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lectures/dynamic_programming/coleman_policy_iter.md

@@ -404,7 +404,7 @@ function K!(Kg, g, grid, beta, dudc, f, f_prime, shocks)
     for (i, y) in enumerate(grid)
         function h(c)
             vals = dudc.(g_func.(f(y - c) * shocks)) .* f_prime(y - c) .* shocks
-            return dudc*c - beta * mean(vals)
+            return dudc * c - beta * mean(vals)
         end
         Kg[i] = find_zero(h, (1e-10, y - 1e-10))
     end
@@ -465,7 +465,7 @@ Here's an object containing data from the log-linear growth model we used in the
 
 ```{code-cell} julia
 isoelastic(c, gamma) = isone(gamma) ? log(c) : (c^(1 - gamma) - 1) / (1 - gamma)
-function Model(;alpha = 0.65,                            # Productivity parameter
+function Model(; alpha = 0.65,                            # Productivity parameter
                beta = 0.95,                            # Discount factor
                gamma = 1.0,                             # Risk aversion
                mu = 0.0,                             # First parameter in lognorm(mu, sigma)

lectures/dynamic_programming/mccall_model_with_separation.md

@@ -288,7 +288,7 @@ We'll use the default parameterizations found in the code above.
 u(c, sigma) = (c^(1 - sigma) - 1) / (1 - sigma)
 
 # model constructor
-function McCallModel(;alpha = 0.2,
+function McCallModel(; alpha = 0.2,
                      beta = 0.98, # discount rate
                      gamma = 0.7,
                      c = 6.0, # unemployment compensation

lectures/dynamic_programming/odu.md

@@ -591,7 +591,7 @@ phi_init = ones(sp.n_pi)
 g(x) = res_wage_operator(sp, x)
 w_bar_vals = compute_fixed_point(g, phi_init)
 w_bar = extrapolate(interpolate((sp.pi_grid,), w_bar_vals,
-                             Gridded(Linear())), Flat())
+                                Gridded(Linear())), Flat())
 
 # Holds the employment state and beliefs of an individual agent.
 mutable struct Agent{TF <: AbstractFloat, TI <: Integer}