term will zero out in the last period, so the last entry of cons2 can be complete
    # gibberish (which it is).  It just has to exist so cons2 is the right size to match all other arrays in the equation.
    euler = marg_ut_cons(cons1, params) - beta * (1-rho) * deriv * marg_ut_cons(
        cons2, params) * np.exp(-sigma * g_y) - savings_ut
    return euler
コード例 #3
0
ファイル: TPI.py プロジェクト: jdclawson/dynamic
def SS_TPI_firstdoughnutring(guesses, winit, rinit, BQinit, T_H_init, initial_b, factor_ss, j, parameters, theta, tau_bq):
    '''
    Solves the first entries of the upper triangle of the twist doughnut.  This is
    separate from the main TPI function because the the values of b and n are scalars,
    so it is easier to just have a separate function for these cases.
    Inputs:
        guesses = guess for b and n (2x1 list)
        winit = initial wage rate (scalar)
        rinit = initial rental rate (scalar)
        BQinit = initial aggregate bequest (scalar)
        T_H_init = initial lump sum tax (scalar)
        initial_b = initial distribution of capital (SxJ array)
        factor_ss = steady state scaling factor (scalar)
        j = which ability type is being solved for (scalar)
        parameters = list of parameters (list)
        theta = replacement rates (Jx1 array)
        tau_bq = bequest tax rates (Jx1 array)
    Output:
        euler errors (2x1 list)
    '''
    b2 = float(guesses[0])
    n1 = float(guesses[1])
    b1 = float(initial_b[-2, j])
    # Euler 1 equations
    tax1 = tax.total_taxes(rinit, b1, winit, e[-1, j], n1, BQinit, lambdas[
                           j], factor_ss, T_H_init, j, 'TPI_scalar', False, parameters, theta, tau_bq)
    cons1 = household.get_cons(
        rinit, b1, winit, e[-1, j], n1, BQinit, lambdas[j], b2, parameters, tax1)
    bequest_ut = rho[-1] * np.exp(-sigma * g_y) * chi_b[-1, j] * b2 ** (-sigma)
    error1 = household.marg_ut_cons(cons1, parameters) - bequest_ut
    # Euler 2 equations
    income2 = (rinit * b1 + winit * e[-1, j] * n1) * factor_ss
    deriv2 = 1 - tau_payroll - tax.tau_income(rinit, b1, winit, e[
        -1, j], n1, factor_ss, parameters) - tax.tau_income_deriv(
        rinit, b1, winit, e[-1, j], n1, factor_ss, parameters) * income2
    error2 = household.marg_ut_cons(cons1, parameters) * winit * \
        e[-1, j] * deriv2 - household.marg_ut_labor(n1, chi_n[-1], parameters)
    if n1 <= 0 or n1 >= 1:
        error2 += 1e12
    if b2 <= 0:
        error1 += 1e12
    if cons1 <= 0:
        error1 += 1e12
    return [error1] + [error2]
コード例 #4
0
ファイル: TPI.py プロジェクト: jdclawson/dynamic
def Steady_state_TPI_solver(guesses, winit, rinit, BQinit, T_H_init, factor, j, s, t, params, theta, tau_bq, rho, lambdas, e, initial_b, chi_b, chi_n):
    '''
    Parameters:
        guesses = distribution of capital and labor (various length list)
        winit   = wage rate ((T+S)x1 array)
        rinit   = rental rate ((T+S)x1 array)
        BQinit = aggregate bequests ((T+S)x1 array)
        T_H_init = lump sum tax over time ((T+S)x1 array)
        factor = scaling factor (scalar)
        j = which ability type is being solved for (scalar)
        s = which upper triangle loop is being solved for (scalar)
        t = which diagonal is being solved for (scalar)
        params = list of parameters (list)
        theta = replacement rates (Jx1 array)
        tau_bq = bequest tax rate (Jx1 array)
        rho = mortalit rate (Sx1 array)
        lambdas = ability weights (Jx1 array)
        e = ability type (SxJ array)
        initial_b = capital stock distribution in period 0 (SxJ array)
        chi_b = chi^b_j (Jx1 array)
        chi_n = chi^n_s (Sx1 array)
    Output:
        Value of Euler error (various length list)
    '''

    J, S, T, beta, sigma, alpha, Z, delta, ltilde, nu, g_y, g_n_ss, tau_payroll, retire, mean_income_data, \
        a_tax_income, b_tax_income, c_tax_income, d_tax_income, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon = params
    length = len(guesses) / 2
    b_guess = np.array(guesses[:length])
    n_guess = np.array(guesses[length:])

    if length == S:
        b_s = np.array([0] + list(b_guess[:-1]))
    else:
        b_s = np.array([(initial_b[-(s + 3), j])] + list(b_guess[:-1]))
    b_splus1 = b_guess
    b_splus2 = np.array(list(b_guess[1:]) + [0])
    w_s = winit[t:t + length]
    w_splus1 = winit[t + 1:t + length + 1]
    r_s = rinit[t:t + length]
    r_splus1 = rinit[t + 1:t + length + 1]
    n_s = n_guess
    n_extended = np.array(list(n_guess[1:]) + [0])
    e_s = e[-length:, j]
    e_extended = np.array(list(e[-length + 1:, j]) + [0])
    BQ_s = BQinit[t:t + length]
    BQ_splus1 = BQinit[t + 1:t + length + 1]
    T_H_s = T_H_init[t:t + length]
    T_H_splus1 = T_H_init[t + 1:t + length + 1]
    # Savings euler equations
    tax_s = tax.total_taxes(r_s, b_s, w_s, e_s, n_s, BQ_s, lambdas[
                            j], factor, T_H_s, j, 'TPI', False, params, theta, tau_bq)
    tax_splus1 = tax.total_taxes(r_splus1, b_splus1, w_splus1, e_extended, n_extended, BQ_splus1, lambdas[
                                 j], factor, T_H_splus1, j, 'TPI', True, params, theta, tau_bq)
    cons_s = household.get_cons(r_s, b_s, w_s, e_s, n_s, BQ_s, lambdas[
                                j], b_splus1, params, tax_s)
    cons_splus1 = household.get_cons(r_splus1, b_splus1, w_splus1, e_extended, n_extended, BQ_splus1, lambdas[
                                     j], b_splus2, params, tax_splus1)
    income_splus1 = (r_splus1 * b_splus1 + w_splus1 *
                     e_extended * n_extended) * factor
    savings_ut = rho[-(length):] * np.exp(-sigma * g_y) * \
        chi_b[-(length):, j] * b_splus1 ** (-sigma)
    deriv_savings = 1 + r_splus1 * (1 - tax.tau_income(
        r_splus1, b_splus1, w_splus1, e_extended, n_extended, factor, params) - tax.tau_income_deriv(
        r_splus1, b_splus1, w_splus1, e_extended, n_extended, factor, params) * income_splus1) - tax.tau_w_prime(
        b_splus1, params) * b_splus1 - tax.tau_wealth(b_splus1, params)
    error1 = household.marg_ut_cons(cons_s, params) - beta * (1 - rho[-(length):]) * np.exp(-sigma * g_y) * deriv_savings * household.marg_ut_cons(
        cons_splus1, params) - savings_ut
    # Labor leisure euler equations
    income_s = (r_s * b_s + w_s * e_s * n_s) * factor
    deriv_laborleisure = 1 - tau_payroll - tax.tau_income(r_s, b_s, w_s, e_s, n_s, factor, params) - tax.tau_income_deriv(
        r_s, b_s, w_s, e_s, n_s, factor, params) * income_s
    error2 = household.marg_ut_cons(cons_s, params) * w_s * e[-(
        length):, j] * deriv_laborleisure - household.marg_ut_labor(n_s, chi_n[-length:], params)
    # Check and punish constraint violations
    mask1 = n_guess < 0
    error2[mask1] += 1e12
    mask2 = n_guess > ltilde
    error2[mask2] += 1e12
    mask3 = cons_s < 0
    error2[mask3] += 1e12
    mask4 = b_guess <= 0
    error2[mask4] += 1e12
    mask5 = cons_splus1 < 0
    error2[mask5] += 1e12
    return list(error1.flatten()) + list(error2.flatten())
コード例 #5
0
ファイル: TPI.py プロジェクト: jdebacker/wealthtax
def run_TPI(income_tax_params, tpi_params, iterative_params,
            initial_values, SS_values, fix_transfers=False,
            output_dir="./OUTPUT"):

    # unpack tuples of parameters
    analytical_mtrs, etr_params, mtrx_params, mtry_params = income_tax_params
    maxiter, mindist_SS, mindist_TPI = iterative_params
    J, S, T, BW, beta, sigma, alpha, Z, delta, ltilde, nu, g_y,\
                  g_n_vector, tau_payroll, tau_bq, rho, omega, N_tilde, lambdas, imm_rates, e, retire, mean_income_data,\
                  factor, T_H_baseline, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon, chi_b, chi_n, theta = tpi_params
    K0, b_sinit, b_splus1init, factor, initial_b, initial_n, omega_S_preTP = initial_values
    Kss, Lss, rss, wss, BQss, T_Hss, Gss, bssmat_splus1, nssmat = SS_values


    TPI_FIG_DIR = output_dir
    # Initialize guesses at time paths
    domain = np.linspace(0, T, T)
    r = np.ones(T + S) * rss
    BQ = np.zeros((T + S, J))
    BQ0_params = (omega_S_preTP.reshape(S, 1), lambdas, rho.reshape(S, 1), g_n_vector[0], 'SS')
    BQ0 = household.get_BQ(r[0], initial_b, BQ0_params)
    for j in xrange(J):
        BQ[:, j] = list(np.linspace(BQ0[j], BQss[j], T)) + [BQss[j]] * S
    BQ = np.array(BQ)
    # print "BQ values = ", BQ[0, :], BQ[100, :], BQ[-1, :], BQss
    # print "K0 vs Kss = ", K0-Kss

    if fix_transfers:
        T_H = T_H_baseline
    else:
        if np.abs(T_Hss) < 1e-13 :
            T_Hss2 = 0.0 # sometimes SS is very small but not zero, even if taxes are zero, this get's rid of the approximation error, which affects the perc changes below
        else:
            T_Hss2 = T_Hss
        T_H = np.ones(T + S) * T_Hss2 * (r/rss)
    G = np.ones(T + S) * Gss
    # # print "T_H values = ", T_H[0], T_H[100], T_H[-1], T_Hss
    # # print "omega diffs = ", (omega_S_preTP-omega[-1]).max(), (omega[10]-omega[-1]).max()
    #
    # Make array of initial guesses for labor supply and savings
    domain2 = np.tile(domain.reshape(T, 1, 1), (1, S, J))
    ending_b = bssmat_splus1
    guesses_b = (-1 / (domain2 + 1)) * (ending_b - initial_b) + ending_b
    ending_b_tail = np.tile(ending_b.reshape(1, S, J), (S, 1, 1))
    guesses_b = np.append(guesses_b, ending_b_tail, axis=0)
    # print 'diff btwn start and end b: ', (guesses_b[0]-guesses_b[-1]).max()
    #
    domain3 = np.tile(np.linspace(0, 1, T).reshape(T, 1, 1), (1, S, J))
    guesses_n = domain3 * (nssmat - initial_n) + initial_n
    ending_n_tail = np.tile(nssmat.reshape(1, S, J), (S, 1, 1))
    guesses_n = np.append(guesses_n, ending_n_tail, axis=0)
    # b_mat = np.zeros((T + S, S, J))
    # n_mat = np.zeros((T + S, S, J))
    ind = np.arange(S)
    # # print 'diff btwn start and end n: ', (guesses_n[0]-guesses_n[-1]).max()
    #
    # # find economic aggregates
    K = np.zeros(T+S)
    L = np.zeros(T+S)
    K[0] = K0
    K_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J), imm_rates[:T-1].reshape(T-1,S,1), g_n_vector[1:T], 'TPI')
    K[1:T] = household.get_K(guesses_b[:T-1], K_params)
    K[T:] = Kss
    L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
    L[:T] = firm.get_L(guesses_n[:T], L_params)
    L[T:] = Lss
    Y_params = (alpha, Z)
    Y = firm.get_Y(K, L, Y_params)
    r_params = (alpha, delta)
    r[:T] = firm.get_r(Y[:T], K[:T], r_params)

    # uncomment lines below if want to use starting values from prior run
    r = TPI_START_VALUES['r']
    K = TPI_START_VALUES['K']
    L = TPI_START_VALUES['L']
    Y = TPI_START_VALUES['Y']
    T_H = TPI_START_VALUES['T_H']
    BQ = TPI_START_VALUES['BQ']
    G = TPI_START_VALUES['G']

    guesses_b = TPI_START_VALUES['b_mat']
    guesses_n = TPI_START_VALUES['n_mat']


    TPIiter = 0
    TPIdist = 10
    PLOT_TPI = False

    euler_errors = np.zeros((T, 2 * S, J))
    TPIdist_vec = np.zeros(maxiter)

    # print 'analytical mtrs in tpi = ', analytical_mtrs

    while (TPIiter < maxiter) and (TPIdist >= mindist_TPI):
        # Plot TPI for K for each iteration, so we can see if there is a
        # problem
        if PLOT_TPI is True:
            K_plot = list(K) + list(np.ones(10) * Kss)
            L_plot = list(L) + list(np.ones(10) * Lss)
            plt.figure()
            plt.axhline(
                y=Kss, color='black', linewidth=2, label=r"Steady State $\hat{K}$", ls='--')
            plt.plot(np.arange(
                T + 10), Kpath_plot[:T + 10], 'b', linewidth=2, label=r"TPI time path $\hat{K}_t$")
            plt.savefig(os.path.join(TPI_FIG_DIR, "TPI_K"))


        guesses = (guesses_b, guesses_n)
        w_params = (Z, alpha, delta)
        w = firm.get_w_from_r(r, w_params)
        # print 'r and rss diff = ', r-rss
        # print 'w and wss diff = ', w-wss
        # print 'BQ and BQss diff = ', BQ-BQss
        # print 'T_H and T_Hss diff = ', T_H - T_Hss
        # print 'guess b and bss = ', (bssmat_splus1 - guesses_b).max()
        # print 'guess n and nss = ', (nssmat - guesses_n).max()
        outer_loop_vars = (r, w, BQ, T_H)
        inner_loop_params = (income_tax_params, tpi_params, initial_values, ind)

        # Solve HH problem in inner loop
        euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)

        # print 'guess b and bss = ', (b_mat - guesses_b).max()
        # print 'guess n and nss over time = ', (n_mat - guesses_n).max(axis=2).max(axis=1)
        # print 'guess n and nss over age = ', (n_mat - guesses_n).max(axis=0).max(axis=1)
        # print 'guess n and nss over ability = ', (n_mat - guesses_n).max(axis=0).max(axis=0)
        # quit()

        print 'Max Euler error: ', (np.abs(euler_errors)).max()

        bmat_s = np.zeros((T, S, J))
        bmat_s[0, 1:, :] = initial_b[:-1, :]
        bmat_s[1:, 1:, :] = b_mat[:T-1, :-1, :]
        bmat_splus1 = np.zeros((T, S, J))
        bmat_splus1[:, :, :] = b_mat[:T, :, :]

        K[0] = K0
        K_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J),
                    imm_rates[:T-1].reshape(T-1, S, 1), g_n_vector[1:T], 'TPI')
        K[1:T] = household.get_K(bmat_splus1[:T-1], K_params)
        L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1),
                    lambdas.reshape(1, 1, J), 'TPI')
        L[:T] = firm.get_L(n_mat[:T], L_params)
        # print 'K diffs = ', K-K0
        # print 'L diffs = ', L-L[0]

        Y_params = (alpha, Z)
        Ynew = firm.get_Y(K[:T], L[:T], Y_params)
        r_params = (alpha, delta)
        rnew = firm.get_r(Ynew[:T], K[:T], r_params)
        wnew = firm.get_w_from_r(rnew, w_params)

        omega_shift = np.append(omega_S_preTP.reshape(1, S),
                                omega[:T-1, :], axis=0)
        BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J),
                     rho.reshape(1, S, 1), g_n_vector[:T].reshape(T, 1), 'TPI')
        # b_mat_shift = np.append(np.reshape(initial_b, (1, S, J)),
        #                         b_mat[:T-1, :, :], axis=0)
        b_mat_shift = bmat_splus1[:T, :, :]
        # print 'b diffs = ', (bmat_splus1[100, :, :] - initial_b).max(), (bmat_splus1[0, :, :] - initial_b).max(), (bmat_splus1[1, :, :] - initial_b).max()
        # print 'r diffs = ', rnew[1]-r[1], rnew[100]-r[100], rnew[-1]-r[-1]
        BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat_shift,
                                 BQ_params)
        BQss2 = np.empty(J)
        for j in range(J):
            BQss_params = (omega[1, :], lambdas[j], rho, g_n_vector[1], 'SS')
            BQss2[j] = household.get_BQ(rnew[1], bmat_splus1[1, :, j],
                                        BQss_params)
        # print 'BQ test = ', BQss2-BQss, BQss-BQnew[1], BQss-BQnew[100], BQss-BQnew[-1]

        total_tax_params = np.zeros((T, S, J, etr_params.shape[2]))
        for i in range(etr_params.shape[2]):
            total_tax_params[:, :, :, i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))

        tax_receipt_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
                total_tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J)
        net_tax_receipts = np.array(list(tax.get_lump_sum(np.tile(rnew[:T].reshape(T, 1, 1),(1,S,J)), np.tile(wnew[:T].reshape(T, 1, 1),(1,S,J)),
               bmat_s, n_mat[:T,:,:], BQnew[:T].reshape(T, 1, J), factor, tax_receipt_params)) + [T_Hss] * S)

        r[:T] = utils.convex_combo(rnew[:T], r[:T], nu)
        BQ[:T] = utils.convex_combo(BQnew[:T], BQ[:T], nu)
        if fix_transfers:
            T_H_new = T_H
            G[:T] = net_tax_receipts[:T] - T_H[:T]
        else:
            T_H_new = net_tax_receipts
            T_H[:T] = utils.convex_combo(T_H_new[:T], T_H[:T], nu)
            G[:T] = 0.0

        etr_params_path = np.zeros((T,S,J,etr_params.shape[2]))
        for i in range(etr_params.shape[2]):
            etr_params_path[:,:,:,i] = np.tile(
                np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
        tax_path_params = (np.tile(e.reshape(1, S, J),(T,1,1)),
                           lambdas, 'TPI', retire, etr_params_path, h_wealth,
                           p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
        b_to_use = np.zeros((T, S, J))
        b_to_use[0, 1:, :] = initial_b[:-1, :]
        b_to_use[1:, 1:, :] = b_mat[:T-1, :-1, :]
        tax_path = tax.total_taxes(
            np.tile(r[:T].reshape(T, 1, 1),(1,S,J)),
            np.tile(w[:T].reshape(T, 1, 1),(1,S,J)), b_to_use,
            n_mat[:T,:,:], BQ[:T, :].reshape(T, 1, J), factor,
            T_H[:T].reshape(T, 1, 1), None, False, tax_path_params)

        y_path = (np.tile(r[:T].reshape(T, 1, 1), (1, S, J)) * b_to_use[:T, :, :] +
                  np.tile(w[:T].reshape(T, 1, 1), (1, S, J)) *
                  np.tile(e.reshape(1, S, J), (T, 1, 1)) * n_mat[:T, :, :])
        cons_params = (e.reshape(1, S, J), lambdas.reshape(1, 1, J), g_y)
        c_path = household.get_cons(r[:T].reshape(T, 1, 1), w[:T].reshape(T, 1, 1), b_to_use[:T,:,:], b_mat[:T,:,:], n_mat[:T,:,:],
                       BQ[:T].reshape(T, 1, J), tax_path, cons_params)


        guesses_b = utils.convex_combo(b_mat, guesses_b, nu)
        guesses_n = utils.convex_combo(n_mat, guesses_n, nu)
        if T_H.all() != 0:
            TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) +
                               list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) +
                               list(utils.pct_diff_func(T_H_new[:T], T_H[:T]))).max()
            print 'r dist = ', np.array(list(utils.pct_diff_func(rnew[:T], r[:T]))).max()
            print 'BQ dist = ', np.array(list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten())).max()
            print 'T_H dist = ', np.array(list(utils.pct_diff_func(T_H_new[:T], T_H[:T]))).max()
            print 'T_H path = ', T_H[:20]
            # print 'r old = ', r[:T]
            # print 'r new = ', rnew[:T]
            # print 'K old = ', K[:T]
            # print 'L old = ', L[:T]
            # print 'income = ', y_path[:, :, -1]
            # print 'taxes = ', tax_path[:, :, -1]
            # print 'labor supply = ', n_mat[:, :, -1]
            # print 'max and min labor = ', n_mat.max(), n_mat.min()
            # print 'max and min labor = ', np.argmax(n_mat), np.argmin(n_mat)
            # print 'max and min labor, j = 7 = ', n_mat[:,:,-1].max(), n_mat[:,:,-1].min()
            # print 'max and min labor, j = 6 = ', n_mat[:,:,-2].max(), n_mat[:,:,-2].min()
            # print 'max and min labor, j = 5 = ', n_mat[:,:,4].max(), n_mat[:,:,4].min()
            # print 'max and min labor, j = 4 = ', n_mat[:,:,3].max(), n_mat[:,:,3].min()
            # print 'max and min labor, j = 3 = ', n_mat[:,:,2].max(), n_mat[:,:,2].min()
            # print 'max and min labor, j = 2 = ', n_mat[:,:,1].max(), n_mat[:,:,1].min()
            # print 'max and min labor, j = 1 = ', n_mat[:,:,0].max(), n_mat[:,:,0].min()
            # print 'max and min labor, S = 80 = ', n_mat[:,-1,-1].max(), n_mat[:,-1,-1].min()
            # print "number  > 1 = ", (n_mat > 1).sum()
            # print "number  < 0, = ", (n_mat < 0).sum()
            # print "number  > 1, j=7 = ", (n_mat[:T,:,-1] > 1).sum()
            # print "number  < 0, j=7 = ", (n_mat[:T,:,-1] < 0).sum()
            # print "number  > 1, s=80, j=7 = ", (n_mat[:T,-1,-1] > 1).sum()
            # print "number  < 0, s=80, j=7 = ", (n_mat[:T,-1,-1] < 0).sum()
            # print "number  > 1, j= 7, age 80= ", (n_mat[:T,-1,-1] > 1).sum()
            # print "number  < 0, j = 7, age 80= ", (n_mat[:T,-1,-1] < 0).sum()
            # print "number  > 1, j= 7, age 80, period 0 to 10= ", (n_mat[:30,-1,-1] > 1).sum()
            # print "number  < 0, j = 7, age 80, period 0 to 10= ", (n_mat[:30,-1,-1] < 0).sum()
            # print "number  > 1, j= 7, age 70-79, period 0 to 10= ", (n_mat[:30,70:80,-1] > 1).sum()
            # print "number  < 0, j = 7, age 70-79, period 0 to 10= ", (n_mat[:30,70:80   ,-1] < 0).sum()
            # diag_dict = {'n_mat': n_mat, 'b_mat': b_mat, 'y_path': y_path, 'c_path': c_path}
            # pickle.dump(diag_dict, open('tpi_iter1.pkl', 'wb'))

        else:
            TPIdist = np.array(list(utils.pct_diff_func(rnew[:T], r[:T])) +
                               list(utils.pct_diff_func(BQnew[:T], BQ[:T]).flatten()) +
                               list(np.abs(T_H_new[:T]-T_H[:T]))).max()
        TPIdist_vec[TPIiter] = TPIdist
        # After T=10, if cycling occurs, drop the value of nu
        # wait til after T=10 or so, because sometimes there is a jump up
        # in the first couple iterations
        # if TPIiter > 10:
        #     if TPIdist_vec[TPIiter] - TPIdist_vec[TPIiter - 1] > 0:
        #         nu /= 2
        #         print 'New Value of nu:', nu
        TPIiter += 1
        print '\tIteration:', TPIiter
        print '\t\tDistance:', TPIdist

    Y[:T] = Ynew


    # Solve HH problem in inner loop
    guesses = (guesses_b, guesses_n)
    outer_loop_vars = (r, w, BQ, T_H)
    inner_loop_params = (income_tax_params, tpi_params, initial_values, ind)
    euler_errors, b_mat, n_mat = inner_loop(guesses, outer_loop_vars, inner_loop_params)

    bmat_s = np.zeros((T, S, J))
    bmat_s[0, 1:, :] = initial_b[:-1, :]
    bmat_s[1:, 1:, :] = b_mat[:T-1, :-1, :]
    bmat_splus1 = np.zeros((T, S, J))
    bmat_splus1[:, :, :] = b_mat[:T, :, :]

    K[0] = K0
    K_params = (omega[:T-1].reshape(T-1, S, 1), lambdas.reshape(1, 1, J), imm_rates[:T-1].reshape(T-1,S,1), g_n_vector[1:T], 'TPI')
    K[1:T] = household.get_K(bmat_splus1[:T-1], K_params)
    L_params = (e.reshape(1, S, J), omega[:T, :].reshape(T, S, 1), lambdas.reshape(1, 1, J), 'TPI')
    L[:T]  = firm.get_L(n_mat[:T], L_params)

    Y_params = (alpha, Z)
    Ynew = firm.get_Y(K[:T], L[:T], Y_params)
    r_params = (alpha, delta)
    rnew = firm.get_r(Ynew[:T], K[:T], r_params)
    wnew = firm.get_w_from_r(rnew, w_params)

    omega_shift = np.append(omega_S_preTP.reshape(1,S),omega[:T-1,:],axis=0)
    BQ_params = (omega_shift.reshape(T, S, 1), lambdas.reshape(1, 1, J), rho.reshape(1, S, 1),
                 g_n_vector[:T].reshape(T, 1), 'TPI')
    b_mat_shift = np.append(np.reshape(initial_b,(1,S,J)),b_mat[:T-1,:,:],axis=0)
    BQnew = household.get_BQ(rnew[:T].reshape(T, 1), b_mat_shift, BQ_params)

    total_tax_params = np.zeros((T,S,J,etr_params.shape[2]))
    for i in range(etr_params.shape[2]):
        total_tax_params[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))

    tax_receipt_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas.reshape(1, 1, J), omega[:T].reshape(T, S, 1), 'TPI',
            total_tax_params, theta, tau_bq, tau_payroll, h_wealth, p_wealth, m_wealth, retire, T, S, J)
    net_tax_receipts = np.array(list(tax.get_lump_sum(np.tile(rnew[:T].reshape(T, 1, 1),(1,S,J)), np.tile(wnew[:T].reshape(T, 1, 1),(1,S,J)),
           bmat_s, n_mat[:T,:,:], BQnew[:T].reshape(T, 1, J), factor, tax_receipt_params)) + [T_Hss] * S)

    if fix_transfers:
        G[:T] = net_tax_receipts[:T] - T_H[:T]
    else:
        T_H[:T] = net_tax_receipts[:T]
        G[:T] = 0.0

    etr_params_path = np.zeros((T,S,J,etr_params.shape[2]))
    for i in range(etr_params.shape[2]):
        etr_params_path[:,:,:,i] = np.tile(np.reshape(np.transpose(etr_params[:,:T,i]),(T,S,1)),(1,1,J))
    tax_path_params = (np.tile(e.reshape(1, S, J),(T,1,1)), lambdas, 'TPI', retire, etr_params_path, h_wealth,
                       p_wealth, m_wealth, tau_payroll, theta, tau_bq, J, S)
    tax_path = tax.total_taxes(np.tile(r[:T].reshape(T, 1, 1),(1,S,J)), np.tile(w[:T].reshape(T, 1, 1),(1,S,J)), bmat_s,
                               n_mat[:T,:,:], BQ[:T, :].reshape(T, 1, J), factor, T_H[:T].reshape(T, 1, 1), None, False, tax_path_params)

    cons_params = (e.reshape(1, S, J), lambdas.reshape(1, 1, J), g_y)
    c_path = household.get_cons(r[:T].reshape(T, 1, 1), w[:T].reshape(T, 1, 1), bmat_s, bmat_splus1, n_mat[:T,:,:],
                   BQ[:T].reshape(T, 1, J), tax_path, cons_params)
    C_params = (omega[:T].reshape(T, S, 1), lambdas, 'TPI')
    C = household.get_C(c_path, C_params)
    I_params = (delta, g_y, omega[:T].reshape(T, S, 1), lambdas, imm_rates[:T].reshape(T, S, 1), g_n_vector[1:T+1], 'TPI')
    I = firm.get_I(bmat_splus1[:T], K[1:T+1], K[:T], I_params)
    rc_error = Y[:T] - C[:T] - I[:T] - G[:T]
    print 'Resource Constraint Difference:', rc_error

    # compute utility
    u_params = (sigma, np.tile(chi_n.reshape(1, S, 1), (T, 1, J)),
                b_ellipse, ltilde, upsilon,
                np.tile(rho.reshape(1, S, 1), (T, 1, J)),
                np.tile(chi_b.reshape(1, 1, J), (T, S, 1)))
    utility_path = household.get_u(c_path[:T, :, :], n_mat[:T, :, :],
                                   bmat_splus1[:T, :, :], u_params)

    # compute before and after-tax income
    y_path = (np.tile(r[:T].reshape(T, 1, 1), (1, S, J)) * bmat_s[:T, :, :] +
              np.tile(w[:T].reshape(T, 1, 1), (1, S, J)) *
              np.tile(e.reshape(1, S, J), (T, 1, 1)) * n_mat[:T, :, :])
    inctax_params = (np.tile(e.reshape(1, S, J), (T, 1, 1)), etr_params_path)
    y_aftertax_path = (y_path -
                       tax.tau_income(np.tile(r[:T].reshape(T, 1, 1), (1, S, J)),
                                      np.tile(w[:T].reshape(T, 1, 1), (1, S, J)),
                                      bmat_s[:T,:,:], n_mat[:T,:,:], factor, inctax_params))

    # compute after-tax wealth
    wtax_params = (h_wealth, p_wealth, m_wealth)
    b_aftertax_path = bmat_s[:T,:,:] - tax.tau_wealth(bmat_s[:T,:,:], wtax_params)

    print'Checking time path for violations of constaints.'
    for t in xrange(T):
        household.constraint_checker_TPI(
            b_mat[t], n_mat[t], c_path[t], t, ltilde)

    eul_savings = euler_errors[:, :S, :].max(1).max(1)
    eul_laborleisure = euler_errors[:, S:, :].max(1).max(1)

    print 'Max Euler error, savings: ', eul_savings
    print 'Max Euler error labor supply: ', eul_laborleisure



    '''
    ------------------------------------------------------------------------
    Save variables/values so they can be used in other modules
    ------------------------------------------------------------------------
    '''

    output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I, 'BQ': BQ, 'G': G,
              'T_H': T_H, 'r': r, 'w': w, 'b_mat': b_mat, 'n_mat': n_mat,
              'c_path': c_path, 'tax_path': tax_path, 'bmat_s': bmat_s,
              'utility_path': utility_path, 'b_aftertax_path': b_aftertax_path,
              'y_aftertax_path': y_aftertax_path, 'y_path': y_path,
              'eul_savings': eul_savings, 'eul_laborleisure': eul_laborleisure}

    macro_output = {'Y': Y, 'K': K, 'L': L, 'C': C, 'I': I,
                    'BQ': BQ, 'G': G, 'T_H': T_H, 'r': r, 'w': w,
                    'tax_path': tax_path}


    # if ((TPIiter >= maxiter) or (np.absolute(TPIdist) > mindist_TPI)) and ENFORCE_SOLUTION_CHECKS :
    #     raise RuntimeError("Transition path equlibrium not found")
    #
    # if ((np.any(np.absolute(rc_error) >= 1e-6))
    #     and ENFORCE_SOLUTION_CHECKS):
    #     raise RuntimeError("Transition path equlibrium not found")
    #
    # if ((np.any(np.absolute(eul_savings) >= mindist_TPI) or
    #     (np.any(np.absolute(eul_laborleisure) > mindist_TPI)))
    #     and ENFORCE_SOLUTION_CHECKS):
    #     raise RuntimeError("Transition path equlibrium not found")

    return output, macro_output