Update Links to myst Format

lectures/BCG_complete_mkts.md

@@ -58,8 +58,8 @@ This simplification of BCG’s setup helps us by
 - introducing `Big K, little k` issues in a simple context that will
   recur in the BCG incomplete markets environment
 
-A Big K, little k analysis also played roles in [this quantecon lecture](https://python.quantecon.org/cass_koopmans_1.html) as well  as
-[here](https://python.quantecon.org/rational_expectations.html) and {doc}`here <dps:dyn_stack>`.
+A Big K, little k analysis also played roles in {doc}`this quantecon lecture <eqm:cass_koopmans_1>` as well  as
+{doc}`here <eqm:rational_expectations>` and {doc}`here <dps:dyn_stack>`.
 
 ### Setup
 

lectures/_config.yml

@@ -88,6 +88,9 @@ 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/asset_pricing_lph.md

@@ -58,9 +58,9 @@ We'll also describe how  practitioners have implemented the model using
 * time series of returns on various assets
 
 
-For background and basic  concepts about linear least squares projections, see our lecture [orthogonal projections and their applications](https://python-advanced.quantecon.org/orth_proj.html).
+For background and basic  concepts about linear least squares projections, see our lecture {doc}`orthogonal projections and their applications <dynam:orth_proj>`.
 
-As a sequel to the material here, please see our lecture [two modifications of mean-variance portfolio theory](https://python-advanced.quantecon.org/black_litterman.html).
+As a sequel to the material here, please see our lecture {doc}`two modifications of mean-variance portfolio theory <dynam:black_litterman>`.
 
 ## Key Equation
 
@@ -94,10 +94,10 @@ factor $m \geq 0$.
 In order to say something about the **uniqueness** of a stochastic discount factor, we  would have to impose more theoretical structure than we do in this
 lecture.
 
-For example, in **complete markets** models like those illustrated in this lecture [equilibrium capital structures with incomplete markets](https://python-advanced.quantecon.org/BCG_incomplete_mkts.html),
+For example, in **complete markets** models like those illustrated in this lecture {doc}`equilibrium capital structures with incomplete markets <dynam:BCG_incomplete_mkts>`,
 the stochastic discount factor is unique.
 
-In **incomplete markets** models like those illustrated in this lecture [the Aiyagari model](https://python.quantecon.org/aiyagari.html), the stochastic discount factor is not unique.
+In **incomplete markets** models like those illustrated in this lecture {doc}`the Aiyagari model <eqm:aiyagari>`, the stochastic discount factor is not unique.
 
 
 ## Implications of Key Equation

lectures/black_litterman.md

@@ -29,15 +29,15 @@ kernelspec:
 
 ## Overview
 
-This lecture describes extensions to the classical mean-variance portfolio theory summarized in our lecture [Elementary Asset Pricing Theory](https://python-advanced.quantecon.org/asset_pricing_lph.html).
+This lecture describes extensions to the classical mean-variance portfolio theory summarized in our lecture {doc}`Elementary Asset Pricing Theory <dynam:asset_pricing_lph>`.
 
 The classic theory described there assumes that a decision maker completely trusts the statistical model that he posits to govern the joint distribution of returns on a list of available assets.
 
 Both extensions described here put distrust of that statistical model into the mind of the decision maker.
 
 One is a model of Black and Litterman {cite}`black1992global` that imputes to the decision maker distrust of historically estimated mean returns but still complete trust of estimated covariances of returns.
 
-The second model also imputes to the decision maker doubts about his  statistical model, but now by saying that, because of that distrust,  the decision maker uses a version of  robust control theory described in this lecture [Robustness](https://python-advanced.quantecon.org/robustness.html).
+The second model also imputes to the decision maker doubts about his  statistical model, but now by saying that, because of that distrust,  the decision maker uses a version of  robust control theory described in this lecture {doc}`Robustness <tools:robustness>`.
 
 
 The famous **Black-Litterman** (1992) {cite}`black1992global` portfolio choice model was motivated by the finding that with high frequency or

lectures/lake_model.md

@@ -914,7 +914,7 @@ This is safer and means we don't need to create a fresh instance for every new p
 
 In this exercise, your task is to arrange the `LakeModel` class by using descriptors and decorators such as `@property`.
 
-(If you need to refresh your understanding of how these work, consult [this lecture](https://python-programming.quantecon.org/python_advanced_features.html).)
+(If you need to refresh your understanding of how these work, consult {doc}`this lecture <pyprog:python_advanced_features>`.)
 ```
 
 ```{solution-start} lm_ex1

lectures/markov_asset.md

@@ -173,7 +173,7 @@ It is useful to regard equation {eq}`lteeqs102`   as a generalization of equatio
 
 Equation {eq}`lteeqs102` asserts that the covariance of the stochastic discount factor with the one period payout $d_{t+1} + p_{t+1}$ is an important determinant of the price $p_t$.
 
-We give examples of some models of stochastic discount factors that have been proposed later in this lecture and also in a [later lecture](https://python-advanced.quantecon.org/lucas_model.html).
+We give examples of some models of stochastic discount factors that have been proposed later in this lecture and also in a {doc}`later lecture <dynam:lucas_model>`.
 
 ### The Price-Dividend Ratio
 
@@ -470,7 +470,7 @@ m_{t+1} = \beta \frac{u'(c_{t+1})}{u'(c_t)}
 
 where $u$ is a concave utility function and $c_t$ is time $t$ consumption of a representative consumer.
 
-(A derivation of this expression is given in a [later lecture](https://python-advanced.quantecon.org/lucas_model.html))
+(A derivation of this expression is given in a {doc}`later lecture <dynam:lucas_model>`)
 
 Assume the existence of an endowment that follows growth process {eq}`mass_fmce`.
 

lectures/mccall_model.md

@@ -367,7 +367,7 @@ plt.show()
 
 We are going to use Numba to accelerate our code.
 
-* See, in particular, the discussion of `@jitclass` in [our lecture on Numba](https://python-programming.quantecon.org/numba.html).
+* See, in particular, the discussion of `@jitclass` in {doc}`our lecture on Numba <pyprog:numba>`.
 
 The following helps Numba by providing some type
 

lectures/odu.md

@@ -35,10 +35,10 @@ tags: [hide-output]
 
 ## Overview
 
-In this lecture, we consider an extension of the [previously studied](https://python.quantecon.org/mccall_model.html) job search model of McCall
+In this lecture, we consider an extension of the {doc}`previously studied <dynam:mccall_model>` job search model of McCall
 {cite}`McCall1970`.
 
-We'll build on a model of Bayesian learning discussed in [this lecture](https://python.quantecon.org/exchangeable.html) on the topic of exchangeability and its relationship to
+We'll build on a model of Bayesian learning discussed in {doc}`this lecture <stats:exchangeable>` on the topic of exchangeability and its relationship to
 the concept of IID (identically and independently distributed) random variables and to  Bayesian updating.
 
 In the McCall model, an unemployed worker decides when to accept a
@@ -851,7 +851,7 @@ In this appendix we provide more details about how Bayes' Law contributes to the
 
 We present some graphs that bring out additional insights about how learning works.
 
-We build on graphs proposed in [this lecture](https://python.quantecon.org/exchangeable.html).
+We build on graphs proposed in {doc}`this lecture <stats:exchangeable>`.
 
 In particular, we'll add actions of  our searching worker to a key graph
 presented in that lecture.
@@ -1061,7 +1061,7 @@ $F$ ~ Beta(1, 1), $G$ ~ Beta(3, 1.2), $c$=0.3.
 In the graphs below, the red arrows in the upper right figure show how $\pi_t$ is updated in response to the
 new information $w_t$.
 
-Recall the following formula from [this lecture](https://python.quantecon.org/exchangeable.html)
+Recall the following formula from {doc}`this lecture <stats:exchangeable>`
 
 $$
 \frac{\pi_{t+1}}{\pi_{t}}=\frac{l\left(w_{t+1}\right)}{\pi_{t}l\left(w_{t+1}\right)+\left(1-\pi_{t}\right)}\begin{cases}

lectures/optgrowth.md

@@ -197,7 +197,7 @@ In other words, a feasible consumption policy is a Markov policy that respects t
 
 The set of all feasible consumption policies will be denoted by $\Sigma$.
 
-Each $\sigma \in \Sigma$ determines a [continuous state Markov process](https://python-advanced.quantecon.org/stationary_densities.html) $\{y_t\}$ for output via
+Each $\sigma \in \Sigma$ determines a {doc}`continuous state Markov process <tools:stationary_densities>` $\{y_t\}$ for output via
 
 ```{math}
 :label: firstp0_og2

lectures/optgrowth_fast.md

@@ -112,7 +112,7 @@ As before, we will be able to compare with the true solutions
 
 We will again store the primitives of the optimal growth model in a class.
 
-But now we are going to use [Numba's](https://python-programming.quantecon.org/numba.html) `@jitclass` decorator to target our class for JIT compilation.
+But now we are going to use {doc}`Numba's <pyprog:numba>` `@jitclass` decorator to target our class for JIT compilation.
 
 Because we are going to use Numba to compile our class, we need to specify the data types.
 

lectures/samuelson.md

@@ -36,7 +36,7 @@ tags: [hide-output]
 
 This lecture creates non-stochastic and stochastic versions of Paul Samuelson's celebrated multiplier accelerator model {cite}`Samuelson1939`.
 
-In doing so, we extend the example of the Solow model class in [our second OOP lecture](https://python-programming.quantecon.org/python_oop.html).
+In doing so, we extend the example of the Solow model class in {doc}`our second OOP lecture <pyprog:python_oop>`.
 
 Our objectives are to
 
@@ -384,7 +384,7 @@ We use the Samuelson multiplier-accelerator model as a vehicle for teaching how
 
 We want to have a method in the class that automatically generates a simulation, either non-stochastic ($\sigma=0$) or stochastic ($\sigma > 0$).
 
-We also show how to map the Samuelson model into a simple instance of the `LinearStateSpace` class described [here](https://python.quantecon.org/linear_models.html).
+We also show how to map the Samuelson model into a simple instance of the `LinearStateSpace` class described {doc}`here <dle:linear_models>`.
 
 We can use a `LinearStateSpace` instance to do various things that we did above with our homemade function and class.
 

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.