Fix broken links and redirects in documentation with API endpoint correction (#404)

* Initial plan

* Fix broken Thomas Sargent link and update redirect URLs

Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com>

* Update Thomas Sargent URL to use http:// instead of removing it

Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com>

* Revert FRED URL in requests.get() call to fix broken Python code execution

Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: mmcky <8263752+mmcky@users.noreply.github.com>

Author: Copilot

lectures/_config.yml

@@ -33,7 +33,7 @@ sphinx:
     html_theme_options:
       authors:
         - name: Thomas J. Sargent
-          url: https://www.tomsargent.com/
+          url: http://www.tomsargent.com/
         - name: John Stachurski
           url: https://johnstachurski.net/      
       dark_logo: quantecon-logo-transparent.png

lectures/about_py.md

@@ -311,7 +311,7 @@ But when we want to work with larger arrays in real programs we need more effici
 For this we need to use libraries for working with arrays.
 
 For Python, the most important matrix and array processing library is
-[NumPy](https://www.numpy.org/) library.
+[NumPy](https://numpy.org/) library.
 
 For example, let's build a NumPy array with 100 elements
 
@@ -365,7 +365,7 @@ This lecture series will provide you with extensive background in NumPy.
 
 ### SciPy
 
-The [SciPy](https://www.scipy.org) library is built on top of NumPy and provides additional functionality.
+The [SciPy](https://scipy.org/) library is built on top of NumPy and provides additional functionality.
 
 (tuple_unpacking_example)=
 For example, let's calculate $\int_{-2}^2 \phi(z) dz$ where $\phi$ is the standard normal density.

lectures/numpy.md

@@ -1165,7 +1165,7 @@ np.linalg.inv(A)           # Compute the inverse
 ```{index} single: Python; SciPy
 ```
 
-Much of this functionality is also available in [SciPy](https://www.scipy.org/), a collection of modules that are built on top of NumPy.
+Much of this functionality is also available in [SciPy](https://scipy.org/), a collection of modules that are built on top of NumPy.
 
 We'll cover the SciPy versions in more detail {doc}`soon <scipy>`.
 

lectures/pandas.md

@@ -47,7 +47,7 @@ Here's a popularity comparison over time against Matlab and STATA courtesy of St
 :scale: 100
 ```
 
-Just as [NumPy](https://www.numpy.org/) provides the basic array data type plus core array operations, pandas
+Just as [NumPy](https://numpy.org/) provides the basic array data type plus core array operations, pandas
 
 1. defines fundamental structures for working with data and
 1. endows them with methods that facilitate operations such as
@@ -499,9 +499,9 @@ plt.show()
 
 Python makes it straightforward to query online databases programmatically.
 
-An important database for economists is [FRED](https://research.stlouisfed.org/fred2/) --- a vast collection of time series data maintained by the St. Louis Fed.
+An important database for economists is [FRED](https://fred.stlouisfed.org/) --- a vast collection of time series data maintained by the St. Louis Fed.
 
-For example, suppose that we are interested in the [unemployment rate](https://research.stlouisfed.org/fred2/series/UNRATE).
+For example, suppose that we are interested in the [unemployment rate](https://fred.stlouisfed.org/series/UNRATE).
 
 (To download the data as a csv, click on the top right `Download` and select the `CSV (data)` option).
 

lectures/scipy.md

@@ -25,7 +25,7 @@ kernelspec:
 
 ## Overview
 
-[SciPy](https://www.scipy.org) builds on top of NumPy to provide common tools for scientific programming such as
+[SciPy](https://scipy.org/) builds on top of NumPy to provide common tools for scientific programming such as
 
 * [linear algebra](https://docs.scipy.org/doc/scipy/reference/linalg.html)
 * [numerical integration](https://docs.scipy.org/doc/scipy/reference/integrate.html)