FIX: Broken Links and Redirects (#264)

* update links

* remove a space

* update links v2

Author: HumphreyYang

lectures/aiyagari.md

@@ -70,7 +70,7 @@ The primary reference for this lecture is {cite}`Aiyagari1994`.
 
 A textbook treatment is available in chapter 18 of {cite}`Ljungqvist2012`.
 
-A continuous time version of the model by SeHyoun Ahn and Benjamin Moll can be found [here](http://nbviewer.jupyter.org/github/QuantEcon/QuantEcon.notebooks/blob/master/aiyagari_continuous_time.ipynb).
+A continuous time version of the model by SeHyoun Ahn and Benjamin Moll can be found [here](https://nbviewer.org/github/QuantEcon/QuantEcon.notebooks/blob/master/aiyagari_continuous_time.ipynb).
 
 ## The Economy
 
@@ -187,7 +187,7 @@ If this final quantity agrees with $K$ then we have a SREE.
 
 Let's look at how we might compute such an equilibrium in practice.
 
-To solve the household's dynamic programming problem we'll use the [DiscreteDP](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/markov/ddp.py) class from [QuantEcon.py](http://quantecon.org/quantecon-py).
+To solve the household's dynamic programming problem we'll use the [DiscreteDP](https://github.com/QuantEcon/QuantEcon.py/blob/master/quantecon/markov/ddp.py) class from [QuantEcon.py](https://quantecon.org/quantecon-py/).
 
 Our first task is the least exciting one: write code that maps parameters for a household problem into the `R` and `Q` matrices needed to generate an instance of `DiscreteDP`.
 

lectures/coleman_policy_iter.md

@@ -129,7 +129,7 @@ $$
 
 Differentiating with respect to $y$,  and then evaluating at the optimum yields {eq}`cpi_env`.
 
-(Section 12.1 of [EDTC](http://johnstachurski.net/edtc.html) contains full proofs of these results, and closely related discussions can be found in many other texts.)
+(Section 12.1 of [EDTC](https://johnstachurski.net/edtc.html) contains full proofs of these results, and closely related discussions can be found in many other texts.)
 
 Differentiability of the value function and interiority of the optimal policy
 imply that optimal consumption satisfies the first order condition associated

lectures/finite_markov.md

@@ -45,7 +45,7 @@ You will find them in many of the workhorse models of economics and finance.
 
 In this lecture, we review some of the theory of Markov chains.
 
-We will also introduce some of the high-quality routines for working with Markov chains available in [QuantEcon.py](http://quantecon.org/quantecon-py).
+We will also introduce some of the high-quality routines for working with Markov chains available in [QuantEcon.py](https://quantecon.org/quantecon-py/).
 
 Prerequisite knowledge is basic probability and linear algebra.
 
@@ -690,7 +690,7 @@ Mathematically, a stationary distribution is a fixed point of $P$ when $P$ is th
 
 (We are assuming here that the state space $S$ is finite; if not more assumptions are required)
 
-For proof of this result, you can apply [Brouwer's fixed point theorem](https://en.wikipedia.org/wiki/Brouwer_fixed-point_theorem), or see [EDTC](http://johnstachurski.net/edtc.html), theorem 4.3.5.
+For proof of this result, you can apply [Brouwer's fixed point theorem](https://en.wikipedia.org/wiki/Brouwer_fixed-point_theorem), or see [EDTC](https://johnstachurski.net/edtc.html), theorem 4.3.5.
 
 There can be many stationary distributions corresponding to a given stochastic matrix $P$.
 

lectures/intro.md

@@ -12,7 +12,7 @@ kernelspec:
 # Quantitative Economics with Python
 
 This website presents a set of lectures on quantitative economic modeling, designed and written by
-[Thomas J. Sargent](http://www.tomsargent.com/) and [John Stachurski](http://johnstachurski.net/).
+[Thomas J. Sargent](http://www.tomsargent.com/) and [John Stachurski](https://johnstachurski.net/).
 
 For an overview of the series, see [this page](https://quantecon.org/python-lectures/)
 
@@ -21,5 +21,5 @@ For an overview of the series, see [this page](https://quantecon.org/python-lect
 
 
 ```{admonition} Previous website
-While this new site will receive all future updates, you may still view the [old site here](http://rst-python.quantecon.org) for the next month.
+While this new site will receive all future updates, you may still view the [old site here](https://d6mtww49nma8j.cloudfront.net/) for the next month.
 ```

lectures/lake_model.md

@@ -631,7 +631,7 @@ The wage offer distribution will be a discretized version of the lognormal distr
 
 We take a period to be a month.
 
-We set $b$ and $d$ to match monthly [birth](http://www.cdc.gov/nchs/fastats/births.htm) and [death rates](http://www.cdc.gov/nchs/fastats/deaths.htm), respectively, in the U.S. population
+We set $b$ and $d$ to match monthly [birth](https://www.cdc.gov/nchs/fastats/births.htm) and [death rates](https://www.cdc.gov/nchs/fastats/deaths.htm), respectively, in the U.S. population
 
 * $b = 0.0124$
 * $d = 0.00822$

lectures/linear_algebra.md

@@ -533,7 +533,7 @@ If $A$ and $B$ are two matrices, then their product $A B$ is formed by taking as
 $i,j$-th element the inner product of the $i$-th row of $A$ and the
 $j$-th column of $B$.
 
-There are many tutorials to help you visualize this operation, such as [this one](http://www.mathsisfun.com/algebra/matrix-multiplying.html), or the discussion on the [Wikipedia page](https://en.wikipedia.org/wiki/Matrix_multiplication).
+There are many tutorials to help you visualize this operation, such as [this one](https://www.mathsisfun.com/algebra/matrix-multiplying.html), or the discussion on the [Wikipedia page](https://en.wikipedia.org/wiki/Matrix_multiplication).
 
 If $A$ is $n \times k$ and $B$ is $j \times m$, then
 to multiply $A$ and $B$ we require $k = j$, and the
@@ -1155,9 +1155,9 @@ Then
 
 ### Further Reading
 
-The documentation of the `scipy.linalg` submodule can be found [here](http://docs.scipy.org/doc/scipy/reference/linalg.html).
+The documentation of the `scipy.linalg` submodule can be found [here](https://docs.scipy.org/doc/scipy/reference/linalg.html).
 
-Chapters 2 and 3 of the [Econometric Theory](http://www.johnstachurski.net/emet.html) contains
+Chapters 2 and 3 of the [Econometric Theory](https://johnstachurski.net/emet.html) contains
 a discussion of linear algebra along the same lines as above, with solved exercises.
 
 If you don't mind a slightly abstract approach, a nice intermediate-level text on linear algebra

lectures/mle.md

@@ -35,7 +35,7 @@ involves specifying a class of distributions, indexed by unknown parameters, and
 
 The benefit relative to linear regression is that it allows more flexibility in the probabilistic relationships between variables.
 
-Here we illustrate maximum likelihood by replicating Daniel Treisman's (2016) paper, [Russia's Billionaires](http://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.p20161068), which connects the number of billionaires in a country to its economic characteristics.
+Here we illustrate maximum likelihood by replicating Daniel Treisman's (2016) paper, [Russia's Billionaires](https://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.p20161068), which connects the number of billionaires in a country to its economic characteristics.
 
 The paper concludes that Russia has a higher number of billionaires than
 economic factors such as market size and tax rate predict.
@@ -636,7 +636,7 @@ print(stats_poisson.summary())
 ```
 
 Now let's replicate results from Daniel Treisman's paper, [Russia's
-Billionaires](http://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.p20161068),
+Billionaires](https://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.p20161068),
 mentioned earlier in the lecture.
 
 Treisman starts by estimating equation {eq}`poissonreg`, where:
@@ -766,14 +766,14 @@ In this lecture, we used Maximum Likelihood Estimation to estimate the
 parameters of a Poisson model.
 
 `statsmodels` contains other built-in likelihood models such as
-[Probit](http://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.Probit.html)
+[Probit](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.Probit.html)
 and
-[Logit](http://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.Logit.html).
+[Logit](https://www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.Logit.html).
 
 For further flexibility, `statsmodels` provides a way to specify the
 distribution manually using the `GenericLikelihoodModel` class - an
 example notebook can be found
-[here](http://www.statsmodels.org/dev/examples/notebooks/generated/generic_mle.html).
+[here](https://www.statsmodels.org/dev/examples/notebooks/generated/generic_mle.html).
 
 ## Exercises
 

lectures/ols.md

@@ -47,7 +47,7 @@ Along the way, we'll discuss a variety of topics, including
 
 As an example, we will replicate results from Acemoglu, Johnson and Robinson's seminal paper {cite}`Acemoglu2001`.
 
-* You can download a copy [here](http://economics.mit.edu/files/4123).
+* You can download a copy [here](https://web.archive.org/web/20220901051300/http://economics.mit.edu/files/4123).
 
 In the paper, the authors emphasize the importance of institutions in economic development.
 
@@ -86,7 +86,7 @@ In this paper,
 - economic outcomes are proxied by log GDP per capita in 1995, adjusted for exchange rates.
 - institutional differences are proxied by an index of protection against expropriation on average over 1985-95, constructed by the [Political Risk Services Group](https://www.prsgroup.com/).
 
-These variables and other data used in the paper are available for download on Daron Acemoglu's [webpage](http://economics.mit.edu/faculty/acemoglu/data/ajr2001).
+These variables and other data used in the paper are available for download on Daron Acemoglu's [webpage](https://web.archive.org/web/20220901063129/http://economics.mit.edu/faculty/acemoglu/data/ajr2001).
 
 We will use pandas' `.read_stata()` function to read in data contained in the `.dta` files to dataframes
 
@@ -552,7 +552,7 @@ significance of institutions in economic development.
 
 We have demonstrated basic OLS and 2SLS regression in `statsmodels` and `linearmodels`.
 
-If you are familiar with R, you may want to use the [formula interface](http://www.statsmodels.org/dev/example_formulas.html) to `statsmodels`, or consider using [r2py](https://rpy2.github.io/) to call R from within Python.
+If you are familiar with R, you may want to use the [formula interface](https://www.statsmodels.org/dev/example_formulas.html) to `statsmodels`, or consider using [r2py](https://rpy2.github.io/) to call R from within Python.
 
 ## Exercises
 

lectures/pandas_panel.md

@@ -487,7 +487,7 @@ This lecture has provided an introduction to some of pandas' more
 advanced features, including multiindices, merging, grouping and
 plotting.
 
-Other tools that may be useful in panel data analysis include [xarray](http://xarray.pydata.org/en/stable/), a python package that
+Other tools that may be useful in panel data analysis include [xarray](https://docs.xarray.dev/en/stable/), a python package that
 extends pandas to N-dimensional data structures.
 
 ## Exercises
@@ -497,7 +497,7 @@ extends pandas to N-dimensional data structures.
 ```
 
 In these exercises, you'll work with a dataset of employment rates
-in Europe by age and sex from [Eurostat](http://ec.europa.eu/eurostat/data/database).
+in Europe by age and sex from [Eurostat](https://ec.europa.eu/eurostat/data/database).
 
 The dataset can be accessed with the following link:
 

lectures/status.md

@@ -17,4 +17,4 @@ This table contains the latest execution statistics.
 ```
 
 These lectures are built on `linux` instances through `github actions` so are
-executed using the following [hardware specifications](https://docs.github.com/en/actions/reference/specifications-for-github-hosted-runners#supported-runners-and-hardware-resources)
+executed using the following [hardware specifications](https://docs.github.com/en/actions/using-github-hosted-runners/about-github-hosted-runners#supported-runners-and-hardware-resources)