Merge pull request #9 from QuantEcon/update-links

Update Links to `myst` Format

lectures/_config.yml

@@ -89,6 +89,9 @@ sphinx:
       launch_buttons:
         colab_url                 : https://colab.research.google.com
     intersphinx_mapping:
+      pyprog:
+        - https://python-programming.quantecon.org/
+        - null
       intro: 
         - https://intro.quantecon.org/
         - null

lectures/aiyagari.md

@@ -196,7 +196,7 @@ In reading the code, the following information will be helpful
 * `R` needs to be a matrix where `R[s, a]` is the reward at state `s` under action `a`.
 * `Q` needs to be a three-dimensional array where `Q[s, a, s']` is the probability of transitioning to state `s'` when the current state is `s` and the current action is `a`.
 
-(A more detailed discussion of `DiscreteDP` is available in the [Discrete State Dynamic Programming](https://python-advanced.quantecon.org/discrete_dp.html) lecture in the [Advanced
+(A more detailed discussion of `DiscreteDP` is available in the {doc}`Discrete State Dynamic Programming <tools:discrete_dp>` lecture in the [Advanced
 Quantitative Economics with Python](https://python-advanced.quantecon.org) lecture series.)
 
 Here we take the state to be $s_t := (a_t, z_t)$, where $a_t$ is assets and $z_t$ is the shock.

lectures/cass_koopmans_1.md

@@ -30,7 +30,7 @@ This lecture and {doc}`Cass-Koopmans Competitive Equilibrium <cass_koopmans_2>`
 and David Cass {cite}`Cass` used to analyze optimal growth.
 
 The model can be viewed as an extension of the model of Robert Solow
-described in [an earlier lecture](https://python-programming.quantecon.org/python_oop.html)
+described in {doc}`an earlier lecture <pyprog:python_oop>`
 but adapted to make the saving rate be a choice.
 
 (Solow assumed a constant saving rate determined outside the model.)

lectures/cass_koopmans_2.md

@@ -49,8 +49,8 @@ The present lecture uses  additional  ideas including
   problem and the Hicks-Arrow prices.
 - A **Big** $K$ **, little** $k$ trick widely used in
   macroeconomic dynamics.
-    * We shall encounter this trick in [this lecture](https://python.quantecon.org/rational_expectations.html)
-      and also in [this lecture](https://python-advanced.quantecon.org/dyn_stack.html).
+    * We shall encounter this trick in {doc}`this lecture <eqm:rational_expectations>`
+      and also in {doc}`this lecture <dps:dyn_stack>`.
 - A non-stochastic version of a theory of the **term structure of
   interest rates**.
 - An intimate connection between  two
@@ -424,8 +424,8 @@ price system.
 
 ```{note}
 This allocation will constitute the **Big** $K$  to be in the present instance of the **Big** $K$ **, little** $k$ trick
-that we'll apply to  a competitive equilibrium in the spirit of [this lecture](https://python.quantecon.org/rational_expectations.html)
-and  [this lecture](https://python-advanced.quantecon.org/dyn_stack.html).
+that we'll apply to  a competitive equilibrium in the spirit of {doc}`this lecture <eqm:rational_expectations>`
+and  {doc}`this lecture <dps:dyn_stack>`.
 ```
 
 In particular, we shall use the following procedure:

lectures/matsuyama.md

@@ -336,7 +336,7 @@ conditions is not trivial.
 
 In order to make our code fast, we will use just in time compiled functions that will get called and handled by our class.
 
-These are the `@jit` statements that you see below (review [this lecture](https://python-programming.quantecon.org/numba.html) if you don't recall how to use JIT compilation).
+These are the `@jit` statements that you see below (review {doc}`this lecture <pyprog:numba>` if you don't recall how to use JIT compilation).
 
 Here's the main body of code
 

lectures/rational_expectations.md

@@ -591,8 +591,8 @@ If there were a unit measure of  identical competitive firms all behaving accord
 :class: dropdown
 ```
 
-To map a problem into a [discounted optimal linear control
-problem](https://python.quantecon.org/lqcontrol.html), we need to define
+To map a problem into a {doc}`discounted optimal linear control
+problem <dle:lqcontrol>`, we need to define
 
 - state vector $x_t$ and control vector $u_t$
 - matrices $A, B, Q, R$ that define preferences and the law of

lectures/troubleshooting.md

@@ -33,7 +33,7 @@ The basic assumption of the lectures is that code in a lecture should execute wh
 1. it is executed in a Jupyter notebook and
 1. the notebook is running on a machine with the latest version of Anaconda Python.
 
-You have installed Anaconda, haven't you, following the instructions in [this lecture](https://python-programming.quantecon.org/getting_started.html)?
+You have installed Anaconda, haven't you, following the instructions in {doc}`this lecture <pyprog:getting_started>`?
 
 Assuming that you have, the most common source of problems for our readers is that their Anaconda distribution is not up to date.
 

lectures/two_auctions.md

@@ -13,7 +13,7 @@ kernelspec:
 
 # First-Price and Second-Price Auctions
 
-This lecture is designed to set the stage for a subsequent lecture about [Multiple Good Allocation Mechanisms](https://python.quantecon.org/house_auction.html)
+This lecture is designed to set the stage for a subsequent lecture about {doc}`Multiple Good Allocation Mechanisms <eqm:house_auction>`
 
 In that lecture, a planner or auctioneer simultaneously allocates several goods to set of people.