def TheWholeSmack(S, I, sigma):
#Parameters Zone
#I = 10 #Number of countries
#S = 80 #Upper bound of age for agents
#I=rc.I
#S=rc.S
T = int(
round(2.5 * S)
) #Number of time periods to convergence, based on Rick Evans' function.
T_1 = S #This is like TransYear in the FORTRAN I think
if S > 50:
T_1 = 50
StartFertilityAge = int(S / 80. *
23) #The age when agents have their first children
EndFertilityAge = int(S / 80. *
45) #The age when agents have their last children
StartDyingAge = int(S / 80. * 68) #The first age agents can begin to die
MaxImmigrantAge = int(
S / 80. * 65) #All immigrants are between ages 0 and MaxImmigrantAge
g_A = 0.001 #Technical growth rate
beta_ann = .95 #Starting future consumption discount rate
delta_ann = .08 #Starting depreciation rate
beta = beta_ann**(70 / S) #Future consumption discount rate
#sigma = 2 #Utility curvature parameter
#sigma=rc.Sigma
delta = 1 - (1 - delta_ann)**(70 / S) #Depreciation Rate
alpha = .3 #Capital Share of production
e = np.ones((I, S, T + S + 1)) #Labor productivities
#A = np.ones(I) #Techonological Change, used for idential countries
A = np.linspace(
1, 2,
num=I) #Techonological Change, used for when countries are different
diff = 1e-6 #Convergence Tolerance
distance = 10 #Used in taking the norm, arbitrarily set to 10
xi = .99 #Parameter used to take the convex conjugate of paths
MaxIters = 3000 #Maximum number of iterations on TPI.
#Program Levers
PrintAges = False #Prints the different key ages in the demographics
PrintSS = False #Prints the result of the Steady State functions
CalcTPI = True #Activates the calculation of Time Path Iteration
#NOTE: Graphing only works if CalcTPI is activated.
Graphs = False #Activates graphing the graphs.
CountryNamesON = False #Turns on labels for the graphs. Replaces "Country x" with proper names.
#MAIN CODE
#Gets demographic data
demog_params = (I, S, T, T_1, StartFertilityAge, EndFertilityAge,
StartDyingAge, MaxImmigrantAge, g_A, PrintAges)
FertilityRates, MortalityRates, Migrants, N_matrix, Nhat_matrix = Stepfuncs.getDemographics(
demog_params)
#Stepfuncs.plotDemographics((S,T),0,[0,19],"USA", N_matrix)
#Initalizes initial guesses
assets_guess = np.ones((I, S - 1)) * .15
kf_guess = np.zeros((I))
#Gets the steady state variables
params_ss = (I, S, beta, sigma, delta, alpha, e, A)
assets_ss, kf_ss, kd_ss, n_ss, y_ss, r_ss, w_ss, c_vec_ss = Stepfuncs.getSteadyState(
params_ss, assets_guess, kf_guess)
if PrintSS == True: #Prints the results of the steady state, line 23 activates this
print "assets steady state", assets_ss
print "kf steady state", kf_ss
print "kd steady state", kd_ss
print "n steady state", n_ss
print "y steady state", y_ss
print "r steady state", r_ss
print "w steady state", w_ss
print "c_vec_ss steady state", c_vec_ss
if CalcTPI == True: #Time Path Iteration, activated by line 24
print "Beginning TPI"
initialguess_params = (I, S, T, delta, alpha, e, A)
assets_init, kf_init, w_initguess, r_initguess, kd_init, n_init, y_init, c_init = \
Stepfuncs.get_initialguesses(initialguess_params, assets_ss, kf_ss, w_ss, r_ss)
tp_params = (I, S, T, T_1, beta, sigma, delta, alpha, e, A,
StartFertilityAge, StartDyingAge, N_matrix,
MortalityRates, distance, diff, xi, MaxIters)
wpath, rpath, cpath, Kpath, ypath = Stepfuncs.get_Timepath(
tp_params, w_initguess, r_initguess, assets_init, kd_ss, kf_ss,
w_ss, r_ss)
#print "Cohorts:",S,"Countries:", I,"Curvature:", sigma
if Graphs == True:
Stepfuncs.plotTimepaths(I, S, T, wpath, rpath, cpath, Kpath, ypath,
CountryNamesON)
#Initalizes initial guesses assets_guess = np.ones((I, S-1))*.15 kf_guess = np.zeros((I)) #Gets the steady state variables params_ss = (I, S, beta, sigma, delta, alpha, e, A) assets_ss, kf_ss, kd_ss, n_ss, y_ss, r_ss, w_ss, c_vec_ss = Stepfuncs.getSteadyState(params_ss, assets_guess, kf_guess) if PrintSS==True: #Prints the results of the steady state, line 23 activates this print "assets steady state", assets_ss print "kf steady state", kf_ss print "kd steady state", kd_ss print "n steady state",n_ss print "y steady state", y_ss print "r steady state",r_ss print "w steady state", w_ss print "c_vec_ss steady state",c_vec_ss if CalcTPI==True: #Time Path Iteration, activated by line 24 print "Beginning TPI" initialguess_params = (I, S, T, delta, alpha, e, A) assets_init, kf_init, w_initguess, r_initguess, kd_init, n_init, y_init, c_init = \ Stepfuncs.get_initialguesses(initialguess_params, assets_ss, kf_ss, w_ss, r_ss) tp_params = (I, S, T, T_1, beta, sigma, delta, alpha, e, A, StartFertilityAge, StartDyingAge, N_matrix, MortalityRates, distance, diff, xi, MaxIters) wpath, rpath, cpath, Kpath, ypath = Stepfuncs.get_Timepath(tp_params, w_initguess, r_initguess, assets_init, kd_ss, kf_ss, w_ss, r_ss) if Graphs==True: Stepfuncs.plotTimepaths(I, S, T, wpath, rpath, cpath, Kpath, ypath, CountryNamesON)
def TheWholeSmack(S,I,sigma):
T = int(round(2.5*S)) #Number of time periods to convergence, based on Rick Evans' function.
T_1 = S #This is like TransYear in the FORTRAN I think
if S > 50:
T_1 = 50
StartFertilityAge = int(S/80.*23)#The age when agents have their first children
EndFertilityAge = int(S/80.*45)#The age when agents have their last children
StartDyingAge = int(S/80.*68)#The first age agents can begin to die
MaxImmigrantAge = int(S/80.*65)#All immigrants are between ages 0 and MaxImmigrantAge
g_A = 0.015#Technical growth rate
beta_ann=.95 #Starting future consumption discount rate
delta_ann=.08 #Starting depreciation rate
beta = beta_ann**(70/S) #Future consumption discount rate
#sigma = 1 #Utility curvature parameter
delta = 1-(1-delta_ann)**(70/S) #Depreciation Rate
alpha = .3 #Capital Share of production
e = np.ones((I, S, T+S+1)) #Labor productivities
A = np.ones(I) #Techonological Change, used for idential countries
#A=np.array([1,4,2,5,6]) #Techonological Change, used for when countries are different
diff=1e-8 #Convergence Tolerance
distance=10 #Used in taking the norm, arbitrarily set to 10
xi=.95 #Parameter used to take the convex conjugate of paths
MaxIters=1000 #Maximum number of iterations on TPI.
#Program Levers
PrintAges = False #Prints the different key ages in the demographics
PrintSS = False #Prints the result of the Steady State functions
CalcTPI = True #Activates the calculation of Time Path Iteration
#NOTE: Graphing only works if CalcTPI is activated.
Graphs = False #Activates graphing the graphs.
CountryNamesON = False #Turns on labels for the graphs. Replaces "Country x" with proper names.
DiffDemog = True #Turns on different demographics over countries.
#MAIN CODE
#Gets demographic data
demog_params = (I, S, T, T_1, StartFertilityAge, EndFertilityAge, StartDyingAge, MaxImmigrantAge, g_A)
FertilityRates, MortalityRates, Migrants, N_matrix, Nhat_matrix = Stepfuncs.getDemographics(demog_params, PrintAges, DiffDemog)
#Initalizes initial guesses
assets_guess = np.ones((I, S-1))*.15
kf_guess = np.zeros((I))
#Gets the steady state variables
params_ss = (I, S, beta, sigma, delta, alpha, e, A)
assets_ss, kf_ss, kd_ss, n_ss, y_ss, r_ss, w_ss, c_vec_ss = Stepfuncs.getSteadyState(params_ss, assets_guess, kf_guess)
if PrintSS==True: #Prints the results of the steady state, line 23 activates this
print "assets steady state", assets_ss
print "kf steady state", kf_ss
print "kd steady state", kd_ss
print "n steady state",n_ss
print "y steady state", y_ss
print "r steady state",r_ss
print "w steady state", w_ss
print "c_vec_ss steady state",c_vec_ss
if CalcTPI==True: #Time Path Iteration, activated by line 24
print "Beginning TPI"
initialguess_params = (I, S, T, delta, alpha, e, A)
assets_init, kf_init, w_initguess, r_initguess, kd_init, n_init, y_init, c_init = \
Stepfuncs.get_initialguesses(initialguess_params, assets_ss, kf_ss, w_ss, r_ss)
tp_params = (I, S, T, T_1, beta, sigma, delta, alpha, e, A, StartFertilityAge, StartDyingAge, N_matrix, MortalityRates, distance, diff, xi, MaxIters)
wpath, rpath, cpath, Kpath, ypath, apath = Stepfuncs.get_Timepath(tp_params, w_initguess, r_initguess, assets_init, kd_ss, kf_ss, w_ss, r_ss)
if Graphs==True:
Stepfuncs.plotTimepaths(I, S, T, wpath, rpath, cpath, Kpath, ypath, CountryNamesON)
print wpath.shape
print rpath.shape
print apath.shape
outfile = TemporaryFile()
apath=np.reshape(apath,(I*(S+1)*(T+S+1)))
np.savetxt("apath.csv",apath,delimiter=",")
np.savetxt("wpath.csv",wpath,delimiter=",")
np.savetxt("rpath.csv",rpath,delimiter=",")
def Multi_Country(S,I,sigma):
I_dict = {"usa":0,"eu":1,"japan":2,"china":3,"india":4,"russia":5,"korea":6}
#Parameters Zone
T = int(round(4*S)) #Number of time periods to convergence, based on Rick Evans' function.
I_touse = ["eu","russia","usa","japan","korea","china","india"]
T_1 = S #This is like TransYear in the FORTRAN I think
if S > 50:
T_1 = 50
g_A = 0.015 #Technical growth rate
beta_ann=.95 #Starting future consumption discount rate
delta_ann=.08 #Starting depreciation rate
beta = beta_ann**(70./S) #Future consumption discount rate
delta = 1-(1-delta_ann)**(70./S) #Depreciation Rate
alpha = .3 #Capital Share of production
chi = 1.5 #New Parameter
rho = 1.3 #Other New Parameter
tpi_tol = 1e-8 #Convergence Tolerance
demog_ss_tol = 1e-8 #Used in getting ss for population share
xi = .9999 #Parameter used to take the convex conjugate of paths
MaxIters = 10000 #Maximum number of iterations on TPI.
#Program Levers
CalcTPI = False #Activates the calculation of Time Path Iteration
PrintAges = False #Prints the different key ages in the demographics
PrintLoc = False #Displays the current locations of the program inside key TPI functions
PrintEulErrors = False #Prints the euler errors in each attempt of calculating the steady state
PrintSS = True #Prints the result of the Steady State functions
Print_cabqTimepaths = False #Prints the consumption, assets, and bequests timepath as it gets filled in for each iteration of TPI
CheckerMode = False #Reduces the number of prints when checking for robustness
DemogGraphs = False #Activates graphing graphs with demographic data and population shares
TPIGraphs = True #Activates graphing the graphs.
UseStaggeredAges = True #Activates using staggered ages
UseDiffDemog = True #Turns on different demographics for each country
UseSSDemog = False #Activates using only steady state demographics for TPI calculation
UseDiffProductivities = False #Activates having e vary across cohorts
UseTape = True #Activates setting any value of kd<0 to 0.001 in TPI calculation
SAVE = False #Saves the graphs
SHOW = True #Shows the graphs
ADJUSTKOREAIMMIGRATION = True
LeaveHouseAge, FirstFertilityAge, LastFertilityAge, MaxImmigrantAge, FirstDyingAge, agestopull = Stepfuncs.getkeyages(S, PrintAges, UseStaggeredAges)
if len(I_touse) < I:
print "WARNING: We are changing I from", I, "to", len(I_touse), "to fit the length of I_touse. So the countries we are using now are", I_touse
I = len(I_touse)
time.sleep(2)
elif len(I_touse) > I:
print "WARNING: We are changing I_touse from", I_touse, "to", I_touse[:I], "so there are", I, "regions"
I_touse = I_touse[:I]
time.sleep(2)
if UseDiffDemog:
A = np.ones(I)+np.cumsum(np.ones(I)*.05)-.05 #Techonological Change, used for when countries are different
#A = np.ones(I)
else:
A = np.ones(I) #Techonological Change, used for idential countries
if UseDiffProductivities:
e = np.ones((I, S, T))
e[:,FirstDyingAge:,:] = 0.3
e[:,:LeaveHouseAge,:] = 0.3
else:
e = np.ones((I, S, T)) #Labor productivities
#MAIN CODE
#Gets demographic data
demog_params = (I, S, T, T_1, LeaveHouseAge, FirstFertilityAge, LastFertilityAge, FirstDyingAge, MaxImmigrantAge, agestopull, g_A, demog_ss_tol)
demog_levers = PrintLoc, UseStaggeredAges, UseDiffDemog, DemogGraphs, CheckerMode
MortalityRates, Nhat_matrix, Nhat_ss, lbar = Stepfuncs.getDemographics(demog_params, demog_levers, I_dict, I_touse, ADJUSTKOREAIMMIGRATION)
#Initalizes initial guesses
assets_guess = np.ones((I, S-1))*.1
kf_guess = np.zeros((I))
#Gets the steady state variables
params_ss = (I, S, beta, sigma, delta, alpha, chi, rho, e[:,:,-1], A,\
FirstFertilityAge, FirstDyingAge, Nhat_ss, MortalityRates[:,:,-1],\
g_A, lbar[-1], PrintEulErrors, CheckerMode)
#assets_ss, kf_ss, kd_ss, n_ss, y_ss, r_ss, w_ss, c_vec_ss = Stepfuncs.getSteadyState(params_ss, assets_guess, kf_guess)
#NEW CODE BEGINS HERE
r_ss_guess = .2
bq_ss_guess = np.ones(I)*.2
bq_ss, r_ss, w_ss, cvec_ss, avec_ss, kd_ss, kf_ss, n_ss, y_ss = Stepfuncs.getSteadyStateNEWEST(params_ss, bq_ss_guess, r_ss_guess, I_touse)
#NEW CODE ENDS HERE
if PrintSS==True: #Prints the results of the steady state, line 23 activates this
print "assets steady state", avec_ss
print "kf steady state", kf_ss
print "kd steady state", kd_ss
print "n steady state",n_ss
print "y steady state", y_ss
print "r steady state",r_ss
print "w steady state", w_ss
print "c_vec_ss steady state",cvec_ss
if UseSSDemog == True:
print "NOTE: USING SS DEMOGRAPHICS FOR TIMEPATH\n"
Nhat_matrix = np.einsum("is,t->ist", Nhat_matrix[:,:,-1],np.ones(T))
MortalityRates = np.einsum("is,t->ist", MortalityRates[:,:,-1],np.ones(T))
time.sleep(2)
if CalcTPI==True: #Time Path Iteration, activated by line 24
print "Beginning TPI..."
#Gets initial guesses for TPI
initialguess_params = (I, S, T, delta, alpha, e[:,:,0], lbar, A, FirstFertilityAge, FirstDyingAge, Nhat_matrix[:,:,0], MortalityRates[:,:,0], g_A)
assets_init, wpath_initguess, rpath_initguess = \
Stepfuncs.get_initialguesses(initialguess_params, assets_ss, kf_ss, w_ss, r_ss, PrintLoc)
#Gets timepaths for w, r, C, K, and Y
tp_params = (I, S, T, T_1, beta, sigma, delta, alpha, rho, chi, e, A, FirstFertilityAge, FirstDyingAge, Nhat_matrix, MortalityRates, g_A, lbar, tpi_tol, xi, MaxIters, CheckerMode)
wpath, rpath, Cpath, Kpath, Ypath = Stepfuncs.get_Timepath(tp_params, wpath_initguess, rpath_initguess, assets_init, kd_ss, kf_ss, PrintLoc, Print_cabqTimepaths, UseTape)
if TPIGraphs==True:
Stepfuncs.plotTimepaths(I, S, T, sigma, wpath, rpath, Cpath, Kpath, Ypath, I_touse, SAVE, SHOW, CheckerMode)