Fix quotation mark inconsistencies in 5 lecture files

Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com>

Author: Copilot

lectures/ak2.md

@@ -226,7 +226,7 @@ r_t & = \alpha K_t^\alpha L_t^{1-\alpha}
 \end{aligned}
 $$  (eq:firmfonc)
 
-Output can  be consumed either by old people or young people; or sold to young people who use it  to augment the capital stock;  or  sold to  the government for  uses that do not generate utility for the people in the model  (i.e., ``it is thrown into the ocean'').  
+Output can  be consumed either by old people or young people; or sold to young people who use it  to augment the capital stock;  or  sold to  the government for  uses that do not generate utility for the people in the model  (i.e., "it is thrown into the ocean").  
 
 
 The firm  thus sells output to old people, young people, and the government.

lectures/ar1_turningpts.md

@@ -276,7 +276,7 @@ $$
 
 This is designed to express the event
 
-- ``after one or two decrease(s), $Y$ will grow for two consecutive quarters'' 
+- "after one or two decrease(s), $Y$ will grow for two consecutive quarters" 
 
 Following {cite}`wecker1979predicting`, we can use simulations to calculate  probabilities of $P_t$ and $N_t$ for each period $t$. 
 

lectures/exchangeable.md

@@ -262,7 +262,7 @@ So there is something to learn from the past about the future.
 ## Exchangeability
 
 While the sequence $W_0, W_1, \ldots$ is not IID, it can be verified that it is
-**exchangeable**, which means that the   joint distributions $h(W_0, W_1)$ and $h(W_1, W_0)$ of the ''re-ordered'' sequences
+**exchangeable**, which means that the   joint distributions $h(W_0, W_1)$ and $h(W_1, W_0)$ of the "re-ordered" sequences
 satisfy
 
 $$

lectures/likelihood_ratio_process.md

@@ -1725,7 +1725,7 @@ markov_results = analyze_markov_chains(P_f, P_g)
 Likelihood processes play an important role in Bayesian learning, as described in {doc}`likelihood_bayes`
 and as applied in {doc}`odu`.
 
-Likelihood ratio processes are central to  Lawrence Blume and David Easley's answer to their question ''If you're so smart, why aren't you rich?'' {cite}`blume2006if`, the subject of the lecture{doc}`likelihood_ratio_process_2`.
+Likelihood ratio processes are central to  Lawrence Blume and David Easley's answer to their question "If you're so smart, why aren't you rich?" {cite}`blume2006if`, the subject of the lecture{doc}`likelihood_ratio_process_2`.
 
 Likelihood ratio processes also appear  in {doc}`advanced:additive_functionals`, which contains another illustration of the **peculiar property** of likelihood ratio processes described above.
 

lectures/likelihood_ratio_process_2.md

@@ -29,7 +29,7 @@ kernelspec:
 ## Overview
 
 A likelihood ratio process lies behind Lawrence Blume and David Easley's answer to their question
-''If you're so smart, why aren't you rich?'' {cite}`blume2006if`.  
+"If you're so smart, why aren't you rich?" {cite}`blume2006if`.  
 
 Blume and Easley constructed formal models to study how differences of opinions about probabilities governing risky income processes would influence outcomes and be reflected in prices of stocks, bonds, and insurance policies that individuals use to share and hedge risks.
 
@@ -148,10 +148,10 @@ f = jit(lambda x: p(x, F_a, F_b))
 g = jit(lambda x: p(x, G_a, G_b))
 ```
 
-```{code-cell} ipython3
+`"{code-cell} ipython3
 @jit
 def simulate(a, b, T=50, N=500):
-    '''
+    "'
     Generate N sets of T observations of the likelihood ratio,
     return as N x T matrix.
     '''
@@ -294,7 +294,7 @@ $$ (eq:welfareW)
 
 where $\lambda \in [0,1]$ is a Pareto weight that tells how much the planner likes agent $1$ and $1 - \lambda$ is a Pareto weight that tells how much the social planner likes agent $2$.  
 
-Setting $\lambda = .5$ expresses ''egalitarian'' social preferences. 
+Setting $\lambda = .5$ expresses "egalitarian" social preferences. 
 
 Notice how social welfare criterion {eq}`eq:welfareW` takes into account both agents' preferences as represented by formula {eq}`eq:objectiveagenti`.
 
@@ -369,7 +369,7 @@ values of the likelihood ratio process $l_t(s^t)$:
 
 $$l_\infty (s^\infty) = 0; \quad c_\infty^1 = 0$$
 
-* In the above case, agent 2 is ''smarter'' than agent 1, and agent 1's share of the aggregate endowment converges to zero.
+* In the above case, agent 2 is "smarter" than agent 1, and agent 1's share of the aggregate endowment converges to zero.