def use_points_for_interpolation(self, cNrm, mNrm, interpolator): """ Make a basic solution object with a consumption function and marginal value function (unconditional on the preference shock). Parameters ---------- cNrm : np.array Consumption points for interpolation. mNrm : np.array Corresponding market resource points for interpolation. interpolator : function A function that constructs and returns a consumption function. Returns ------- solution_now : ConsumerSolution The solution to this period's consumption-saving problem, with a consumption function, marginal value function, and minimum m. """ # Make the preference-shock specific consumption functions PrefShkCount = self.PrefShkVals.size cFunc_list = [] for j in range(PrefShkCount): MPCmin_j = self.MPCminNow * self.PrefShkVals[j]**(1.0 / self.CRRA) cFunc_this_shock = LowerEnvelope( LinearInterp( mNrm[j, :], cNrm[j, :], intercept_limit=self.hNrmNow * MPCmin_j, slope_limit=MPCmin_j, ), self.cFuncNowCnst, ) cFunc_list.append(cFunc_this_shock) # Combine the list of consumption functions into a single interpolation cFuncNow = LinearInterpOnInterp1D(cFunc_list, self.PrefShkVals) # Make the ex ante marginal value function (before the preference shock) m_grid = self.aXtraGrid + self.mNrmMinNow vP_vec = np.zeros_like(m_grid) for j in range( PrefShkCount): # numeric integration over the preference shock vP_vec += (self.uP(cFunc_list[j](m_grid)) * self.PrefShkPrbs[j] * self.PrefShkVals[j]) vPnvrs_vec = self.uPinv(vP_vec) vPfuncNow = MargValueFuncCRRA(LinearInterp(m_grid, vPnvrs_vec), self.CRRA) # Store the results in a solution object and return it solution_now = ConsumerSolution(cFunc=cFuncNow, vPfunc=vPfuncNow, mNrmMin=self.mNrmMinNow) return solution_now
def make_linear_cFunc(self, mLvl, pLvl, cLvl): """ Makes a quasi-bilinear interpolation to represent the (unconstrained) consumption function. Parameters ---------- mLvl : np.array Market resource points for interpolation. pLvl : np.array Persistent income level points for interpolation. cLvl : np.array Consumption points for interpolation. Returns ------- cFuncUnc : LinearInterp The unconstrained consumption function for this period. """ cFunc_by_pLvl_list = [] # list of consumption functions for each pLvl for j in range(pLvl.shape[0]): pLvl_j = pLvl[j, 0] m_temp = mLvl[j, :] - self.BoroCnstNat(pLvl_j) c_temp = cLvl[ j, :] # Make a linear consumption function for this pLvl if pLvl_j > 0: cFunc_by_pLvl_list.append( LinearInterp( m_temp, c_temp, lower_extrap=True, slope_limit=self.MPCminNow, intercept_limit=self.MPCminNow * self.hLvlNow(pLvl_j), )) else: cFunc_by_pLvl_list.append( LinearInterp(m_temp, c_temp, lower_extrap=True)) pLvl_list = pLvl[:, 0] cFuncUncBase = LinearInterpOnInterp1D( cFunc_by_pLvl_list, pLvl_list) # Combine all linear cFuncs cFuncUnc = VariableLowerBoundFunc2D( cFuncUncBase, self.BoroCnstNat ) # Re-adjust for natural borrowing constraint (as lower bound) return cFuncUnc
def make_cubic_cFunc(self, mLvl, pLvl, cLvl): """ Makes a quasi-cubic spline interpolation of the unconstrained consumption function for this period. Function is cubic splines with respect to mLvl, but linear in pLvl. Parameters ---------- mLvl : np.array Market resource points for interpolation. pLvl : np.array Persistent income level points for interpolation. cLvl : np.array Consumption points for interpolation. Returns ------- cFuncUnc : CubicInterp The unconstrained consumption function for this period. """ # Calculate the MPC at each gridpoint EndOfPrdvPP = (self.DiscFacEff * self.Rfree * self.Rfree * np.sum( self.vPPfuncNext(self.mLvlNext, self.pLvlNext) * self.ShkPrbs_temp, axis=0, )) dcda = EndOfPrdvPP / self.uPP(np.array(cLvl[1:, 1:])) MPC = dcda / (dcda + 1.0) MPC = np.concatenate((np.reshape(MPC[:, 0], (MPC.shape[0], 1)), MPC), axis=1) # Stick an extra MPC value at bottom; MPCmax doesn't work MPC = np.concatenate((self.MPCminNow * np.ones( (1, self.aXtraGrid.size + 1)), MPC), axis=0) # Make cubic consumption function with respect to mLvl for each persistent income level cFunc_by_pLvl_list = [] # list of consumption functions for each pLvl for j in range(pLvl.shape[0]): pLvl_j = pLvl[j, 0] m_temp = mLvl[j, :] - self.BoroCnstNat(pLvl_j) c_temp = cLvl[ j, :] # Make a cubic consumption function for this pLvl MPC_temp = MPC[j, :] if pLvl_j > 0: cFunc_by_pLvl_list.append( CubicInterp( m_temp, c_temp, MPC_temp, lower_extrap=True, slope_limit=self.MPCminNow, intercept_limit=self.MPCminNow * self.hLvlNow(pLvl_j), )) else: # When pLvl=0, cFunc is linear cFunc_by_pLvl_list.append( LinearInterp(m_temp, c_temp, lower_extrap=True)) pLvl_list = pLvl[:, 0] cFuncUncBase = LinearInterpOnInterp1D( cFunc_by_pLvl_list, pLvl_list) # Combine all linear cFuncs cFuncUnc = VariableLowerBoundFunc2D(cFuncUncBase, self.BoroCnstNat) # Re-adjust for lower bound of natural borrowing constraint return cFuncUnc
def make_vFunc(self, solution): """ Creates the value function for this period, defined over market resources m and persistent income p. self must have the attribute EndOfPrdvFunc in order to execute. Parameters ---------- solution : ConsumerSolution The solution to this single period problem, which must include the consumption function. Returns ------- vFuncNow : ValueFuncCRRA A representation of the value function for this period, defined over market resources m and persistent income p: v = vFuncNow(m,p). """ mSize = self.aXtraGrid.size pSize = self.pLvlGrid.size # Compute expected value and marginal value on a grid of market resources pLvl_temp = np.tile(self.pLvlGrid, (mSize, 1)) # Tile pLvl across m values mLvl_temp = (np.tile(self.mLvlMinNow(self.pLvlGrid), (mSize, 1)) + np.tile(np.reshape(self.aXtraGrid, (mSize, 1)), (1, pSize)) * pLvl_temp) cLvlNow = solution.cFunc(mLvl_temp, pLvl_temp) aLvlNow = mLvl_temp - cLvlNow vNow = self.u(cLvlNow) + self.EndOfPrdvFunc(aLvlNow, pLvl_temp) vPnow = self.uP(cLvlNow) # Calculate pseudo-inverse value and its first derivative (wrt mLvl) vNvrs = self.uinv(vNow) # value transformed through inverse utility vNvrsP = vPnow * self.uinvP(vNow) # Add data at the lower bound of m mLvl_temp = np.concatenate((np.reshape(self.mLvlMinNow(self.pLvlGrid), (1, pSize)), mLvl_temp), axis=0) vNvrs = np.concatenate((np.zeros((1, pSize)), vNvrs), axis=0) vNvrsP = np.concatenate((np.reshape(vNvrsP[0, :], (1, vNvrsP.shape[1])), vNvrsP), axis=0) # Add data at the lower bound of p MPCminNvrs = self.MPCminNow**(-self.CRRA / (1.0 - self.CRRA)) m_temp = np.reshape(mLvl_temp[:, 0], (mSize + 1, 1)) mLvl_temp = np.concatenate((m_temp, mLvl_temp), axis=1) vNvrs = np.concatenate((MPCminNvrs * m_temp, vNvrs), axis=1) vNvrsP = np.concatenate((MPCminNvrs * np.ones((mSize + 1, 1)), vNvrsP), axis=1) # Construct the pseudo-inverse value function vNvrsFunc_list = [] for j in range(pSize + 1): pLvl = np.insert(self.pLvlGrid, 0, 0.0)[j] vNvrsFunc_list.append( CubicInterp( mLvl_temp[:, j] - self.mLvlMinNow(pLvl), vNvrs[:, j], vNvrsP[:, j], MPCminNvrs * self.hLvlNow(pLvl), MPCminNvrs, )) vNvrsFuncBase = LinearInterpOnInterp1D( vNvrsFunc_list, np.insert(self.pLvlGrid, 0, 0.0)) # Value function "shifted" vNvrsFuncNow = VariableLowerBoundFunc2D(vNvrsFuncBase, self.mLvlMinNow) # "Re-curve" the pseudo-inverse value function into the value function vFuncNow = ValueFuncCRRA(vNvrsFuncNow, self.CRRA) return vFuncNow
def make_EndOfPrdvFunc(self, EndOfPrdvP): """ Construct the end-of-period value function for this period, storing it as an attribute of self for use by other methods. Parameters ---------- EndOfPrdvP : np.array Array of end-of-period marginal value of assets corresponding to the asset values in self.aLvlNow x self.pLvlGrid. Returns ------- none """ vLvlNext = self.vFuncNext( self.mLvlNext, self.pLvlNext) # value in many possible future states EndOfPrdv = self.DiscFacEff * np.sum( vLvlNext * self.ShkPrbs_temp, axis=0) # expected value, averaging across states EndOfPrdvNvrs = self.uinv( EndOfPrdv) # value transformed through inverse utility EndOfPrdvNvrsP = EndOfPrdvP * self.uinvP(EndOfPrdv) # Add points at mLvl=zero EndOfPrdvNvrs = np.concatenate((np.zeros( (self.pLvlGrid.size, 1)), EndOfPrdvNvrs), axis=1) if hasattr(self, "MedShkDstn"): EndOfPrdvNvrsP = np.concatenate((np.zeros( (self.pLvlGrid.size, 1)), EndOfPrdvNvrsP), axis=1) else: EndOfPrdvNvrsP = np.concatenate( ( np.reshape(EndOfPrdvNvrsP[:, 0], (self.pLvlGrid.size, 1)), EndOfPrdvNvrsP, ), axis=1, ) # This is a very good approximation, vNvrsPP = 0 at the asset minimum aLvl_temp = np.concatenate( ( np.reshape(self.BoroCnstNat(self.pLvlGrid), (self.pLvlGrid.size, 1)), self.aLvlNow, ), axis=1, ) # Make an end-of-period value function for each persistent income level in the grid EndOfPrdvNvrsFunc_list = [] for p in range(self.pLvlGrid.size): EndOfPrdvNvrsFunc_list.append( CubicInterp( aLvl_temp[p, :] - self.BoroCnstNat(self.pLvlGrid[p]), EndOfPrdvNvrs[p, :], EndOfPrdvNvrsP[p, :], )) EndOfPrdvNvrsFuncBase = LinearInterpOnInterp1D(EndOfPrdvNvrsFunc_list, self.pLvlGrid) # Re-adjust the combined end-of-period value function to account for the natural borrowing constraint shifter EndOfPrdvNvrsFunc = VariableLowerBoundFunc2D(EndOfPrdvNvrsFuncBase, self.BoroCnstNat) self.EndOfPrdvFunc = ValueFuncCRRA(EndOfPrdvNvrsFunc, self.CRRA)
def solveConsPortfolio(solution_next, ShockDstn, IncomeDstn, RiskyDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, ShareGrid, vFuncBool, AdjustPrb, DiscreteShareBool, ShareLimit, IndepDstnBool): ''' Solve the one period problem for a portfolio-choice consumer. Parameters ---------- solution_next : PortfolioSolution Solution to next period's problem. ShockDstn : [np.array] List with four arrays: discrete probabilities, permanent income shocks, transitory income shocks, and risky returns. This is only used if the input IndepDstnBool is False, indicating that income and return distributions can't be assumed to be independent. IncomeDstn : [np.array] List with three arrays: discrete probabilities, permanent income shocks, and transitory income shocks. This is only used if the input IndepDsntBool is True, indicating that income and return distributions are independent. RiskyDstn : [np.array] List with two arrays: discrete probabilities and risky asset returns. This is only used if the input IndepDstnBool is True, indicating that income and return distributions are independent. LivPrb : float Survival probability; likelihood of being alive at the beginning of the succeeding period. DiscFac : float Intertemporal discount factor for future utility. CRRA : float Coefficient of relative risk aversion. Rfree : float Risk free interest factor on end-of-period assets. PermGroFac : float Expected permanent income growth factor at the end of this period. BoroCnstArt: float or None Borrowing constraint for the minimum allowable assets to end the period with. In this model, it is *required* to be zero. aXtraGrid: np.array Array of "extra" end-of-period asset values-- assets above the absolute minimum acceptable level. ShareGrid : np.array Array of risky portfolio shares on which to define the interpolation of the consumption function when Share is fixed. vFuncBool: boolean An indicator for whether the value function should be computed and included in the reported solution. AdjustPrb : float Probability that the agent will be able to update his portfolio share. DiscreteShareBool : bool Indicator for whether risky portfolio share should be optimized on the continuous [0,1] interval using the FOC (False), or instead only selected from the discrete set of values in ShareGrid (True). If True, then vFuncBool must also be True. ShareLimit : float Limiting lower bound of risky portfolio share as mNrm approaches infinity. IndepDstnBool : bool Indicator for whether the income and risky return distributions are in- dependent of each other, which can speed up the expectations step. Returns ------- solution_now : PortfolioSolution The solution to the single period consumption-saving with portfolio choice problem. Includes two consumption and risky share functions: one for when the agent can adjust his portfolio share (Adj) and when he can't (Fxd). ''' # Make sure the individual is liquidity constrained. Allowing a consumer to # borrow *and* invest in an asset with unbounded (negative) returns is a bad mix. if BoroCnstArt != 0.0: raise ValueError('PortfolioConsumerType must have BoroCnstArt=0.0!') # Make sure that if risky portfolio share is optimized only discretely, then # the value function is also constructed (else this task would be impossible). if (DiscreteShareBool and (not vFuncBool)): raise ValueError( 'PortfolioConsumerType requires vFuncBool to be True when DiscreteShareBool is True!' ) # Define temporary functions for utility and its derivative and inverse u = lambda x: utility(x, CRRA) uP = lambda x: utilityP(x, CRRA) uPinv = lambda x: utilityP_inv(x, CRRA) n = lambda x: utility_inv(x, CRRA) nP = lambda x: utility_invP(x, CRRA) # Unpack next period's solution vPfuncAdj_next = solution_next.vPfuncAdj dvdmFuncFxd_next = solution_next.dvdmFuncFxd dvdsFuncFxd_next = solution_next.dvdsFuncFxd vFuncAdj_next = solution_next.vFuncAdj vFuncFxd_next = solution_next.vFuncFxd # Major method fork: (in)dependent risky asset return and income distributions if IndepDstnBool: # If the distributions ARE independent... # Unpack the shock distribution IncPrbs_next = IncomeDstn.pmf PermShks_next = IncomeDstn.X[0] TranShks_next = IncomeDstn.X[1] Rprbs_next = RiskyDstn.pmf Risky_next = RiskyDstn.X zero_bound = ( np.min(TranShks_next) == 0. ) # Flag for whether the natural borrowing constraint is zero RiskyMax = np.max(Risky_next) # bNrm represents R*a, balances after asset return shocks but before income. # This just uses the highest risky return as a rough shifter for the aXtraGrid. if zero_bound: aNrmGrid = aXtraGrid bNrmGrid = np.insert(RiskyMax * aXtraGrid, 0, np.min(Risky_next) * aXtraGrid[0]) else: aNrmGrid = np.insert(aXtraGrid, 0, 0.0) # Add an asset point at exactly zero bNrmGrid = RiskyMax * np.insert(aXtraGrid, 0, 0.0) # Get grid and shock sizes, for easier indexing aNrm_N = aNrmGrid.size bNrm_N = bNrmGrid.size Share_N = ShareGrid.size Income_N = IncPrbs_next.size Risky_N = Rprbs_next.size # Make tiled arrays to calculate future realizations of mNrm and Share when integrating over IncomeDstn bNrm_tiled = np.tile(np.reshape(bNrmGrid, (bNrm_N, 1, 1)), (1, Share_N, Income_N)) Share_tiled = np.tile(np.reshape(ShareGrid, (1, Share_N, 1)), (bNrm_N, 1, Income_N)) IncPrbs_tiled = np.tile(np.reshape(IncPrbs_next, (1, 1, Income_N)), (bNrm_N, Share_N, 1)) PermShks_tiled = np.tile(np.reshape(PermShks_next, (1, 1, Income_N)), (bNrm_N, Share_N, 1)) TranShks_tiled = np.tile(np.reshape(TranShks_next, (1, 1, Income_N)), (bNrm_N, Share_N, 1)) # Calculate future realizations of market resources mNrm_next = bNrm_tiled / (PermShks_tiled * PermGroFac) + TranShks_tiled Share_next = Share_tiled # Evaluate realizations of marginal value of market resources next period dvdmAdj_next = vPfuncAdj_next(mNrm_next) if AdjustPrb < 1.: dvdmFxd_next = dvdmFuncFxd_next(mNrm_next, Share_next) dvdm_next = AdjustPrb * dvdmAdj_next + ( 1. - AdjustPrb) * dvdmFxd_next # Combine by adjustment probability else: # Don't bother evaluating if there's no chance that portfolio share is fixed dvdm_next = dvdmAdj_next # Evaluate realizations of marginal value of risky share next period dvdsAdj_next = np.zeros_like( mNrm_next) # No marginal value of Share if it's a free choice! if AdjustPrb < 1.: dvdsFxd_next = dvdsFuncFxd_next(mNrm_next, Share_next) dvds_next = AdjustPrb * dvdsAdj_next + ( 1. - AdjustPrb) * dvdsFxd_next # Combine by adjustment probability else: # Don't bother evaluating if there's no chance that portfolio share is fixed dvds_next = dvdsAdj_next # If the value function has been requested, evaluate realizations of value if vFuncBool: vAdj_next = vFuncAdj_next(mNrm_next) if AdjustPrb < 1.: vFxd_next = vFuncFxd_next(mNrm_next, Share_next) v_next = AdjustPrb * vAdj_next + (1. - AdjustPrb) * vFxd_next else: # Don't bother evaluating if there's no chance that portfolio share is fixed v_next = vAdj_next else: v_next = np.zeros_like(dvdm_next) # Trivial array # Calculate intermediate marginal value of bank balances by taking expectations over income shocks temp_fac_A = uP(PermShks_tiled * PermGroFac) # Will use this in a couple places dvdb_intermed = np.sum(IncPrbs_tiled * temp_fac_A * dvdm_next, axis=2) dvdbNvrs_intermed = uPinv(dvdb_intermed) dvdbNvrsFunc_intermed = BilinearInterp(dvdbNvrs_intermed, bNrmGrid, ShareGrid) dvdbFunc_intermed = MargValueFunc2D(dvdbNvrsFunc_intermed, CRRA) # Calculate intermediate value by taking expectations over income shocks temp_fac_B = (PermShks_tiled * PermGroFac)**(1. - CRRA ) # Will use this below if vFuncBool: v_intermed = np.sum(IncPrbs_tiled * temp_fac_B * v_next, axis=2) vNvrs_intermed = n(v_intermed) vNvrsFunc_intermed = BilinearInterp(vNvrs_intermed, bNrmGrid, ShareGrid) vFunc_intermed = ValueFunc2D(vNvrsFunc_intermed, CRRA) # Calculate intermediate marginal value of risky portfolio share by taking expectations dvds_intermed = np.sum(IncPrbs_tiled * temp_fac_B * dvds_next, axis=2) dvdsFunc_intermed = BilinearInterp(dvds_intermed, bNrmGrid, ShareGrid) # Make tiled arrays to calculate future realizations of bNrm and Share when integrating over RiskyDstn aNrm_tiled = np.tile(np.reshape(aNrmGrid, (aNrm_N, 1, 1)), (1, Share_N, Risky_N)) Share_tiled = np.tile(np.reshape(ShareGrid, (1, Share_N, 1)), (aNrm_N, 1, Risky_N)) Rprbs_tiled = np.tile(np.reshape(Rprbs_next, (1, 1, Risky_N)), (aNrm_N, Share_N, 1)) Risky_tiled = np.tile(np.reshape(Risky_next, (1, 1, Risky_N)), (aNrm_N, Share_N, 1)) # Calculate future realizations of bank balances bNrm Share_next = Share_tiled Rxs = Risky_tiled - Rfree Rport = Rfree + Share_next * Rxs bNrm_next = Rport * aNrm_tiled # Evaluate realizations of value and marginal value after asset returns are realized dvdb_next = dvdbFunc_intermed(bNrm_next, Share_next) dvds_next = dvdsFunc_intermed(bNrm_next, Share_next) if vFuncBool: v_next = vFunc_intermed(bNrm_next, Share_next) else: v_next = np.zeros_like(dvdb_next) # Calculate end-of-period marginal value of assets by taking expectations EndOfPrddvda = DiscFac * LivPrb * np.sum( Rprbs_tiled * Rport * dvdb_next, axis=2) EndOfPrddvdaNvrs = uPinv(EndOfPrddvda) # Calculate end-of-period value by taking expectations if vFuncBool: EndOfPrdv = DiscFac * LivPrb * np.sum(Rprbs_tiled * v_next, axis=2) EndOfPrdvNvrs = n(EndOfPrdv) # Calculate end-of-period marginal value of risky portfolio share by taking expectations EndOfPrddvds = DiscFac * LivPrb * np.sum( Rprbs_tiled * (Rxs * aNrm_tiled * dvdb_next + dvds_next), axis=2) else: # If the distributions are NOT independent... # Unpack the shock distribution ShockPrbs_next = ShockDstn[0] PermShks_next = ShockDstn[1] TranShks_next = ShockDstn[2] Risky_next = ShockDstn[3] zero_bound = ( np.min(TranShks_next) == 0. ) # Flag for whether the natural borrowing constraint is zero # Make tiled arrays to calculate future realizations of mNrm and Share; dimension order: mNrm, Share, shock if zero_bound: aNrmGrid = aXtraGrid else: aNrmGrid = np.insert(aXtraGrid, 0, 0.0) # Add an asset point at exactly zero aNrm_N = aNrmGrid.size Share_N = ShareGrid.size Shock_N = ShockPrbs_next.size aNrm_tiled = np.tile(np.reshape(aNrmGrid, (aNrm_N, 1, 1)), (1, Share_N, Shock_N)) Share_tiled = np.tile(np.reshape(ShareGrid, (1, Share_N, 1)), (aNrm_N, 1, Shock_N)) ShockPrbs_tiled = np.tile(np.reshape(ShockPrbs_next, (1, 1, Shock_N)), (aNrm_N, Share_N, 1)) PermShks_tiled = np.tile(np.reshape(PermShks_next, (1, 1, Shock_N)), (aNrm_N, Share_N, 1)) TranShks_tiled = np.tile(np.reshape(TranShks_next, (1, 1, Shock_N)), (aNrm_N, Share_N, 1)) Risky_tiled = np.tile(np.reshape(Risky_next, (1, 1, Shock_N)), (aNrm_N, Share_N, 1)) # Calculate future realizations of market resources Rport = (1. - Share_tiled) * Rfree + Share_tiled * Risky_tiled mNrm_next = Rport * aNrm_tiled / (PermShks_tiled * PermGroFac) + TranShks_tiled Share_next = Share_tiled # Evaluate realizations of marginal value of market resources next period dvdmAdj_next = vPfuncAdj_next(mNrm_next) if AdjustPrb < 1.: dvdmFxd_next = dvdmFuncFxd_next(mNrm_next, Share_next) dvdm_next = AdjustPrb * dvdmAdj_next + ( 1. - AdjustPrb) * dvdmFxd_next # Combine by adjustment probability else: # Don't bother evaluating if there's no chance that portfolio share is fixed dvdm_next = dvdmAdj_next # Evaluate realizations of marginal value of risky share next period dvdsAdj_next = np.zeros_like( mNrm_next) # No marginal value of Share if it's a free choice! if AdjustPrb < 1.: dvdsFxd_next = dvdsFuncFxd_next(mNrm_next, Share_next) dvds_next = AdjustPrb * dvdsAdj_next + ( 1. - AdjustPrb) * dvdsFxd_next # Combine by adjustment probability else: # Don't bother evaluating if there's no chance that portfolio share is fixed dvds_next = dvdsAdj_next # If the value function has been requested, evaluate realizations of value if vFuncBool: vAdj_next = vFuncAdj_next(mNrm_next) if AdjustPrb < 1.: vFxd_next = vFuncFxd_next(mNrm_next, Share_next) v_next = AdjustPrb * vAdj_next + (1. - AdjustPrb) * vFxd_next else: # Don't bother evaluating if there's no chance that portfolio share is fixed v_next = vAdj_next else: v_next = np.zeros_like(dvdm_next) # Trivial array # Calculate end-of-period marginal value of assets by taking expectations temp_fac_A = uP(PermShks_tiled * PermGroFac) # Will use this in a couple places EndOfPrddvda = DiscFac * LivPrb * np.sum( ShockPrbs_tiled * Rport * temp_fac_A * dvdm_next, axis=2) EndOfPrddvdaNvrs = uPinv(EndOfPrddvda) # Calculate end-of-period value by taking expectations temp_fac_B = (PermShks_tiled * PermGroFac)**(1. - CRRA ) # Will use this below if vFuncBool: EndOfPrdv = DiscFac * LivPrb * np.sum( ShockPrbs_tiled * temp_fac_B * v_next, axis=2) EndOfPrdvNvrs = n(EndOfPrdv) # Calculate end-of-period marginal value of risky portfolio share by taking expectations Rxs = Risky_tiled - Rfree EndOfPrddvds = DiscFac * LivPrb * np.sum( ShockPrbs_tiled * (Rxs * aNrm_tiled * temp_fac_A * dvdm_next + temp_fac_B * dvds_next), axis=2) # Major method fork: discrete vs continuous choice of risky portfolio share if DiscreteShareBool: # Optimization of Share on the discrete set ShareGrid opt_idx = np.argmax(EndOfPrdv, axis=1) Share_now = ShareGrid[ opt_idx] # Best portfolio share is one with highest value cNrmAdj_now = EndOfPrddvdaNvrs[np.arange( aNrm_N), opt_idx] # Take cNrm at that index as well if not zero_bound: Share_now[ 0] = 1. # aNrm=0, so there's no way to "optimize" the portfolio cNrmAdj_now[0] = EndOfPrddvdaNvrs[ 0, -1] # Consumption when aNrm=0 does not depend on Share else: # Optimization of Share on continuous interval [0,1] # For values of aNrm at which the agent wants to put more than 100% into risky asset, constrain them FOC_s = EndOfPrddvds Share_now = np.zeros_like( aNrmGrid) # Initialize to putting everything in safe asset cNrmAdj_now = np.zeros_like(aNrmGrid) constrained = FOC_s[:, -1] > 0. # If agent wants to put more than 100% into risky asset, he is constrained Share_now[constrained] = 1.0 if not zero_bound: Share_now[ 0] = 1. # aNrm=0, so there's no way to "optimize" the portfolio cNrmAdj_now[0] = EndOfPrddvdaNvrs[ 0, -1] # Consumption when aNrm=0 does not depend on Share cNrmAdj_now[constrained] = EndOfPrddvdaNvrs[ constrained, -1] # Get consumption when share-constrained # For each value of aNrm, find the value of Share such that FOC-Share == 0. # This loop can probably be eliminated, but it's such a small step that it won't speed things up much. crossing = np.logical_and(FOC_s[:, 1:] <= 0., FOC_s[:, :-1] >= 0.) for j in range(aNrm_N): if Share_now[j] == 0.: try: idx = np.argwhere(crossing[j, :])[0][0] bot_s = ShareGrid[idx] top_s = ShareGrid[idx + 1] bot_f = FOC_s[j, idx] top_f = FOC_s[j, idx + 1] bot_c = EndOfPrddvdaNvrs[j, idx] top_c = EndOfPrddvdaNvrs[j, idx + 1] alpha = 1. - top_f / (top_f - bot_f) Share_now[j] = (1. - alpha) * bot_s + alpha * top_s cNrmAdj_now[j] = (1. - alpha) * bot_c + alpha * top_c except: print('No optimal controls found for a=' + str(aNrmGrid[j])) # Calculate the endogenous mNrm gridpoints when the agent adjusts his portfolio mNrmAdj_now = aNrmGrid + cNrmAdj_now # Construct the risky share function when the agent can adjust if DiscreteShareBool: mNrmAdj_mid = (mNrmAdj_now[1:] + mNrmAdj_now[:-1]) / 2 mNrmAdj_plus = mNrmAdj_mid * (1. + 1e-12) mNrmAdj_comb = (np.transpose(np.vstack( (mNrmAdj_mid, mNrmAdj_plus)))).flatten() mNrmAdj_comb = np.append(np.insert(mNrmAdj_comb, 0, 0.0), mNrmAdj_now[-1]) Share_comb = (np.transpose(np.vstack( (Share_now, Share_now)))).flatten() ShareFuncAdj_now = LinearInterp(mNrmAdj_comb, Share_comb) else: if zero_bound: Share_lower_bound = ShareLimit else: Share_lower_bound = 1.0 Share_now = np.insert(Share_now, 0, Share_lower_bound) ShareFuncAdj_now = LinearInterp(np.insert(mNrmAdj_now, 0, 0.0), Share_now, intercept_limit=ShareLimit, slope_limit=0.0) # Construct the consumption function when the agent can adjust cNrmAdj_now = np.insert(cNrmAdj_now, 0, 0.0) cFuncAdj_now = LinearInterp(np.insert(mNrmAdj_now, 0, 0.0), cNrmAdj_now) # Construct the marginal value (of mNrm) function when the agent can adjust vPfuncAdj_now = MargValueFunc(cFuncAdj_now, CRRA) # Construct the consumption function when the agent *can't* adjust the risky share, as well # as the marginal value of Share function cFuncFxd_by_Share = [] dvdsFuncFxd_by_Share = [] for j in range(Share_N): cNrmFxd_temp = EndOfPrddvdaNvrs[:, j] mNrmFxd_temp = aNrmGrid + cNrmFxd_temp cFuncFxd_by_Share.append( LinearInterp(np.insert(mNrmFxd_temp, 0, 0.0), np.insert(cNrmFxd_temp, 0, 0.0))) dvdsFuncFxd_by_Share.append( LinearInterp(np.insert(mNrmFxd_temp, 0, 0.0), np.insert(EndOfPrddvds[:, j], 0, EndOfPrddvds[0, j]))) cFuncFxd_now = LinearInterpOnInterp1D(cFuncFxd_by_Share, ShareGrid) dvdsFuncFxd_now = LinearInterpOnInterp1D(dvdsFuncFxd_by_Share, ShareGrid) # The share function when the agent can't adjust his portfolio is trivial ShareFuncFxd_now = IdentityFunction(i_dim=1, n_dims=2) # Construct the marginal value of mNrm function when the agent can't adjust his share dvdmFuncFxd_now = MargValueFunc2D(cFuncFxd_now, CRRA) # If the value function has been requested, construct it now if vFuncBool: # First, make an end-of-period value function over aNrm and Share EndOfPrdvNvrsFunc = BilinearInterp(EndOfPrdvNvrs, aNrmGrid, ShareGrid) EndOfPrdvFunc = ValueFunc2D(EndOfPrdvNvrsFunc, CRRA) # Construct the value function when the agent can adjust his portfolio mNrm_temp = aXtraGrid # Just use aXtraGrid as our grid of mNrm values cNrm_temp = cFuncAdj_now(mNrm_temp) aNrm_temp = mNrm_temp - cNrm_temp Share_temp = ShareFuncAdj_now(mNrm_temp) v_temp = u(cNrm_temp) + EndOfPrdvFunc(aNrm_temp, Share_temp) vNvrs_temp = n(v_temp) vNvrsP_temp = uP(cNrm_temp) * nP(v_temp) vNvrsFuncAdj = CubicInterp( np.insert(mNrm_temp, 0, 0.0), # x_list np.insert(vNvrs_temp, 0, 0.0), # f_list np.insert(vNvrsP_temp, 0, vNvrsP_temp[0])) # dfdx_list vFuncAdj_now = ValueFunc( vNvrsFuncAdj, CRRA) # Re-curve the pseudo-inverse value function # Construct the value function when the agent *can't* adjust his portfolio mNrm_temp = np.tile(np.reshape(aXtraGrid, (aXtraGrid.size, 1)), (1, Share_N)) Share_temp = np.tile(np.reshape(ShareGrid, (1, Share_N)), (aXtraGrid.size, 1)) cNrm_temp = cFuncFxd_now(mNrm_temp, Share_temp) aNrm_temp = mNrm_temp - cNrm_temp v_temp = u(cNrm_temp) + EndOfPrdvFunc(aNrm_temp, Share_temp) vNvrs_temp = n(v_temp) vNvrsP_temp = uP(cNrm_temp) * nP(v_temp) vNvrsFuncFxd_by_Share = [] for j in range(Share_N): vNvrsFuncFxd_by_Share.append( CubicInterp( np.insert(mNrm_temp[:, 0], 0, 0.0), # x_list np.insert(vNvrs_temp[:, j], 0, 0.0), # f_list np.insert(vNvrsP_temp[:, j], 0, vNvrsP_temp[j, 0]))) #dfdx_list vNvrsFuncFxd = LinearInterpOnInterp1D(vNvrsFuncFxd_by_Share, ShareGrid) vFuncFxd_now = ValueFunc2D(vNvrsFuncFxd, CRRA) else: # If vFuncBool is False, fill in dummy values vFuncAdj_now = None vFuncFxd_now = None # Create and return this period's solution return PortfolioSolution(cFuncAdj=cFuncAdj_now, ShareFuncAdj=ShareFuncAdj_now, vPfuncAdj=vPfuncAdj_now, vFuncAdj=vFuncAdj_now, cFuncFxd=cFuncFxd_now, ShareFuncFxd=ShareFuncFxd_now, dvdmFuncFxd=dvdmFuncFxd_now, dvdsFuncFxd=dvdsFuncFxd_now, vFuncFxd=vFuncFxd_now)
def solve_ConsLaborIntMarg( solution_next, PermShkDstn, TranShkDstn, LivPrb, DiscFac, CRRA, Rfree, PermGroFac, BoroCnstArt, aXtraGrid, TranShkGrid, vFuncBool, CubicBool, WageRte, LbrCost, ): """ Solves one period of the consumption-saving model with endogenous labor supply on the intensive margin by using the endogenous grid method to invert the first order conditions for optimal composite consumption and between consumption and leisure, obviating any search for optimal controls. Parameters ---------- solution_next : ConsumerLaborSolution The solution to the next period's problem; must have the attributes vPfunc and bNrmMinFunc representing marginal value of bank balances and minimum (normalized) bank balances as a function of the transitory shock. PermShkDstn: [np.array] Discrete distribution of permanent productivity shocks. TranShkDstn: [np.array] Discrete distribution of transitory productivity shocks. LivPrb : float Survival probability; likelihood of being alive at the beginning of the succeeding period. DiscFac : float Intertemporal discount factor. CRRA : float Coefficient of relative risk aversion over the composite good. Rfree : float Risk free interest rate on assets retained at the end of the period. PermGroFac : float Expected permanent income growth factor for next period. BoroCnstArt: float or None Borrowing constraint for the minimum allowable assets to end the period with. Currently not handled, must be None. aXtraGrid: np.array Array of "extra" end-of-period asset values-- assets above the absolute minimum acceptable level. TranShkGrid: np.array Grid of transitory shock values to use as a state grid for interpolation. vFuncBool: boolean An indicator for whether the value function should be computed and included in the reported solution. Not yet handled, must be False. CubicBool: boolean An indicator for whether the solver should use cubic or linear interpolation. Cubic interpolation is not yet handled, must be False. WageRte: float Wage rate per unit of labor supplied. LbrCost: float Cost parameter for supplying labor: u_t = U(x_t), x_t = c_t*z_t^LbrCost, where z_t is leisure = 1 - Lbr_t. Returns ------- solution_now : ConsumerLaborSolution The solution to this period's problem, including a consumption function cFunc, a labor supply function LbrFunc, and a marginal value function vPfunc; each are defined over normalized bank balances and transitory prod shock. Also includes bNrmMinNow, the minimum permissible bank balances as a function of the transitory productivity shock. """ # Make sure the inputs for this period are valid: CRRA > LbrCost/(1+LbrCost) # and CubicBool = False. CRRA condition is met automatically when CRRA >= 1. frac = 1.0 / (1.0 + LbrCost) if CRRA <= frac * LbrCost: print( "Error: make sure CRRA coefficient is strictly greater than alpha/(1+alpha)." ) sys.exit() if BoroCnstArt is not None: print( "Error: Model cannot handle artificial borrowing constraint yet. ") sys.exit() if vFuncBool or CubicBool is True: print("Error: Model cannot handle cubic interpolation yet.") sys.exit() # Unpack next period's solution and the productivity shock distribution, and define the inverse (marginal) utilty function vPfunc_next = solution_next.vPfunc TranShkPrbs = TranShkDstn.pmf TranShkVals = TranShkDstn.X.flatten() PermShkPrbs = PermShkDstn.pmf PermShkVals = PermShkDstn.X.flatten() TranShkCount = TranShkPrbs.size PermShkCount = PermShkPrbs.size uPinv = lambda X: CRRAutilityP_inv(X, gam=CRRA) # Make tiled versions of the grid of a_t values and the components of the shock distribution aXtraCount = aXtraGrid.size bNrmGrid = aXtraGrid # Next period's bank balances before labor income # Replicated axtraGrid of b_t values (bNowGrid) for each transitory (productivity) shock bNrmGrid_rep = np.tile(np.reshape(bNrmGrid, (aXtraCount, 1)), (1, TranShkCount)) # Replicated transitory shock values for each a_t state TranShkVals_rep = np.tile(np.reshape(TranShkVals, (1, TranShkCount)), (aXtraCount, 1)) # Replicated transitory shock probabilities for each a_t state TranShkPrbs_rep = np.tile(np.reshape(TranShkPrbs, (1, TranShkCount)), (aXtraCount, 1)) # Construct a function that gives marginal value of next period's bank balances *just before* the transitory shock arrives # Next period's marginal value at every transitory shock and every bank balances gridpoint vPNext = vPfunc_next(bNrmGrid_rep, TranShkVals_rep) # Integrate out the transitory shocks (in TranShkVals direction) to get expected vP just before the transitory shock vPbarNext = np.sum(vPNext * TranShkPrbs_rep, axis=1) # Transformed marginal value through the inverse marginal utility function to "decurve" it vPbarNvrsNext = uPinv(vPbarNext) # Linear interpolation over b_{t+1}, adding a point at minimal value of b = 0. vPbarNvrsFuncNext = LinearInterp(np.insert(bNrmGrid, 0, 0.0), np.insert(vPbarNvrsNext, 0, 0.0)) # "Recurve" the intermediate marginal value function through the marginal utility function vPbarFuncNext = MargValueFuncCRRA(vPbarNvrsFuncNext, CRRA) # Get next period's bank balances at each permanent shock from each end-of-period asset values # Replicated grid of a_t values for each permanent (productivity) shock aNrmGrid_rep = np.tile(np.reshape(aXtraGrid, (aXtraCount, 1)), (1, PermShkCount)) # Replicated permanent shock values for each a_t value PermShkVals_rep = np.tile(np.reshape(PermShkVals, (1, PermShkCount)), (aXtraCount, 1)) # Replicated permanent shock probabilities for each a_t value PermShkPrbs_rep = np.tile(np.reshape(PermShkPrbs, (1, PermShkCount)), (aXtraCount, 1)) bNrmNext = (Rfree / (PermGroFac * PermShkVals_rep)) * aNrmGrid_rep # Calculate marginal value of end-of-period assets at each a_t gridpoint # Get marginal value of bank balances next period at each shock vPbarNext = (PermGroFac * PermShkVals_rep)**(-CRRA) * vPbarFuncNext(bNrmNext) # Take expectation across permanent income shocks EndOfPrdvP = (DiscFac * Rfree * LivPrb * np.sum(vPbarNext * PermShkPrbs_rep, axis=1, keepdims=True)) # Compute scaling factor for each transitory shock TranShkScaleFac_temp = (frac * (WageRte * TranShkGrid)**(LbrCost * frac) * (LbrCost**(-LbrCost * frac) + LbrCost**frac)) # Flip it to be a row vector TranShkScaleFac = np.reshape(TranShkScaleFac_temp, (1, TranShkGrid.size)) # Use the first order condition to compute an array of "composite good" x_t values corresponding to (a_t,theta_t) values xNow = (np.dot(EndOfPrdvP, TranShkScaleFac))**(-1.0 / (CRRA - LbrCost * frac)) # Transform the composite good x_t values into consumption c_t and leisure z_t values TranShkGrid_rep = np.tile(np.reshape(TranShkGrid, (1, TranShkGrid.size)), (aXtraCount, 1)) xNowPow = xNow**frac # Will use this object multiple times in math below # Find optimal consumption from optimal composite good cNrmNow = (( (WageRte * TranShkGrid_rep) / LbrCost)**(LbrCost * frac)) * xNowPow # Find optimal leisure from optimal composite good LsrNow = (LbrCost / (WageRte * TranShkGrid_rep))**frac * xNowPow # The zero-th transitory shock is TranShk=0, and the solution is to not work: Lsr = 1, Lbr = 0. cNrmNow[:, 0] = uPinv(EndOfPrdvP.flatten()) LsrNow[:, 0] = 1.0 # Agent cannot choose to work a negative amount of time. When this occurs, set # leisure to one and recompute consumption using simplified first order condition. # Find where labor would be negative if unconstrained violates_labor_constraint = LsrNow > 1.0 EndOfPrdvP_temp = np.tile(np.reshape(EndOfPrdvP, (aXtraCount, 1)), (1, TranShkCount)) cNrmNow[violates_labor_constraint] = uPinv( EndOfPrdvP_temp[violates_labor_constraint]) LsrNow[violates_labor_constraint] = 1.0 # Set up z=1, upper limit # Calculate the endogenous bNrm states by inverting the within-period transition aNrmNow_rep = np.tile(np.reshape(aXtraGrid, (aXtraCount, 1)), (1, TranShkGrid.size)) bNrmNow = (aNrmNow_rep - WageRte * TranShkGrid_rep + cNrmNow + WageRte * TranShkGrid_rep * LsrNow) # Add an extra gridpoint at the absolute minimal valid value for b_t for each TranShk; # this corresponds to working 100% of the time and consuming nothing. bNowArray = np.concatenate((np.reshape(-WageRte * TranShkGrid, (1, TranShkGrid.size)), bNrmNow), axis=0) # Consume nothing cNowArray = np.concatenate((np.zeros((1, TranShkGrid.size)), cNrmNow), axis=0) # And no leisure! LsrNowArray = np.concatenate((np.zeros((1, TranShkGrid.size)), LsrNow), axis=0) LsrNowArray[0, 0] = 1.0 # Don't work at all if TranShk=0, even if bNrm=0 LbrNowArray = 1.0 - LsrNowArray # Labor is the complement of leisure # Get (pseudo-inverse) marginal value of bank balances using end of period # marginal value of assets (envelope condition), adding a column of zeros # zeros on the left edge, representing the limit at the minimum value of b_t. vPnvrsNowArray = np.concatenate((np.zeros( (1, TranShkGrid.size)), uPinv(EndOfPrdvP_temp))) # Construct consumption and marginal value functions for this period bNrmMinNow = LinearInterp(TranShkGrid, bNowArray[0, :]) # Loop over each transitory shock and make a linear interpolation to get lists # of optimal consumption, labor and (pseudo-inverse) marginal value by TranShk cFuncNow_list = [] LbrFuncNow_list = [] vPnvrsFuncNow_list = [] for j in range(TranShkGrid.size): # Adjust bNrmNow for this transitory shock, so bNrmNow_temp[0] = 0 bNrmNow_temp = bNowArray[:, j] - bNowArray[0, j] # Make consumption function for this transitory shock cFuncNow_list.append(LinearInterp(bNrmNow_temp, cNowArray[:, j])) # Make labor function for this transitory shock LbrFuncNow_list.append(LinearInterp(bNrmNow_temp, LbrNowArray[:, j])) # Make pseudo-inverse marginal value function for this transitory shock vPnvrsFuncNow_list.append( LinearInterp(bNrmNow_temp, vPnvrsNowArray[:, j])) # Make linear interpolation by combining the lists of consumption, labor and marginal value functions cFuncNowBase = LinearInterpOnInterp1D(cFuncNow_list, TranShkGrid) LbrFuncNowBase = LinearInterpOnInterp1D(LbrFuncNow_list, TranShkGrid) vPnvrsFuncNowBase = LinearInterpOnInterp1D(vPnvrsFuncNow_list, TranShkGrid) # Construct consumption, labor, pseudo-inverse marginal value functions with # bNrmMinNow as the lower bound. This removes the adjustment in the loop above. cFuncNow = VariableLowerBoundFunc2D(cFuncNowBase, bNrmMinNow) LbrFuncNow = VariableLowerBoundFunc2D(LbrFuncNowBase, bNrmMinNow) vPnvrsFuncNow = VariableLowerBoundFunc2D(vPnvrsFuncNowBase, bNrmMinNow) # Construct the marginal value function by "recurving" its pseudo-inverse vPfuncNow = MargValueFuncCRRA(vPnvrsFuncNow, CRRA) # Make a solution object for this period and return it solution = ConsumerLaborSolution(cFunc=cFuncNow, LbrFunc=LbrFuncNow, vPfunc=vPfuncNow, bNrmMin=bNrmMinNow) return solution