From e9ac46ad88a2f9e2a12371507b2d5a9d83de1274 Mon Sep 17 00:00:00 2001 From: Humphrey Yang Date: Wed, 31 Jan 2024 16:29:01 +1100 Subject: [PATCH] update links to sphinx syntax --- lectures/BCG_complete_mkts.md | 4 ++-- lectures/_config.yml | 3 +++ lectures/asset_pricing_lph.md | 8 ++++---- lectures/black_litterman.md | 4 ++-- lectures/lake_model.md | 2 +- lectures/markov_asset.md | 4 ++-- lectures/mccall_model.md | 2 +- lectures/odu.md | 8 ++++---- lectures/optgrowth.md | 2 +- lectures/optgrowth_fast.md | 2 +- lectures/samuelson.md | 4 ++-- lectures/troubleshooting.md | 2 +- 12 files changed, 24 insertions(+), 21 deletions(-) diff --git a/lectures/BCG_complete_mkts.md b/lectures/BCG_complete_mkts.md index 242e143..7bacd86 100644 --- a/lectures/BCG_complete_mkts.md +++ b/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 `. +A Big K, little k analysis also played roles in {doc}`this quantecon lecture ` as well as +{doc}`here ` and {doc}`here `. ### Setup diff --git a/lectures/_config.yml b/lectures/_config.yml index c71f2f4..2d50f5e 100644 --- a/lectures/_config.yml +++ b/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 diff --git a/lectures/asset_pricing_lph.md b/lectures/asset_pricing_lph.md index f24f543..e157d1e 100644 --- a/lectures/asset_pricing_lph.md +++ b/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 `. -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 `. ## 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 `, 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 `, the stochastic discount factor is not unique. ## Implications of Key Equation diff --git a/lectures/black_litterman.md b/lectures/black_litterman.md index 35d6250..25e90d1 100644 --- a/lectures/black_litterman.md +++ b/lectures/black_litterman.md @@ -29,7 +29,7 @@ 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 `. 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. @@ -37,7 +37,7 @@ Both extensions described here put distrust of that statistical model into the m 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 `. The famous **Black-Litterman** (1992) {cite}`black1992global` portfolio choice model was motivated by the finding that with high frequency or diff --git a/lectures/lake_model.md b/lectures/lake_model.md index 12deff0..7f8969e 100644 --- a/lectures/lake_model.md +++ b/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 `.) ``` ```{solution-start} lm_ex1 diff --git a/lectures/markov_asset.md b/lectures/markov_asset.md index af7f957..064902f 100644 --- a/lectures/markov_asset.md +++ b/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 `. ### 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 `) Assume the existence of an endowment that follows growth process {eq}`mass_fmce`. diff --git a/lectures/mccall_model.md b/lectures/mccall_model.md index 241a614..6fb1d21 100644 --- a/lectures/mccall_model.md +++ b/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 `. The following helps Numba by providing some type diff --git a/lectures/odu.md b/lectures/odu.md index 0bf08ad..f048db0 100644 --- a/lectures/odu.md +++ b/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 ` 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 ` 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 `. 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 ` $$ \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} diff --git a/lectures/optgrowth.md b/lectures/optgrowth.md index 119765d..1e2eb63 100644 --- a/lectures/optgrowth.md +++ b/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 ` $\{y_t\}$ for output via ```{math} :label: firstp0_og2 diff --git a/lectures/optgrowth_fast.md b/lectures/optgrowth_fast.md index 0feedfb..bb97ba1 100644 --- a/lectures/optgrowth_fast.md +++ b/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 ` `@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. diff --git a/lectures/samuelson.md b/lectures/samuelson.md index 2ab9120..d166146 100644 --- a/lectures/samuelson.md +++ b/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 `. 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 `. We can use a `LinearStateSpace` instance to do various things that we did above with our homemade function and class. diff --git a/lectures/troubleshooting.md b/lectures/troubleshooting.md index e68f030..60b999a 100644 --- a/lectures/troubleshooting.md +++ b/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 `? Assuming that you have, the most common source of problems for our readers is that their Anaconda distribution is not up to date.