updates; Trang's comments

lectures/multi_agent_models/aiyagari.md

@@ -226,7 +226,7 @@ Household(;r = 0.01,
     u = sigma == 1 ? x -> log(x) : x -> (x^(1 - sigma) - 1) / (1 - sigma),
     R = setup_R!(fill(-Inf, n, a_size), a_vals, s_vals, r, w, u),
     # -Inf is the utility of dying (0 consumption)
-    Q = setup_Q!(zeros(n, a_size, n), s_i_vals, z_chain)) = (; r, w, sigma, beta, z_chain, a_min, a_max, a_size, a_vals, z_size, n, s_vals, s_i_vals, u, R, Q)
+    Q = setup_Q!(zeros(n, a_size, n), s_i_vals, z_chain)) = (;r, w, sigma, beta, z_chain, a_min, a_max, a_size, a_vals, z_size, n, s_vals, s_i_vals, u, R, Q)
 
 function setup_Q!(Q, s_i_vals, z_chain)
     for next_s_i in 1:size(Q, 3)
@@ -368,7 +368,7 @@ k_vals = prices_to_capital_stock.(Ref(am), r_vals)
 
 # Plot against demand for capital by firms
 demand = rd.(k_vals)
-labels =  ["demand for capital" "supply of capital"]
+labels = ["demand for capital" "supply of capital"]
 plot(k_vals, [demand r_vals], label = labels, lw = 2, alpha = 0.6)
 plot!(xlabel = "capital", ylabel = "interest rate", xlim = (2, 14), ylim = (0.0, 0.1))
 ```

lectures/multi_agent_models/arellano.md

@@ -590,14 +590,14 @@ using DataFrames, Plots
 Compute the value function, policy and equilibrium prices
 
 ```{code-cell} julia
-ae = ArellanoEconomy(beta = .953,     # time discount rate
+ae = ArellanoEconomy(beta = .953,      # time discount rate
                      gamma = 2.,       # risk aversion
-                     r = 0.017,    # international interest rate
-                     rho = .945,     # persistence in output
-                     eta = 0.025,    # st dev of output shock
+                     r = 0.017,        # international interest rate
+                     rho = .945,       # persistence in output
+                     eta = 0.025,      # st dev of output shock
                      theta = 0.282,    # prob of regaining access
-                     ny = 21,      # number of points in y grid
-                     nB = 251)     # number of points in B grid
+                     ny = 21,          # number of points in y grid
+                     nB = 251)         # number of points in B grid
 
 # now solve the model on the grid.
 vfi!(ae)

lectures/multi_agent_models/lake_model.md

@@ -221,7 +221,7 @@ function transition_matrices(lm)
     A = [(1 - lambda) * (1 - d) + b      (1 - d) * alpha + b
         (1 - d) * lambda                 (1 - d) * (1 - alpha)]
     A_hat = A ./ (1 + g)
-    return (A, A_hat)
+    return (;A, A_hat)
 end
 
 function rate_steady_state(lm)
@@ -630,7 +630,7 @@ Following {cite}`davis2006flow`, we set $\alpha$, the hazard rate of leaving emp
 We will make use of (with some tweaks) the code we wrote in the {doc}`McCall model lecture <../dynamic_programming/mccall_model>`, embedded below for convenience.
 
 ```{code-cell} julia
-function solve_mccall_model(mcm; U_iv = 1.0, V_iv = ones(length(mcm.w)), tol = 1e-5,iter = 2_000)
+function solve_mccall_model(mcm; U_iv = 1.0, V_iv = ones(length(mcm.w)), tol = 1e-5, iter = 2_000)
     (;alpha, beta, sigma, c, gamma, w, E, u) = mcm
 
     # necessary objects
@@ -671,13 +671,14 @@ u(c, sigma) = c > 0 ? (c^(1 - sigma) - 1) / (1 - sigma) : -10e-6
 
 # model constructor
 McCallModel(;alpha = 0.2, 
-            beta = 0.98,                            # discount rate
+            beta = 0.98,                                   # discount rate
             gamma = 0.7,
-            c = 6.0,                                # unemployment compensation
+            c = 6.0,                                       # unemployment compensation
             sigma = 2.0,
-            u = u,                                  # utility function
-            w = range(10, 20, length = 60),         # w= wage values; E= distribution over wage values
-            E = Expectation(BetaBinomial(59, 600, 400))) = (;alpha, beta, gamma, c, sigma, u, w, E)
+            u = u,                                         # utility function
+            w = range(10, 20, length = 60),                # w= wage values
+            E = Expectation(BetaBinomial(59, 600, 400))) = # E = distribution over wage values
+            (;alpha, beta, gamma, c, sigma, u, w, E)
             
 ```
 

lectures/multi_agent_models/lucas_model.md

@@ -413,7 +413,7 @@ function LucasTree(;gamma = 2.0,
         h[i] = beta * mean((y^alpha .* shocks).^(1 - gamma))
     end
 
-    return (gamma = gamma, beta = beta, alpha = alpha, sigma = sigma, phi = phi, grid = grid, shocks = shocks, h = h)
+    return (;gamma, beta, alpha, sigma, phi, grid, shocks, h)
 end
 
 # approximate Lucas operator, which returns the updated function Tf on the grid

lectures/multi_agent_models/markov_perf.md

@@ -440,13 +440,13 @@ A  = I + zeros(3, 3)
 B1 = [0.0, 1.0, 0.0]
 B2 = [0.0, 0.0, 1.0]
 
-R1 = [      0.0   -a0 / 2.0          0.0;
-    -a0 / 2.0          a1     a1 / 2.0;
-            0.0    a1 / 2.0          0.0]
+R1 = [ 0.0      -a0 / 2.0      0.0;
+    -a0 / 2.0       a1       a1 / 2.0;
+       0.0       a1 / 2.0      0.0    ]
 
-R2 = [      0.0          0.0      -a0 / 2.0;
-            0.0          0.0       a1 / 2.0;
-    -a0 / 2.0     a1 / 2.0             a1]
+R2 = [ 0.0         0.0      -a0 / 2.0;
+       0.0         0.0       a1 / 2.0;
+    -a0 / 2.0    a1 / 2.0      a1     ]
 
 Q1 = Q2 = gamma
 S1 = S2 = W1 = W2 = M1 = M2 = 0.0
@@ -480,8 +480,8 @@ In particular, let's take F2 as computed above, plug it into {eq}`eq_mpe_p1p` an
 We hope that the resulting policy will agree with F1 as computed above
 
 ```{code-cell} julia
-Lamda1 = A - (B2 * F2)
-lq1 = QuantEcon.LQ(Q1, R1, Lamda1, B1, bet=beta)
+Lambda1 = A - (B2 * F2)
+lq1 = QuantEcon.LQ(Q1, R1, Lambda1, B1, bet=beta)
 P1_ih, F1_ih, d = stationary_values(lq1)
 F1_ih
 ```
@@ -493,7 +493,7 @@ tags: [remove-cell]
 @testset begin
   @test P1_ih[2, 2] ≈ 5.441368459897164
   @test d ≈ 0.0
-  @test Lamda1[1, 1] ≈ 1.0 && Lamda1[3, 2] ≈ -0.07584666305807419
+  @test Lambda1[1, 1] ≈ 1.0 && Lambda1[3, 2] ≈ -0.07584666305807419
   @test F1_ih ≈ [-0.6684661291052371 0.29512481789806305 0.07584666292394007]
   @test isapprox(F1, F1_ih, atol=1e-7) # Make sure the test below comes up true.
 end
@@ -860,9 +860,9 @@ we set up the matrices as follows:
 
 ```{code-cell} julia
 # create matrices needed to compute the Nash feedback equilibrium
-A = [delta_1     0   -delta_1 * b[1];
-       0   delta_1   -delta_1 * b[2];
-       0     0             1]
+A = [delta_1     0      -delta_1 * b[1];
+       0      delta_1   -delta_1 * b[2];
+       0        0                1      ]
 
 B1 = delta_1 * [1 -D[1, 1];
             0 -D[2, 1];

lectures/multi_agent_models/rational_expectations.md

@@ -670,9 +670,9 @@ kappa0 = 95.5
 kappa1 = 0.95
 
 # formulate the LQ problem
-A = [1  0  0
+A = [1    0     0
      0 kappa1 kappa0
-     0  0  1]
+     0    0     1   ]
 
 B = [1.0, 0.0, 0.0]
 

lectures/multi_agent_models/schelling.md

@@ -8,7 +8,6 @@ kernelspec:
   language: julia
   name: julia-1.9
 ---
-##AT THIS ONE BUT LUCAS 
 (schelling)=
 ```{raw} html
 <div id="qe-notebook-header" style="text-align:right;">

lectures/multi_agent_models/uncertainty_traps.md

@@ -236,16 +236,16 @@ using DataFrames, LaTeXStrings, Parameters, Plots
 ```
 
 ```{code-cell} julia
-UncertaintyTrapEcon(;a = 1.5, # risk aversion
-                    gamma_x = 0.5, # production shock precision
-                    rho = 0.99, # correlation coefficient for theta
-                    sigma_theta = 0.5, # standard dev. of theta shock
-                    num_firms = 100, # number of firms
-                    sigma_F = 1.5, # standard dev. of fixed costs
-                    c = -420.0, # external opportunity cost
-                    mu_init = 0.0, # initial value for mu
-                    gamma_init = 4.0, # initial value for gamma
-                    theta_init = 0.0, # initial value for theta
+UncertaintyTrapEcon(;a = 1.5,                       # risk aversion
+                    gamma_x = 0.5,                  # production shock precision
+                    rho = 0.99,                     # correlation coefficient for theta
+                    sigma_theta = 0.5,              # standard dev. of theta shock
+                    num_firms = 100,                # number of firms
+                    sigma_F = 1.5,                  # standard dev. of fixed costs
+                    c = -420.0,                     # external opportunity cost
+                    mu_init = 0.0,                  # initial value for mu
+                    gamma_init = 4.0,               # initial value for gamma
+                    theta_init = 0.0,               # initial value for theta
                     sigma_x = sqrt(a / gamma_x))  = # standard dev. of shock
                     (;a, gamma_x, rho, sigma_theta, num_firms, sigma_F, c, mu_init, gamma_init, theta_init, sigma_x)
 ```