Change Hints and Notes to Admonitions (#123)

* change to admonitions

* update additive function

* fix a small typo

* add space

Author: HumphreyYang

lectures/BCG_complete_mkts.md

@@ -710,7 +710,10 @@ $[w_0^1, w_0^2, w_1^1(\epsilon), w_1^2(\epsilon), \theta_0^1, \theta_0^2 ]$.
 **Remark:** Multiple arrangements of endowments
 $[w_0^1, w_0^2, w_1^1(\epsilon), w_1^2(\epsilon), \theta_0^1, \theta_0^2 ]$
 associated with the same distribution of wealth $\eta$. Can you explain why?
-**Hint:** Think about the portfolio indeterminacy finding above.
+
+```{hint}
+Think about the portfolio indeterminacy finding above.
+```
 
 ### Modigliani-Miller theorem
 

lectures/additive_functionals.md

@@ -1194,13 +1194,15 @@ Then let's use the plots to  investigate how these densities evolve through time
 
 We will plot the densities of $\log {\widetilde M}_t$ for different values of $t$.
 
-Note: `scipy.stats.lognorm` expects you to pass the standard deviation
+```{note}
+`scipy.stats.lognorm` expects you to pass the standard deviation
 first $(tH \cdot H)$ and then the exponent of the mean as a
 keyword argument `scale` (`scale=np.exp(-t * H2 / 2)`).
 
 * See the documentation [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.lognorm.html#scipy.stats.lognorm).
 
 This is peculiar, so make sure you are careful in working with the log normal distribution.
+```
 
 Here is some code that tackles these tasks
 

lectures/black_litterman.md

@@ -993,10 +993,12 @@ so no robustness to alternative distributions is acquired.
 
 As $\theta$ is lowered, more robustness is achieved.
 
-**Note:** The ${\sf T}$ operator is sometimes called a
+```{note}
+The ${\sf T}$ operator is sometimes called a
 *risk-sensitivity* operator.
+```
 
-We shall apply ${\sf T}$to the special case of a linear value
+We shall apply ${\sf T}$ to the special case of a linear value
 function $w'(\vec r - r_f 1)$
 where $\vec r - r_f 1 \sim {\mathcal N}(\mu,\Sigma)$ or
 $\vec r - r_f {\bf 1} = \mu + C \epsilon$ and

lectures/calvo.md

@@ -903,9 +903,11 @@ is a symptom of time inconsistency.
   past utilities and to reoptimize at date $t \geq 1$ would, if allowed, want
   to deviate from a Ramsey plan.
 
-**Note:** A modified Ramsey plan constructed under the restriction that
+```{note}
+A modified Ramsey plan constructed under the restriction that
 $\mu_t$ must be constant over time is time consistent (see
 $\check \mu$ and $\check \theta$ in the above graphs).
+```
 
 ### Meaning of Time Inconsistency
 

lectures/discrete_dp.md

@@ -440,10 +440,10 @@ For $R$ we set $R[s, a] = u(s - a)$ if $a \leq s$ and $-\infty$ otherwise.
 
 For $Q$ we follow the rule in {eq}`ddp_def_ogq`.
 
-Note:
-
+```{note}
 * The feasibility constraint is embedded into $R$ by setting $R[s, a] = -\infty$ for $a \notin A(s)$.
 * Probability distributions for $(s, a)$ with $a \notin A(s)$ can be arbitrary.
+```
 
 The following code sets up these objects for us
 

lectures/dyn_stack.md

@@ -1147,7 +1147,9 @@ the recursive formulation of the follower problem.
 
 Can you spot what features of $\tilde F$ imply this?
 
-Hint: remember the components of $X_t$
+```{hint}
+Remember the components of $X_t$
+```
 
 ```{code-cell} python3
 # Policy function in the follower's problem

lectures/estspec.md

@@ -207,7 +207,9 @@ $$
 
 can be computed by `np.abs(fft(X))**2 / len(X)`.
 
-Note: The NumPy function `abs` acts elementwise, and correctly handles complex numbers (by computing their modulus, which is exactly what we need).
+```{note}
+The NumPy function `abs` acts elementwise, and correctly handles complex numbers (by computing their modulus, which is exactly what we need).
+```
 
 A function called `periodogram` that puts all this together can be found [here](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/estspec.py).
 

lectures/lu_tricks.md

@@ -1029,7 +1029,9 @@ Express the solution in the "feedback form" {eq}`onefifteen`, giving numerical v
 
 Make sure that the boundary conditions {eq}`onefive` are satisfied.
 
-(Note: this problem differs from the problem in the text in one important way: instead of $h > 0$ in {eq}`oneone`, $h = 0$. This has an important influence on the solution.)
+```{note}
+This problem differs from the problem in the text in one important way: instead of $h > 0$ in {eq}`oneone`, $h = 0$. This has an important influence on the solution.
+```
 
 ```{exercise-end}
 ```

lectures/lucas_model.md

@@ -345,7 +345,9 @@ We now show that
 1. For any $f \in cb\mathbb{R}_+$, the sequence $T^k f$ converges
    uniformly to $f^*$.
 
-(Note: If you find the mathematics heavy going you can take 1--2 as given and skip to the {ref}`next section <lt_comp_eg>`)
+```{note}
+If you find the mathematics heavy going you can take 1--2 as given and skip to the {ref}`next section <lt_comp_eg>`
+```
 
 Recall the [Banach contraction mapping theorem](https://en.wikipedia.org/wiki/Banach_fixed-point_theorem).
 

lectures/muth_kalman.md

@@ -122,10 +122,12 @@ $$
 
 is an IID process.
 
-**Note:** A property of the state-space representation {eq}`state-space` is that in
+```{note}
+A property of the state-space representation {eq}`state-space` is that in
 general neither $\epsilon_{1,t}$ nor $\epsilon_{2,t}$ is in
 the space spanned by square-summable linear combinations of
 $y_t, y_{t-1}, \ldots$.
+```
 
 In general
 $\begin{bmatrix} \epsilon_{1,t} \cr \epsilon_{2t} \end{bmatrix}$
@@ -146,10 +148,14 @@ time-invariant *innovations representation*
 where $\hat x_t = E [x_t | y_{t-1}, y_{t-2}, \ldots ]$ and
 $a_t = y_t - E[y_t |y_{t-1}, y_{t-2}, \ldots ]$.
 
-**Note:** A key property about an *innovations representation* is that
+```{note}
+
+A key property about an *innovations representation* is that
 $a_t$ is in the space spanned by square summable linear
 combinations of $y_t, y_{t-1}, \ldots$.
 
+```
+
 For more ramifications of this property, see the lectures  {doc}`Shock Non-Invertibility <hs_invertibility_example>`  and
 {doc}`Recursive Models of Dynamic Linear Economies <hs_recursive_models>`.