diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index dc481993e..a71bf81a6 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -782,9 +782,6 @@ def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac): ------- None ''' - if isinstance(IncomeDstn, list): - import pdb; pdb.set_trace() - self.DiscFacEff = DiscFac*LivPrb # "effective" discount factor self.ShkPrbsNext = IncomeDstn.pmf self.PermShkValsNext = IncomeDstn.X[0] @@ -1872,113 +1869,137 @@ def getPostStates(self): self.aLvlNow = self.aNrmNow*self.pLvlNow # Useful in some cases to precalculate asset level return None - def checkAIC(self, thorn,verbose,public_call): + def checkCondition(self, + name, + test, + messages, + verbose_messages=None, + verbose=False): + """ + Checks one condition. + + Parameters + ---------- + name : string + Name for the condition. + + test : function(self -> boolean) + A function (of self) which tests the condition + + messages : dict{boolean : string} + A dictiomary with boolean keys containing values + for messages to print if the condition is + true or false. + + verbose_messages : dict{boolean : string} + (Optional) A dictiomary with boolean keys containing values + for messages to print if the condition is + true or false under verbose printing. + """ + self.conditions[name] = test(self) + + print(messages[self.conditions[name]].format(self)) + if verbose and verbose_messages: + print(verbose_messages[self.conditions[name]].format(self)) + + + def checkAIC(self, verbose = False): ''' Evaluate and report on the Absolute Impatience Condition ''' - AIF = thorn - - self.AIF = AIF - if AIF<1: - self.AIC = True - if public_call or verbose: - print('The value of the absolute impatience factor (AIF) for the supplied parameter values satisfies the Absolute Impatience Condition.', end = " ") - self.violated = False - if verbose: - print(' Because the AIF < 1, the absolute amount of consumption is expected to fall over time.') - print() - else: - self.AIC = False - print('The given type violates the Absolute Impatience Condition with the supplied parameter values; the AIF is %1.5f ' % (AIF), end=" ") - self.violated = True - if verbose: - print(' Because the AIF > 1, the absolute amount of consumption is expected to grow over time') - print() + name = "AIC" + test = lambda agent : agent.thorn < 1 + + messages = { + True: "The value of the absolute impatience factor (AIF) for the supplied parameter values satisfies the Absolute Impatience Condition.", + False: "The given type violates the Absolute Impatience Condition with the supplied parameter values; the AIF is {0.thorn}"} + verbose_messages = { + True : "Because the AIF < 1, the absolute amount of consumption is expected to fall over time.", + False : "Because the AIF > 1, the absolute amount of consumption is expected to grow over time" + } - def checkGICPF(self,thorn,verbose,public_call): + self.checkCondition(name, test, messages, verbose_messages, verbose = verbose) + + def checkGICPF(self,verbose = False): ''' Evaluate and report on the Growth Impatience Condition ''' - GIFPF = thorn/self.PermGroFac[0] - self.GIFPF = GIFPF + name = "GICPF" - if GIFPF<1: - self.GICPF = True - if public_call or verbose: - print('The value of the Growth Impatience Factor for the supplied parameter values satisfies the Perfect Foresight Growth Impatience Condition.', end = " ") - if verbose: - print(' Therefore, for a perfect foresight consumer, the ratio of individual wealth to permanent income will fall indefinitely.') - print() - else: - self.GICPF = False - self.violated = True - print('The given parameter values violate the Perfect Foresight Growth Impatience Condition for this consumer type; the GIFPF is: %2.4f' % (GIFPF), end = " ") - if verbose: - print(' Therefore, for a perfect foresight consumer the ratio of individual wealth to permanent income is expected to grow toward infinity.') - print() + self.GIFPF = self.thorn/self.PermGroFac[0] + + test = lambda agent : agent.GIFPF < 1 + + messages = { + True : 'The value of the Growth Impatience Factor for the supplied parameter values satisfies the Perfect Foresight Growth Impatience Condition.', + False : 'Therefore, for a perfect foresight consumer, the ratio of individual wealth to permanent income will fall indefinitely.' + } - def checkRIC(self, thorn,verbose,public_call): + verbose_messages = { + True : 'The given parameter values violate the Perfect Foresight Growth Impatience Condition for this consumer type; the GIFPF is: {0.GIFPF}', + False : ' Therefore, for a perfect foresight consumer the ratio of individual wealth to permanent income is expected to grow toward infinity.' + } + + self.checkCondition(name, test, messages, verbose_messages, verbose = verbose) + + def checkRIC(self, verbose = False): ''' Evaluate and report on the Return Impatience Condition ''' - RIF = thorn/self.Rfree - self.RIF = RIF - if RIF<1: - self.RIC = True - if public_call or verbose: - print('The return impatience factor value for the supplied parameter values satisfies the Return Impatience Condition.', end = " ") - if verbose: - print('Therefore, the limiting consumption function is not c(m)=0 for all m') - print() - else: - self.RIC = False - self.violated = True - print('The given type violates the Return Impatience Condition with the supplied parameter values; the factor is %1.5f ' % (RIF), end = " ") - if verbose: - print('Therefore, the limiting consumption function is c(m)=0 for all m') - print() + self.RIF = self.thorn/self.Rfree + + name = "RIC" + test = lambda agent: self.RIF < 1 + + messages = { + True : 'The return impatience factor value for the supplied parameter values satisfies the Return Impatience Condition.', + False : 'The given type violates the Return Impatience Condition with the supplied parameter values; the factor is {0.RIF}' + } - def checkFHWC(self,verbose,public_call): + verbose_messages = { + True : 'Therefore, the limiting consumption function is not c(m)=0 for all m', + False : 'Therefore, the limiting consumption function is c(m)=0 for all m' + } + self.checkCondition(name, test, messages, verbose = verbose) + + def checkFHWC(self,verbose = False): ''' Evaluate and report on the Finite Human Wealth Condition ''' - FHWF = self.PermGroFac[0]/self.Rfree - self.FHWF = FHWF - if FHWF<1: - self.hNrm = 1.0/(1.0-self.PermGroFac[0]/self.Rfree) - self.FHWC = True - if public_call or verbose: - print('The Finite Human wealth factor value for the supplied parameter values satisfies the Finite Human Wealth Condition.', end = " ") - if verbose: - print('Therefore, the limiting consumption function is not c(m)=Infinity') - print('and human wealth normalized by permanent income is %2.5f' % (self.hNrm)) - self.cNrmPDV = 1.0/(1.0-self.Thorn/self.Rfree) - print('and the PDV of future consumption growth is %2.5f' % (self.cNrmPDV) ) - print() - else: - self.FHWC = False - print('The given type violates the Finite Human Wealth Condition; the Finite Human wealth factor value %2.5f ' % (FHWF), end = " ") - self.violated = True - if verbose: - print('Therefore, the limiting consumption function is c(m)=Infinity for all m') - print() - if verbose and self.violated and verbose_reference: - print('[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') - return self.violated + self.FHWF = self.PermGroFac[0]/self.Rfree + self.cNrmPDV = 1.0/(1.0-self.thorn/self.Rfree) + + name = "FHWC" + test = lambda agent: self.FHWF < 1 + + messages = { + True : 'The Finite Human wealth factor value for the supplied parameter values satisfies the Finite Human Wealth Condition.', + False : 'The given type violates the Finite Human Wealth Condition; the Finite Human wealth factor value {0.FHWF}', + } + verbose_messages = { + True : 'Therefore, the limiting consumption function is not c(m)=Infinity\nand human wealth normalized by permanent income is {0.hNrm}\nand the PDV of future consumption growth is {0.cNrmPDV}', + False : 'Therefore, the limiting consumption function is c(m)=Infinity for all m' + } - def checkConditions(self,verbose=False,verbose_reference=False,public_call=False): + self.checkCondition(name, test, messages, verbose = verbose) + + def checkConditions(self,verbose=False): ''' - This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC), - the Return Impatience Condition (RIC), the Finite Human Wealth Condition (FHWC) and the perfect foresight + This method checks whether the instance's type satisfies the + Absolute Impatience Condition (AIC), + the Return Impatience Condition (RIC), + the Finite Human Wealth Condition (FHWC) and the perfect foresight model's version of the Finite Value of the Growth Impatience Condition (GIC_PF) and Autarky Condition (FVAC_PF). Depending on the configuration of parameter values, some combination of these conditions must be satisfied in order for the problem to have a nondegenerate solution. To check which conditions are required, in the verbose mode a reference to the relevant theoretical literature is made. + + Parameters ---------- verbose : boolean @@ -1990,22 +2011,25 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False ------- None ''' + self.conditions = {} + + self.violated = False + # This method only checks for the conditions for infinite horizon models # with a 1 period cycle. If these conditions are not met, we exit early. if self.cycles!=0 or self.T_cycle > 1: return - self.violated = False - - Thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA) - self.Thorn = Thorn - - self.checkAIC(Thorn,verbose,public_call) - self.checkGICPF(Thorn,verbose,public_call) - self.checkRIC(Thorn,verbose,public_call) - self.checkFHWC(verbose,public_call) + self.thorn = (self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA) + self.checkAIC(verbose) + self.checkGICPF(verbose) + self.checkRIC(verbose) + self.checkFHWC(verbose) + self.violated = any([not self.conditions[c] + for c + in self.conditions]) # Make a dictionary to specify an idiosyncratic income shocks consumer init_idiosyncratic_shocks = dict(init_perfect_foresight, @@ -2031,6 +2055,7 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False 'vFuncBool': False, # Whether to calculate the value function during solution 'CubicBool': False, # Use cubic spline interpolation when True, linear interpolation when False }) + class IndShockConsumerType(PerfForesightConsumerType): ''' A consumer type with idiosyncratic shocks to permanent and transitory income. @@ -2050,7 +2075,7 @@ def __init__(self, **kwds): ''' Instantiate a new ConsumerType with given data. - See init_idiosyncratic_shocks for a dictionary of + See ConsumerParameters.init_idiosyncratic_shocks for a dictionary of the keywords that should be passed to the constructor. Parameters @@ -2315,68 +2340,70 @@ def preSolve(self): # self.updateIncomeProcess() self.updateSolutionTerminal() if not self.quiet: - self.checkConditions(verbose=self.verbose,public_call=False) + self.checkConditions(verbose=self.verbose) - def checkGICInd(self,Thorn,verbose,public_call): + def checkGICInd(self,verbose): ''' Check Individual Growth Impatience Factor. ''' - if self.GIFInd<=1: - self.GICInd = True - if public_call or verbose: - print('The value of the Individual Growth Impatience Factor for the supplied parameter values satisfies the Individual Growth Impatience Condition.', end = " ") - if verbose: - print('Therefore, a target level of the individual market resources ratio m exists (see '+self.url+'/#onetarget for more).') - print() - else: - self.GICInd = False - self.violated = True - print('The given parameter values violate the Individual Growth Impatience Condition; the GIFInd is: %2.4f' % (self.GIFInd), end = " ") - if verbose: - print('') - print('Therefore, a target ratio of individual market resources to individual permanent income does not exist. (see '+self.url+'/#onetarget for more).') - print() - - def checkCIGAgg(self, Thorn,verbose,public_call): - - if self.GIFAgg<=1: - self.GICAgg = True - if public_call or verbose: - print('The value of the Aggregate Growth Impatience Factor for the supplied parameter values satisfies the Aggregate Growth Impatience Condition.', end = " ") - if verbose: - print('Therefore, it is possible that a target level of the ratio of aggregate market resources to aggregate permanent income exists.') # Need to provide reference - print() - else: - self.GICAgg = False - self.violated = True - print('The given parameter values violate the Aggregate Growth Impatience Condition; the GIFAgg is: %2.4f' % (self.GIFAgg), end = " ") - if verbose: - print('') - print('Therefore, a target ratio of aggregate resources to aggregate permanent income does not exist.') # Need to provide reference - print() + self.GIFInd = self.thorn/(self.PermGroFac[0]*self.InvEPermShkInv) # [url]/#GIFI + + name = 'GIC' + test = lambda agent: agent.GIFInd <=1 + + + messages = { + True : 'The value of the Individual Growth Impatience Factor for the supplied parameter values satisfies the Individual Growth Impatience Condition.', + False : 'Therefore, a target level of the individual market resources ratio m exists (see {0.url}/#onetarget for more).' + } + + verbose_messages = { + True : 'The given parameter values violate the Individual Growth Impatience Condition; the GIFInd is: {0.GIFInd}', + False : 'Therefore, a target ratio of individual market resources to individual permanent income does not exist. (see {0.url}/#onetarget for more).' + } + + self.checkCondition(name,test,messages,verbose_messages, verbose=verbose) - def checkWRIC(self, verbose,public_call): + def checkCIGAgg(self, verbose): + name = 'GICAgg' + test = lambda agent : agent.GIFAgg <= 1 + + + messages = { + True : 'The value of the Aggregate Growth Impatience Factor for the supplied parameter values satisfies the Aggregate Growth Impatience Condition.', + False : 'The given parameter values violate the Aggregate Growth Impatience Condition; the GIFAgg is: {0.GIFAgg}' + } + + verbose_messages = { + True : 'Therefore, it is possible that a target level of the ratio of aggregate market resources to aggregate permanent income exists.', + False : 'Therefore, a target ratio of aggregate resources to aggregate permanent income does not exist.' + } + + self.checkCondition(name,test,messages,verbose_messages, verbose=verbose) + + def checkWRIC(self, verbose): ''' Evaluate and report on the Weak Return Impatience Condition [url]/#WRIF modified to incorporate LivPrb ''' - WRIF=(self.UnempPrb**(1/self.CRRA))*(self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA)/self.Rfree - self.WRIF = WRIF - if WRIF<=1: - self.WRIC = True - if public_call or verbose: - print('The Weak Return Impatience Factor value for the supplied parameter values satisfies the Weak Return Impatience Condition (see '+self.url+'/#WRIC for more).') - print() - else: - self.WRIC = False - self.violated = True - print('The given type violates the Weak Return Impatience Condition with the supplied parameter values. The WRIF is: %2.4f' % (WRIF), end = " ") - if verbose: - print('') - print('Therefore, a nondegenerate solution is not available (see '+self.url+'/#WRIC for more.') - print() + self.WRIF=(self.UnempPrb**(1/self.CRRA))*(self.Rfree*self.DiscFac*self.LivPrb[0])**(1/self.CRRA)/self.Rfree + + name = 'WRIC' + test = lambda agent: agent.WRIF <= 1 + + messages = { + True : "", + False : 'The Weak Return Impatience Factor value for the supplied parameter values satisfies the Weak Return Impatience Condition (see {0.url}/#WRIC for more).' + } + + verbose_messages = { + True : 'The given type violates the Weak Return Impatience Condition with the supplied parameter values. The WRIF is: {0.url}', + False : 'Therefore, a nondegenerate solution is not available (see {0.url}/#WRIC for more.' + } - def checkFVAC(self,verbose,public_call): + self.checkCondition(name,test,messages,verbose_messages, verbose=verbose) + + def checkFVAC(self,verbose): ''' Evaluate and report on the Finite Value of Autarky Condition Hyperlink to paper: [url]/#Autarky-Value @@ -2389,24 +2416,30 @@ def checkFVAC(self,verbose,public_call): uInvEpShkuInv = 1.0 self.uInvEpShkuInv = uInvEpShkuInv - FVAF=self.LivPrb[0]*self.DiscFac*self.uInvEpShkuInv - self.FVAF = FVAF - if FVAF<=1: - self.FVAC = True - if public_call or verbose: - print('The Finite Value of Autarky Factor (FVAV) for the supplied parameter values satisfies the Finite Value of Autarky Condition.') - if self.WRIC: - print('Since both WRIC and FVAC are satisfied, the problem has a nondegenerate solution') - else: - self.FVAC = False - print('The given type violates the Finite Value of Autarky Condition with the supplied parameter values. The FVAF is %2.4f' %(FVAF), end = " ") - self.violated = True - if public_call or verbose: - print('Therefore, a nondegenerate solution is not available (see '+self.url+'/#Conditions-Under-Which-the-Problem-Defines-a-Contraction-Mapping') - print() + self.FVAF=self.LivPrb[0]*self.DiscFac*self.uInvEpShkuInv + + name = 'FVAC' + test = lambda agent: agent.FVAF <= 1 + + messages = { + True : '', + False : 'The Finite Value of Autarky Factor (FVAV) for the supplied parameter values satisfies the Finite Value of Autarky Condition.' + } + + verbose_messages = { + True : 'The given type violates the Finite Value of Autarky Condition with the supplied parameter values. The FVAF is {0.FVAF}', + False : 'Therefore, a nondegenerate solution is not available (see {0.url}/#Conditions-Under-Which-the-Problem-Defines-a-Contraction-Mapping' + } - def checkConditions(self,verbose=False,public_call=True): + # Ok, I couldn't figure out how to work this case in ...- SB + #if self.WRIC: + # print('Since both WRIC and FVAC are satisfied, the problem has a nondegenerate solution') + + self.checkCondition(name, test, messages, verbose_messages, verbose=verbose) + + + def checkConditions(self,verbose=False): ''' This method checks whether the instance's type satisfies the Absolute Impatience Condition (AIC), Weak Return Impatience Condition (WRIC), Finite Human Wealth Condition (FHWC) and Finite Value of @@ -2425,6 +2458,8 @@ def checkConditions(self,verbose=False,public_call=True): ------- None ''' + self.conditions = {} + self.violated = False # PerfForesightConsumerType.checkConditions(self, verbose=False, verbose_reference=False) if self.cycles!=0 or self.T_cycle > 1: @@ -2438,47 +2473,46 @@ def checkConditions(self,verbose=False,public_call=True): # [url]/#Uncertainty-Modified-Conditions self.InvPermShkDstn=deepcopy(self.PermShkDstn) - self.InvPermShkDstn[0].X = 1/self.PermShkDstn[0].X - EPermShkInv=np.dot(self.InvPermShkDstn[0].pmf, - 1/self.PermShkDstn[0].X) # $\Ex_{t}[\psi^{-1}_{t+1}]$ (in first eqn in sec) - InvEPermShkInv=(1/EPermShkInv) # $\underline{\psi}$ in the paper (\bar{\isp} in private version) - PermGroFacAdj=self.PermGroFac[0]*InvEPermShkInv # [url]/#PGroAdj + self.InvPermShkDstn[0].X = 1/self.PermShkDstn[0].X + self.EPermShkInv=np.dot(self.InvPermShkDstn[0].pmf, + 1/self.PermShkDstn[0].X) # $\Ex_{t}[\psi^{-1}_{t+1}]$ (in first eqn in sec) # [url]/#Pat, adjusted to include mortality -# Thorn = ((self.Rfree/self.LivPrb[0])*(self.DiscFac*self.LivPrb[0]))**(1/self.CRRA) - Thorn = ((self.Rfree*self.DiscFac))**(1/self.CRRA) - GIFPF = Thorn/(self.PermGroFac[0] ) # [url]/#GIF - GIFInd = Thorn/(self.PermGroFac[0]*InvEPermShkInv) # [url]/#GIFI - GIFAgg = Thorn*self.LivPrb[0]/self.PermGroFac[0] # Lower bound of aggregate wealth growth if all inheritances squandered - -# self.Rnorm = self.Rfree*EPermShkInv/(self.PermGroFac[0]*self.LivPrb[0]) - self.GIFPF = GIFPF - self.GIFInd = GIFInd - self.GIFAgg = GIFAgg - self.Thorn = Thorn - self.PermGroFacAdj = PermGroFacAdj - self.EPermShkInv = EPermShkInv - self.InvEPermShkInv = InvEPermShkInv - self.DiscFacGIFPFMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge - self.DiscFacGIFIndMax = ((self.PermGroFac[0]*InvEPermShkInv)**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge - self.DiscFacGIFAggMax = ((self.PermGroFac[0] )**(self.CRRA))/(self.Rfree*self.LivPrb[0]) # DiscFac at growth impatience knife edge - - self.checkGICPF(Thorn,verbose,public_call) - self.checkGICInd(Thorn,verbose,public_call) - self.checkCIGAgg(Thorn,verbose,public_call) - self.checkWRIC(verbose,public_call) - self.checkFVAC(verbose, public_call) + + self.InvEPermShkInv = (1/self.EPermShkInv) # $\underline{\psi}$ in the paper (\bar{\isp} in private version) + self.PermGroFacAdj = self.PermGroFac[0]*self.InvEPermShkInv # [url]/#PGroAdj + + self.thorn = ((self.Rfree*self.DiscFac))**(1/self.CRRA) + + # self.Rnorm = self.Rfree*EPermShkInv/(self.PermGroFac[0]*self.LivPrb[0]) + self.GIFPF = self.thorn/(self.PermGroFac[0]) # [url]/#GIF + # Lower bound of aggregate wealth growth if all inheritances squandered + self.GIFAgg = self.thorn*self.LivPrb[0]/self.PermGroFac[0] + + self.DiscFacGIFPFMax = ((self.PermGroFac[0])**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge + self.DiscFacGIFIndMax = ((self.PermGroFac[0]*self.InvEPermShkInv)**(self.CRRA))/(self.Rfree) # DiscFac at growth impatience knife edge + self.DiscFacGIFAggMax = ((self.PermGroFac[0])**(self.CRRA))/(self.Rfree*self.LivPrb[0]) # DiscFac at growth impatience knife edge + + self.checkGICPF(verbose) + self.checkGICInd(verbose) + self.checkCIGAgg(verbose) + self.checkWRIC(verbose) + self.checkFVAC(verbose) + + self.violated = any([not self.conditions[c] + for c + in self.conditions]) if verbose and self.violated: print('\n[!] For more information on the conditions, see Tables 3 and 4 in "Theoretical Foundations of Buffer Stock Saving" at '+self.url+'/#Factors-Defined-And-Compared') print('') if verbose: - print('GIFPF = %2.6f ' % (GIFPF)) - print('GIFInd = %2.6f ' % (GIFInd)) - print('GIFAgg = %2.6f ' % (GIFAgg)) - print('Thorn = AIF = %2.6f ' % (Thorn)) - print('PermGroFacAdj = %2.6f ' % (PermGroFacAdj)) + print('GIFPF = %2.6f ' % (self.GIFPF)) + print('GIFInd = %2.6f ' % (self.GIFInd)) + print('GIFAgg = %2.6f ' % (self.GIFAgg)) + print('Thorn = AIF = %2.6f ' % (self.thorn)) + print('PermGroFacAdj = %2.6f ' % (self.PermGroFacAdj)) print('uInvEpShkuInv = %2.6f ' % (self.uInvEpShkuInv)) print('FVAF = %2.6f ' % (self.FVAF)) print('WRIF = %2.6f ' % (self.WRIF)) diff --git a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py index 75e6c777f..db269791b 100644 --- a/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py +++ b/HARK/ConsumptionSaving/tests/test_IndShockConsumerType.py @@ -172,7 +172,7 @@ def test_GICFails(self): self.assertAlmostEqual(c_m[500], 0.7772637042393458) self.assertAlmostEqual(c_m[700], 0.8392649061916746) - self.assertFalse(GICFailExample.GICPF) + self.assertFalse(GICFailExample.conditions['GIC']) def test_infinite_horizon(self): baseEx_inf = IndShockConsumerType(cycles=0, diff --git a/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py b/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py index 2944646b3..b8ac581b7 100644 --- a/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py +++ b/HARK/ConsumptionSaving/tests/test_PerfForesightConsumerType.py @@ -40,11 +40,10 @@ def test_another_solution(self): def test_checkConditions(self): self.agent_infinite.checkConditions() - - self.assertTrue(self.agent_infinite.AIC) - self.assertTrue(self.agent_infinite.GICPF) - self.assertTrue(self.agent_infinite.RIC) - self.assertTrue(self.agent_infinite.FHWC) + self.assertTrue(self.agent_infinite.conditions['AIC']) + self.assertTrue(self.agent_infinite.conditions['GICPF']) + self.assertTrue(self.agent_infinite.conditions['RIC']) + self.assertTrue(self.agent_infinite.conditions['FHWC']) def test_simulation(self): diff --git a/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb b/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb index a8a2c798e..36a33f17f 100644 --- a/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb +++ b/examples/ConsIndShockModel/PerfForesightConsumerType.ipynb @@ -179,7 +179,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[]\n" + "[]\n" ] } ], @@ -203,7 +203,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'cFunc': , 'vFunc': , 'vPfunc': , 'vPPfunc': , 'mNrmMin': -50.49994992551661, 'hNrm': 50.49994992551661, 'MPCmin': 0.04428139169919579, 'MPCmax': 0.04428139169919579}\n" + "{'cFunc': , 'vFunc': , 'vPfunc': , 'vPPfunc': , 'mNrmMin': -50.49994992551661, 'hNrm': 50.49994992551661, 'MPCmin': 0.04428139169919579, 'MPCmax': 0.04428139169919579, 'mNrmSS': -50.49991081984768}\n" ] } ], @@ -232,7 +232,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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fhd59tBG4Dfiiuw9krXoCuM7MasxsFdAKvALsAFqDO42qSV+MfiIIk58A1wbb3wg8Pt16uvrSdx4tbqjJ8x2JiFS2SWcKk/hboAZ4Nn2tmJfd/U/dfZeZPQq8Tfq00i3ungQws1uB7UAUeMDddwX7ug3YZmZ/DbwGfH+6xRzuHQYUCiIi+SooFNz97FOsuwe4J0f7U8BTOdr3kL47KW/dfXEAFs9VKIiI5CNUn2ju0kxBRKQgoQuFqqgxv66q1KWIiJSlUIVCd98wi+bWEFzfEBGRaQpVKHT1DuvUkYhIAcIXCrrILCKSt1CFwujpIxERyU9oQiGVco70x3X6SESkAKEJhRNDIyRTTtOcvH6GQURECFEo9AyMANBUr9tRRUTyFZ5QGEyHQqNCQUQkb+EJhYH0V1zMr9PpIxGRfIUmFI5rpiAiUrDQhMLoNYVGfcWFiEjeQhcK+t4jEZH8hScUBuM01MSIRUPzlkREZlxoRtDjAyPM1/UEEZGChCcUBkd0kVlEpEChCYWewREadTuqiEhBwhMKA3FdZBYRKVBoQuH4oK4piIgUKhSh4O70DIzoMwoiIgUKRSgMxJMkUs48hYKISEFCEQr9wwkA5tbESlyJiEh5C0Uo9CkURESKIhSh0D+cBGCOQkFEpCChCIXe4fT3Hs2piZa4EhGR8haKUBidKej0kYhIYUISCulrCjp9JCJSmFCEgi40i4gURyhCQbekiogUR2hCwQzqq3WhWUSkEAWFgpndbWZvmtnrZvZjM1setJuZfdvMOoL1n8na5kYzez943JjVfqGZvRVs820zs6nW0TecZE51jGlsIiIiORQ6U/imu1/g7uuAJ4G/DNo3Aa3BYwtwP4CZLQDuAi4G1gN3mVlTsM39Qd/R7TZOtYj+4YRuRxURKYKCQsHdT2S9nAN4sLwZeNjTXgYazWwZsAF41t2Puvsx4FlgY7Bunru/5O4OPAxcNdU6+uIJ3XkkIlIEBY+kZnYPcANwHPi9oHkFsD+rW2fQdqr2zhztE/3NLaRnFZx55pn0Dyd0kVlEpAgmnSmY2XNmtjPHYzOAu9/p7iuBR4BbRzfLsSvPoz0nd9/q7m3u3rZ48eL06aNqhYKISKEmHUnd/fIp7usfgH8mfc2gE1iZta4ZOBi0f3Zc+0+D9uYc/aekbzjJikb9FKeISKEKvfuoNevlF4F3g+UngBuCu5AuAY67+8fAduAKM2sKLjBfAWwP1vWa2SXBXUc3AI9PtY6+4RHm6kKziEjBCj3n8jUz+ySQAj4E/jRofwq4EugABoCbANz9qJndDewI+v2Vux8Nlv8MeBCoA54OHlPSP5zUhWYRkSIoaCR19387QbsDt0yw7gHggRzt7cB5+dTRpwvNIiJFUfafaHYgnkhppiAiUgRlHwqpVPomJX3FhYhI4co+FDy4cbW2SqEgIlKosg+FVJAKdQoFEZGChScUdPpIRKRgIQiF9HNtVdm/FRGRkiv7kdSDmYKuKYiIFK7sQ2F0pqBrCiIihQtBKGimICJSLGUfCq67j0REiqbsQyFz+kh3H4mIFCwEoRCcPoopFEREChWCUEg/11aX/VsRESm5sh9J3Z2IQXW07N+KiEjJlf1ImvL0nUfp3+YREZFChCAUXHceiYgUSdmHgrs+oyAiUixlHwopd33vkYhIkZT9aJoOBc0URESKoexDQaePRESKJwSh4LodVUSkSMp+NE05VMfK/m2IiMwKZT+aukJBRKRoyn40dVyhICJSJGU/mqYcahQKIiJFUfajqbsrFEREiqTsR1N3fRmeiEixlP1o6uhCs4hIsZT9aJpyXWgWESmWUIymNfrVNRGRoihKKJjZl83MzWxR8NrM7Ntm1mFmb5rZZ7L63mhm7wePG7PaLzSzt4Jtvm3T+IEEzRRERIqj4NHUzFYCnwc+ymreBLQGjy3A/UHfBcBdwMXAeuAuM2sKtrk/6Du63cap1qALzSIixVGM0fRe4C9IX/MdtRl42NNeBhrNbBmwAXjW3Y+6+zHgWWBjsG6eu7/k7g48DFw11QI0UxARKY6CRlMz+yJwwN3fGLdqBbA/63Vn0Haq9s4c7RP93S1m1m5m7aBQEBEplthkHczsOWBpjlV3Al8Brsi1WY42z6M9J3ffCmwFqFnW6vrwmohIcUwaCu5+ea52MzsfWAW8EVwTbgZ+aWbrSf9Lf2VW92bgYND+2XHtPw3am3P0nxKFgohIceQ9mrr7W+5+hru3uHsL6YH9M+5+CHgCuCG4C+kS4Li7fwxsB64ws6bgAvMVwPZgXa+ZXRLcdXQD8PhUa9HpIxGR4ph0ppCnp4ArgQ5gALgJwN2PmtndwI6g31+5+9Fg+c+AB4E64OngMSXVUX1OQUSkGIoWCsFsYXTZgVsm6PcA8ECO9nbgvHz+tmYKIiLFEYrRVNcURESKIxSjqWYKIiLFEYrRVKEgIlIcoRhN9TUXIiLFEYrRVDMFEZHiCMVoGotM+QtVRUTkFMIRCjp9JCJSFKEYTauimimIiBRDKEIhFgnF2xARKblQjKaaKYiIFEfZh4IB0/jlThEROYWyD4Wcv8QgIiJ5KftQMKWCiEjRlH8oKBNERIqm/EOh1AWIiIRI+YeCpgoiIkVT/qFQ6gJEREKk/ENBqSAiUjTlHwqaK4iIFE35h4IyQUSkaMo+FEREpHjKPhT0UwoiIsVT9qGgW1JFRIqn7ENBRESKp+xDQRMFEZHiKf9Q0C2pIiJFU/6hoEwQESma8g+FUhcgIhIi5R8KmiqIiBRNCEKh1BWIiIRHQaFgZl81swNm9nrwuDJr3R1m1mFmu81sQ1b7xqCtw8xuz2pfZWa/MLP3zeyHZlY9pRoKeQMiInKSYswU7nX3dcHjKQAzWwNcB6wFNgL3mVnUzKLAd4BNwBrg+qAvwNeDfbUCx4Cbp/LHdfpIRKR4Ttfpo83ANncfdve9QAewPnh0uPsed48D24DNlh7ZLwMeC7Z/CLhqKn9IkSAiUjzFCIVbzexNM3vAzJqCthXA/qw+nUHbRO0LgR53T4xrz8nMtphZu5m1Dw4OFOEtiIgITCEUzOw5M9uZ47EZuB84C1gHfAx8a3SzHLvyPNpzcvet7t7m7m319fWTvQUREZmi2GQd3P3yqezIzL4LPBm87ARWZq1uBg4Gy7nau4FGM4sFs4Xs/pP93al0ExGRKSj07qNlWS+vBnYGy08A15lZjZmtAlqBV4AdQGtwp1E16YvRT7i7Az8Brg22vxF4fEo1FPIGRETkJJPOFCbxDTNbR/pUzz7gTwDcfZeZPQq8DSSAW9w9CWBmtwLbgSjwgLvvCvZ1G7DNzP4aeA34/lQKqKkq+49aiIjMGpb+R3r5amtr8/b29lKXISJSVszsVXdvG9+uf2aLiEiGQkFERDIUCiIikqFQEBGRDIWCiIhkKBRERCRDoSAiIhkKBRERySj7D6+ZWRfw4TQ3W0T6+5ZExyKbjsUYHYsxYT0Wn3D3xeMbyz4U8mFm7bk+yVeJdCzG6FiM0bEYU2nHQqePREQkQ6EgIiIZlRoKW0tdwCyiYzFGx2KMjsWYijoWFXlNQUREcqvUmYKIiOSgUBARkYyKCgUz+6qZHTCz14PHlVnr7jCzDjPbbWYbSlnnTDKzL5uZm9mi4PV8M/u/ZvaGme0ys5tKXeNMGX8sgrbPBv+t7DKzn5WyvpmU61gE7ReZWdLMrp1o27DJ8f+RPzKzN4PHi2b2qVLXWEyF/hxnObrX3f9bdoOZrSH9e9FrgeXAc2Z2zuhPiIaVma0EPg98lNV8C/C2u/++mS0GdpvZI+4eL0mRMyTXsTCzRuA+YKO7f2RmZ5Sqvpk0wX8XmFkU+Drpn9OtCBMci73A77r7MTPbRPpC9MWlqO90qKiZwilsBra5+7C77wU6gPUlrmkm3Av8Benf2B7lQIOZGTAXOEr6d7bDLtex+EPgn9z9IwB3P1yKwkog17EA+HPgH4FKOQ6Q41i4+4vufix4+TLQXIrCTpdKDIVbg2nfA2bWFLStAPZn9ekM2kLLzL4IHHD3N8at+ltgNXAQeAv4T+6emun6ZtIpjsU5QJOZ/dTMXjWzG0pQ3oya6FiY2QrgauB/lqSwEjjFfxfZbgaenqGSZkToTh+Z2XPA0hyr7gTuB+4mnfp3A98CvgRYjv5lf6/uJMfiK8AVOdZtAF4HLgPOAp41sxfc/cRpK3QG5HksYsCFwOeAOuAlM3vZ3d87bYXOgDyPxX8HbnP3ZHoSGQ55HovRbX+PdCj89umprjRCFwrufvlU+pnZd4Eng5edwMqs1c2k/6Vc1iY6FmZ2PrAKeCP4P3gz8EszWw/cBHzN0x9g6TCzvcC5wCszU/Xpkeex6AS63b0f6Dez54FPAWUdCnkeizZgW9C+CLjSzBLu/n9mpurTI59j4e6HzOwC4HvAJnc/MmMFz4CK+vCamS1z94+D5f8MXOzu15nZWuAfSF9HWA78P6A17BeaR5nZPqDN3bvN7H7gV+7+VTNbAvwS+JS7h/FbIn/NuGOxmvTptA1ANelgvM7dd5awxBmTfSzGtT8IPOnuj5WirlIY99/FmcC/ADe4+4ulraz4QjdTmMQ3zGwd6VND+4A/AXD3XWb2KPA26Yuqt1RKIORwN/Cgmb1F+rTabZUSCOO5+ztm9gzwJpACvlcpgSCn9JfAQuC+YBaRCNO3qFbUTEFERE6tEu8+EhGRCSgUREQkQ6EgIiIZCgUREclQKIiISIZCQUREMhQKIiKS8f8BGzBx40bRGYsAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] @@ -344,40 +344,28 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The value of the absolute impatience factor for the supplied parameter values satisfies the Absolute Impatience Condition. Therefore, the absolute amount of consumption is expected to fall over time.\n", + "The value of the absolute impatience factor (AIF) for the supplied parameter values satisfies the Absolute Impatience Condition. Because the AIF < 1, the absolute amount of consumption is expected to fall over time.\n", "\n", - "The value of the growth impatience factor for the supplied parameter values satisfies the Growth Impatience Condition. Therefore, the ratio of individual wealth to permanent income will fall indefinitely.\n", + "The value of the Growth Impatience Factor for the supplied parameter values satisfies the Perfect Foresight Growth Impatience Condition. Therefore, for a perfect foresight consumer, the ratio of individual wealth to permanent income will fall indefinitely.\n", "\n", - "The return impatience factor value for the supplied parameter values satisfies the Return Impatience Condition. Therefore, the limiting consumption function is not c(m)=0\n", + "The return impatience factor value for the supplied parameter values satisfies the Return Impatience Condition. Therefore, the limiting consumption function is not c(m)=0 for all m\n", "\n", "The Finite Human wealth factor value for the supplied parameter values satisfies the Finite Human Wealth Condition. Therefore, the limiting consumption function is not c(m)=Infinity\n", "and human wealth normalized by permanent income is 51.50000\n", "and the PDV of future consumption growth is 22.58285\n", "\n" ] - }, - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "PFexample.checkConditions(verbose=True,public_call=True)" + "PFexample.checkConditions(verbose=True)" ] }, { @@ -421,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "code_folding": [ 0 @@ -461,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "code_folding": [ 0 @@ -484,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "code_folding": [ 0 @@ -493,7 +481,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -523,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "code_folding": [ 0 @@ -532,7 +520,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -568,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "code_folding": [ 0 @@ -577,7 +565,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -672,5 +660,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/ConsIndShockModel/PerfForesightConsumerType.py b/examples/ConsIndShockModel/PerfForesightConsumerType.py index 3b7eeea69..c8c5b32cf 100644 --- a/examples/ConsIndShockModel/PerfForesightConsumerType.py +++ b/examples/ConsIndShockModel/PerfForesightConsumerType.py @@ -7,7 +7,7 @@ # extension: .py # format_name: percent # format_version: '1.2' -# jupytext_version: 1.2.1 +# jupytext_version: 1.2.4 # kernelspec: # display_name: Python 3 # language: python @@ -86,7 +86,7 @@ # | Parameter | Description | Code | Example value | Time-varying? | # | :---: | --- | --- | --- | --- | # | $\DiscFac$ |Intertemporal discount factor | $\texttt{DiscFac}$ | $0.96$ | | -# | $\CRRA $ |Coefficient of relative risk aversion | $\texttt{CRRA}$ | $2.0$ | | +# | $\CRRA$|Coefficient of relative risk aversion | $\texttt{CRRA}$ | $2.0$ | | # | $\Rfree$ | Risk free interest factor | $\texttt{Rfree}$ | $1.03$ | | # | $1 - \DiePrb_{t+1}$ |Survival probability | $\texttt{LivPrb}$ | $[0.98]$ | $\surd$ | # |$\PermGroFac_{t+1}$|Permanent income growth factor|$\texttt{PermGroFac}$| $[1.01]$ | $\surd$ | @@ -208,7 +208,7 @@ # The code performs tests for whether the supplied parameter values meet various conditions that determine the properties of the solution. Some conditions (like the Finite Human Wealth Condition) are required for the model to have a sensible solution, and if these conditions are violated the code generates a warning message. Other conditions govern characteristics of the model like whether consumption is falling (whether the consumer is 'absolutely impatient'). All conditions can manually be performed using the syntax below. The function returns "False" if none of the key conditions has been violated. # %% -PFexample.checkConditions(verbose=True,public_call=True) +PFexample.checkConditions(verbose=True) # %% [markdown] # An element of $\texttt{solution}$ also includes the (normalized) marginal value function $\texttt{vPfunc}$, and the lower and upper bounds of the marginal propensity to consume (MPC) $\texttt{MPCmin}$ and $\texttt{MPCmax}$. Note that with a linear consumption function, the MPC is constant, so its lower and upper bound are identical.