Merge wasm into wasm_pr2_business_cycle

Author: kp992

lectures/ar1_processes.md

@@ -3,8 +3,10 @@ jupytext:
   text_representation:
     extension: .md
     format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.16.1
 kernelspec:
-  display_name: Python 3
+  display_name: Python 3 (ipykernel)
   language: python
   name: python3
 ---
@@ -40,7 +42,7 @@ partly because they help us understand important concepts.
 
 Let's start with some imports:
 
-```{code-cell} ipython
+```{code-cell} ipython3
 import numpy as np
 import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
@@ -160,15 +162,15 @@ The following code uses these facts to track the sequence of marginal distributi
 
 The parameters are
 
-```{code-cell} python3
+```{code-cell} ipython3
 a, b, c = 0.9, 0.1, 0.5
 
 mu, v = -3.0, 0.6  # initial conditions mu_0, v_0
 ```
 
 Here's the sequence of distributions:
 
-```{code-cell} python3
+```{code-cell} ipython3
 from scipy.stats import norm
 
 sim_length = 10
@@ -188,8 +190,6 @@ ax.legend(bbox_to_anchor=[1.05,1],loc=2,borderaxespad=1)
 plt.show()
 ```
 
-
-
 ## Stationarity and asymptotic stability
 
 When we use models to study the real world, it is generally preferable that our
@@ -206,7 +206,7 @@ Notice that, in the figure above, the sequence $\{ \psi_t \}$ seems to be conver
 
 This is even clearer if we project forward further into the future:
 
-```{code-cell} python3
+```{code-cell} ipython3
 def plot_density_seq(ax, mu_0=-3.0, v_0=0.6, sim_length=40):
     mu, v = mu_0, v_0
     for t in range(sim_length):
@@ -225,7 +225,7 @@ Moreover, the limit does not depend on the initial condition.
 
 For example, this alternative density sequence also converges to the same limit.
 
-```{code-cell} python3
+```{code-cell} ipython3
 fig, ax = plt.subplots()
 plot_density_seq(ax, mu_0=4.0)
 plt.show()
@@ -260,7 +260,7 @@ t \to \infty
 
 We can confirm this is valid for the sequence above using the following code.
 
-```{code-cell} python3
+```{code-cell} ipython3
 fig, ax = plt.subplots()
 plot_density_seq(ax, mu_0=4.0)
 
@@ -406,7 +406,7 @@ Confirm this by simulation at a range of $k$ using the default parameters from t
 
 Here is one solution:
 
-```{code-cell} python3
+```{code-cell} ipython3
 from scipy.special import factorial2
 
 def sample_moments_ar1(k, m=100_000, mu_0=0.0, sigma_0=1.0, seed=1234):

lectures/eigen_II.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.4
+    jupytext_version: 1.16.1
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python

lectures/french_rev.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.2
+    jupytext_version: 1.16.1
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -61,13 +61,20 @@ This lecture uses data from three spreadsheets assembled by {cite}`sargent_velde
   * [datasets/dette.xlsx](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/datasets/dette.xlsx)
   * [datasets/assignat.xlsx](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/datasets/assignat.xlsx)
 
+```{code-cell} ipython3
+!pip install openpyxl requests
+```
+
 ```{code-cell} ipython3
 import numpy as np
 import pandas as pd
+import pyodide_http
 import matplotlib.pyplot as plt
+from io import BytesIO
+import requests
 plt.rcParams.update({'font.size': 12})
 
-base_url = 'https://github.com/QuantEcon/lecture-python-intro/raw/'\
+base_url = 'https://raw.githubusercontent.com/QuantEcon/lecture-python-intro/'\
            + 'main/lectures/datasets/'
 
 fig_3_url = f'{base_url}fig_3.xlsx'
@@ -90,13 +97,11 @@ These graphs show that during the 18th century
   but were substantially less than government expenditures during wars
  * In France, even in peace time, tax revenues were substantially less than government expenditures
 
-
-
 ```{code-cell} ipython3
 ---
 mystnb:
   figure:
-    caption: "Military Spending in Britain and France"
+    caption: Military Spending in Britain and France
     name: fr_fig4
 ---
 # Read the data from Excel file
@@ -139,16 +144,15 @@ during those four wars.
 
 A remarkable aspect of figure {numref}`fr_fig4` is that despite having a population less than half of France's, Britain was able to finance military expenses of about the same amounts as France's.
 
-This testifies to Britain's  having created state institutions that could sustain high  tax collections, government spending , and government borrowing. See  {cite}`north1989`. 
+This testifies to Britain's  having created state institutions that could sustain high  tax collections, government spending , and government borrowing. See  {cite}`north1989`.
 
 ```{code-cell} ipython3
 ---
 mystnb:
   figure:
-    caption: "Government Expenditures and Tax Revenues in Britain"
+    caption: Government Expenditures and Tax Revenues in Britain
     name: fr_fig2
 ---
-
 # Read the data from Excel file
 data2 = pd.read_excel(dette_url, sheet_name='Militspe', usecols='M:X', 
                       skiprows=7, nrows=102, header=None)
@@ -178,7 +182,6 @@ plt.tight_layout()
 plt.show()
 ```
 
-
 Figures  {numref}`fr_fig2` and  {numref}`fr_fig3` summarize British and French government   fiscal policies  during the century before the start of the French Revolution in 1789.
 
 
@@ -224,10 +227,9 @@ Next we'll plot data on debt service costs as fractions of government revenues i
 ---
 mystnb:
   figure:
-    caption: "Ratio of debt service to taxes, Britain and France"
+    caption: Ratio of debt service to taxes, Britain and France
     name: fr_fig1
 ---
-
 # Read the data from the Excel file
 data1 = pd.read_excel(dette_url, sheet_name='Debt', 
             usecols='R:S', skiprows=5, nrows=99, header=None)
@@ -265,7 +267,6 @@ Figure  {numref}`fr_fig1` shows that interest payments on government debt (i.e.,
 
 But as  we'll see in our next graph, on the eve of the French Revolution in 1788, the  fiscal  *law of gravity* that worked so well in Britain did not  working very well in  France.
 
-
 ```{code-cell} ipython3
 # Read the data from the Excel file
 data1 = pd.read_excel(fig_3_url, sheet_name='Sheet1', 
@@ -278,7 +279,7 @@ data1.replace(0, np.nan, inplace=True)
 ---
 mystnb:
   figure:
-    caption: "Government Spending and Tax Revenues in France"
+    caption: Government Spending and Tax Revenues in France
     name: fr_fig3
 ---
 # Plot the data
@@ -430,7 +431,7 @@ The next figure shows this
 ---
 mystnb:
   figure:
-    caption: "Index of real per capital revenues, France"
+    caption: Index of real per capital revenues, France
     name: fr_fig5
 ---
 # Read data from Excel file
@@ -466,7 +467,7 @@ amounts during the period form 1789 to 1799.
 ---
 mystnb:
   figure:
-    caption: "Spending (blue) and Revenues (orange), (real values)"
+    caption: Spending (blue) and Revenues (orange), (real values)
     name: fr_fig11
 ---
 # Read data from Excel file
@@ -512,7 +513,7 @@ of goods and services, including military goods and soldiers' pay.
 ---
 mystnb:
   figure:
-    caption: "Revenues raised by printing paper money notes"
+    caption: Revenues raised by printing paper money notes
     name: fr_fig24
 ---
 # Read data from Excel file
@@ -575,7 +576,7 @@ Note that we use a log scale because the price level rose so much.
 ---
 mystnb:
   figure:
-    caption: "Price Level and Price of Gold (log scale)"
+    caption: Price Level and Price of Gold (log scale)
     name: fr_fig9
 ---
 # Read the data from Excel file
@@ -644,7 +645,7 @@ nearly met in one of these episodes than in the other two.
 ---
 mystnb:
   figure:
-    caption: "Real balances of assignats (in gold and goods)"
+    caption: Real balances of assignats (in gold and goods)
     name: fr_fig8
 ---
 # Read the data from Excel file
@@ -712,8 +713,10 @@ def fit(x, y):
 
 ```{code-cell} ipython3
 # Load data
-caron = np.load('datasets/caron.npy')
-nom_balances = np.load('datasets/nom_balances.npy')
+caron_response = requests.get(base_url + 'caron.npy')
+nom_balances_response = requests.get(base_url + 'nom_balances.npy')
+caron = np.load(BytesIO(caron_response.content))
+nom_balances = np.load(BytesIO(nom_balances_response.content))
 
 infl = np.concatenate(([np.nan], 
       -np.log(caron[1:63, 1] / caron[0:62, 1])))
@@ -736,7 +739,7 @@ a3_rev, b3_rev = fit(infl[44:63], bal[44:63])
 ---
 mystnb:
   figure:
-    caption: "Inflation and Real Balances"
+    caption: Inflation and Real Balances
     name: fr_fig104
 ---
 plt.figure()
@@ -791,7 +794,7 @@ a3_rev, b3_rev = fit(infl[44:63], bal[44:63])
 ---
 mystnb:
   figure:
-    caption: "Inflation and Real Balances"
+    caption: Inflation and Real Balances
     name: fr_fig104b
 ---
 plt.figure()
@@ -822,7 +825,7 @@ line.
 ---
 mystnb:
   figure:
-    caption: "Inflation and Real Balances"
+    caption: Inflation and Real Balances
     name: fr_fig104c
 ---
 plt.figure()
@@ -867,7 +870,7 @@ line.
 ---
 mystnb:
   figure:
-    caption: "Inflation and Real Balances"
+    caption: Inflation and Real Balances
     name: fr_fig104d
 ---
 plt.figure()
@@ -912,7 +915,7 @@ Cagan {cite}`Cagan`.
 ---
 mystnb:
   figure:
-    caption: "Inflation and Real Balances"
+    caption: Inflation and Real Balances
     name: fr_fig104e
 ---
 plt.figure()
@@ -946,7 +949,7 @@ period of the hyperinflation.
 ---
 mystnb:
   figure:
-    caption: "Inflation and Real Balances"
+    caption: Inflation and Real Balances
     name: fr_fig104f
 ---
 plt.figure()
@@ -993,4 +996,4 @@ This lecture  sets the stage for studying  theories of inflation and the  govern
 
 A  *monetarist theory of the price level* is described in this quantecon lecture {doc}`cagan_ree`.
 
-That lecture sets the stage for these quantecon lectures {doc}`money_inflation` and {doc}`unpleasant`.  
+That lecture sets the stage for these quantecon lectures {doc}`money_inflation` and {doc}`unpleasant`.

lectures/heavy_tails.md

@@ -19,7 +19,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install --upgrade yfinance pandas_datareader
+!pip install yfinance pandas_datareader
 ```
 
 We use the following imports.
@@ -30,11 +30,13 @@ import numpy as np
 import yfinance as yf
 import pandas as pd
 import statsmodels.api as sm
+import pyodide_http
 
 from pandas_datareader import wb
 from scipy.stats import norm, cauchy
 from pandas.plotting import register_matplotlib_converters
 register_matplotlib_converters()
+pyodide_http.patch_all()
 ```
 
 ## Overview