Revert checked emphasis comments back to italic (batch 3)

Based on PR review feedback with checked [x] emphasis comments, reverted
the following terms from bold back to italic (emphasis, not definitions):

- linear_models.md: ergodicity (concept emphasis)
- markov_asset.md: tree, fruit, shares, dividend (metaphorical emphasis)
- mccall_model.md: values (concept emphasis)
- mle.md: parametric class (emphasis not definition)
- morris_learn.md: prior/posterior distributions, speculative behavior,
  ex dividend, Short sales, Harsanyi Common Priors Doctrine (emphasis)
- ols.md: exogenous, marginal effect (emphasis not definitions)
- rational_expectations.md: rational expectations equilibrium (intro emphasis),
  perceived/actual law of motion (intro emphasis, formal definitions come later)
- samuelson.md: second-order linear difference equation, national output identity,
  consumption function, accelerator, accelerator coefficient, aggregate demand/supply,
  random, stochastic, shocks, disturbances (emphasis not definitions)

These are emphasis on concepts or references, not formal definitions.

Author: mmcky

lectures/linear_models.md

@@ -1156,7 +1156,7 @@ $$
 
 Do these time series averages converge to something interpretable in terms of our basic state-space representation?
 
-The answer depends on something called **ergodicity**.
+The answer depends on something called *ergodicity*.
 
 Ergodicity is the property that time series and ensemble averages coincide.
 

lectures/markov_asset.md

@@ -451,8 +451,8 @@ Lucas considered an abstract pure exchange economy with these features:
 
 * a single non-storable consumption good
 * a Markov process that governs the total amount of the consumption good available each period
-* a single **tree** that each period yields **fruit** that equals the total amount of consumption available to the economy
-* a competitive market in  **shares** in the tree that entitles their owners to corresponding shares of the **dividend** stream, i.e., the **fruit** stream, yielded by the tree
+* a single *tree* that each period yields *fruit* that equals the total amount of consumption available to the economy
+* a competitive market in  *shares* in the tree that entitles their owners to corresponding shares of the *dividend* stream, i.e., the *fruit* stream, yielded by the tree
 
 * a representative consumer who in a competitive equilibrium
 

lectures/mccall_model.md

@@ -134,7 +134,7 @@ In order to optimally trade-off current and future rewards, we need to think abo
 1. the current payoffs we get from different choices
 1. the different states that those choices will lead to in next period
 
-To weigh these two aspects of the decision problem, we need to assign **values**
+To weigh these two aspects of the decision problem, we need to assign *values*
 to states.
 
 To this end, let $v^*(w)$ be the total lifetime value accruing to an

lectures/mle.md

@@ -74,7 +74,7 @@ Let's consider the steps we need to go through in maximum likelihood estimation
 
 The first step with maximum likelihood estimation is to choose the probability distribution believed to be generating the data.
 
-More precisely, we need to make an assumption as to which **parametric class** of distributions is generating the data.
+More precisely, we need to make an assumption as to which *parametric class* of distributions is generating the data.
 
 * e.g., the class of all normal distributions, or the class of all gamma distributions.
 

lectures/morris_learn.md

@@ -50,12 +50,12 @@ Key features of the environment in Morris's model include:
 * A single parameter indexes the set of statistical models 
 * All traders observe the same dividend history
 * All traders use Bayes' Law to update beliefs
-* Traders have different initial **prior distributions** over the parameter
-* Traders'  **posterior distributions** over the parameter eventually merge
+* Traders have different initial *prior distributions* over the parameter
+* Traders'  *posterior distributions* over the parameter eventually merge
 * Before their posterior distributions merge,  traders disagree about the  predictive density over prospective dividends 
     * therefore they disagree  about the value of the asset
 
-Just as in the hard-wired beliefs model of Harrison and Kreps, those differences of opinion induce  investors to  engage in **speculative behavior** in the following sense:
+Just as in the hard-wired beliefs model of Harrison and Kreps, those differences of opinion induce  investors to  engage in *speculative behavior* in the following sense:
 
  * sometimes they are willing to pay more for the asset than what they think is its "fundamental" value, i.e., the expected discounted value of its prospective dividend stream 
 
@@ -110,11 +110,11 @@ Traders buy and sell the risky asset in competitive markets each period $t = 0,
 
 As in Harrison-Kreps:
 
-* The asset is traded **ex dividend**
+* The asset is traded *ex dividend*
 * An owner of a share at the end of time $t$ is entitled to the dividend at time $t+1$
 * An owner of a share at the end of period $t$ also has the right to sell the share at time $t+1$ after having received the dividend at time $t+1$.
 
-**Short sales are prohibited**.
+*Short sales are prohibited*.
 
 This matters because it limits how pessimists can express their opinions:
 
@@ -151,7 +151,7 @@ reduce the set of models to a single model by imputing to all agents inside the
 A set of statistical models that has a particular geometric structure is called a [manifold](https://en.wikipedia.org/wiki/Manifold)  of statistical models. Morris endows traders with a shared  manifold of statistical models.
 ```
 
-Proceeding in this way adheres to  the **Harsanyi Common Priors Doctrine**.
+Proceeding in this way adheres to  the *Harsanyi Common Priors Doctrine*.
 
 
 

lectures/ols.md

@@ -51,7 +51,7 @@ As an example, we will replicate results from Acemoglu, Johnson and Robinson's s
 
 In the paper, the authors emphasize the importance of institutions in economic development.
 
-The main contribution is the use of settler mortality rates as a source of **exogenous** variation in institutional differences.
+The main contribution is the use of settler mortality rates as a source of *exogenous* variation in institutional differences.
 
 Such variation is needed to determine whether it is institutions that give rise to greater economic growth, rather than the other way around.
 
@@ -125,7 +125,7 @@ where:
 - $\beta_0$ is the intercept of the linear trend line on the
   y-axis
 - $\beta_1$ is the slope of the linear trend line, representing
-  the **marginal effect** of protection against risk on log GDP per
+  the *marginal effect* of protection against risk on log GDP per
   capita
 - $u_i$ is a random error term (deviations of observations from
   the linear trend due to factors not included in the model)

lectures/rational_expectations.md

@@ -39,7 +39,7 @@ tags: [hide-output]
 
 ## Overview
 
-This lecture introduces the concept of a **rational expectations equilibrium**.
+This lecture introduces the concept of a *rational expectations equilibrium*.
 
 To illustrate it, we describe a linear quadratic version of a  model
 due to Lucas and Prescott {cite}`LucasPrescott1971`.
@@ -53,7 +53,7 @@ Because we use linear quadratic setups for demand and costs, we can deploy the L
 We will learn about how a representative agent's problem differs from a planner's, and how a planning problem can be used to compute  quantities and prices in a rational expectations
 equilibrium.
 
-We will also learn about how a rational expectations equilibrium can be characterized as a [fixed point](https://en.wikipedia.org/wiki/Fixed_point_%28mathematics%29) of a mapping from a **perceived law of motion** to an **actual law of motion**.
+We will also learn about how a rational expectations equilibrium can be characterized as a [fixed point](https://en.wikipedia.org/wiki/Fixed_point_%28mathematics%29) of a mapping from a *perceived law of motion* to an *actual law of motion*.
 
 Equality between a perceived and an actual law of motion for endogenous market-wide objects captures in a nutshell what the rational expectations equilibrium concept is all about.
 

lectures/samuelson.md

@@ -66,21 +66,21 @@ from cmath import sqrt
 
 ### Samuelson's model
 
-Samuelson used a **second-order linear difference equation** to
+Samuelson used a *second-order linear difference equation* to
 represent a model of national output based on three components:
 
-- a **national output identity** asserting that national output or national income is the
+- a *national output identity* asserting that national output or national income is the
   sum of consumption plus investment plus government purchases.
-- a Keynesian **consumption function** asserting that consumption at
+- a Keynesian *consumption function* asserting that consumption at
   time $t$ is equal to a constant times national output at time $t-1$.
-- an investment **accelerator** asserting that investment at time
-  $t$ equals a constant called the **accelerator coefficient**
+- an investment *accelerator* asserting that investment at time
+  $t$ equals a constant called the *accelerator coefficient*
   times the difference in output between period $t-1$ and
   $t-2$.
 
 Consumption plus investment plus government purchases
-constitute **aggregate demand,** which automatically calls forth an
-equal amount of **aggregate supply**.
+constitute *aggregate demand,* which automatically calls forth an
+equal amount of *aggregate supply*.
 
 (To read about linear difference equations see [here](https://en.wikipedia.org/wiki/Linear_difference_equation) or chapter IX of {cite}`Sargent1987`.)
 
@@ -204,10 +204,10 @@ fluctuations by adding a random shock to aggregate demand.
 
 ### Stochastic version of the model
 
-We create a **random** or **stochastic** version of the model by adding
-a random process of **shocks** or **disturbances**
+We create a *random* or *stochastic* version of the model by adding
+a random process of *shocks* or *disturbances*
 $\{\sigma \epsilon_t \}$ to the right side of equation {eq}`second_order`,
-leading to the **second-order scalar linear stochastic difference equation**:
+leading to the *second-order scalar linear stochastic difference equation*:
 
 ```{math}
 :label: second_stochastic