FIX: Adjust for quantecon==0.6.0 (#296)

* FIX: Adjust for quantecon==0.6.0

* fix linkchecker

* fix: n needs to be defined

Author: mmcky

lectures/markov_asset.md

@@ -53,7 +53,7 @@ An asset is a claim on one or more future payoffs.
 
 The spot price of an asset depends primarily on
 
-* the anticipated  income stream  
+* the anticipated  income stream
 * attitudes about risk
 * rates of time preference
 
@@ -313,7 +313,8 @@ The next figure shows a simulation, where
 * $g_t = \exp(X_t)$, so that $\ln g_t = X_t$ is the growth rate.
 
 ```{code-cell} ipython
-mc = qe.tauchen(0.96, 0.25, n=25)
+n = 7
+mc = qe.tauchen(n, 0.96, 0.25)
 sim_length = 80
 
 x_series = mc.simulate(sim_length, init=np.median(mc.state_values))
@@ -404,7 +405,7 @@ Here's the code, including a test of the spectral radius condition
 ```{code-cell} python3
 n = 25  # Size of state space
 β = 0.9
-mc = qe.tauchen(0.96, 0.02, n=n)
+mc = qe.tauchen(n, 0.96, 0.02)
 
 K = mc.P * np.exp(mc.state_values)
 
@@ -566,7 +567,7 @@ class AssetPriceModel:
         if mc is None:
             self.ρ = 0.9
             self.σ = 0.02
-            self.mc = qe.tauchen(self.ρ, self.σ, n=25)
+            self.mc = qe.tauchen(n, self.ρ, self.σ)
         else:
             self.mc = mc
 
@@ -962,7 +963,7 @@ $$
 Consider the following primitives
 
 ```{code-cell} python3
-n = 5
+n = 5  # Size of State Space
 P = np.full((n, n), 0.0125)
 P[range(n), range(n)] += 1 - P.sum(1)
 # State values of the Markov chain

lectures/mix_model.md

@@ -236,7 +236,7 @@ in conjunction with the `numpy.searchsorted` command to sample from $H$ directly
 See <https://numpy.org/doc/stable/reference/generated/numpy.searchsorted.html> for the
 `searchsorted` function.
 
-See the [Mr. P Solver video on Monte Carlo simulation](https://www.google.com/search?client=firefox-b-1-d&q=Mr+P+solver+Monte+Carlo#fpstate=ive&vld=cid:bdcddc9f,vid:U00Kseb6SB4) to see other applications of this powerful trick. 
+See the [Mr. P Solver video on Monte Carlo simulation](https://www.google.com/search?q=Mr.+P+Solver+video+on+Monte+Carlo+simulation&oq=Mr.+P+Solver+video+on+Monte+Carlo+simulation) to see other applications of this powerful trick. 
 
 In the Python code below, we'll use both of our methods and confirm that each of them does a good job of sampling
 from our target mixture distribution.