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 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)
#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) for i in range(I): Stepfuncs.plotDemographics((S,T),i,[0,24],str("Country "+str(i)), 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 = \