Add hogs references

Author: Smit-create

lectures/_static/quant-econ.bib

@@ -2547,3 +2547,17 @@ @article{cobweb_model
  volume = {46},
  year = {1964}
 }
+
+@article{hog_cycle,
+author = {Harlow, Arthur A.},
+title = {The Hog Cycle and the Cobweb Theorem},
+journal = {American Journal of Agricultural Economics},
+volume = {42},
+number = {4},
+pages = {842-853},
+doi = {https://doi.org/10.2307/1235116},
+url = {https://onlinelibrary.wiley.com/doi/abs/10.2307/1235116},
+eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.2307/1235116},
+abstract = {Abstract A surprisingly regular four year cycle in hogs has become apparent in the past ten years. This regularity presents an unusual opportunity to study the mechanism of the cycle because it suggests the cycle may be inherent within the industry rather than the result of lagged responses to outside influences. The cobweb theorem is often mentioned as a theoretical tool for explaining the hog cycle, although a two year cycle is usually predicted. When the nature of the hog industry is examined, certain factors become apparent which enable the cobweb theorem to serve as a theoretical basis for the present four year cycle.},
+year = {1960}
+}

lectures/cobweb.md

@@ -44,6 +44,37 @@ supply and causing the price to climb again.
 You can imagine how these dynamics could cause cycles in prices and quantities
 that persist over time.
 
+This motivation is also taken from one of the earliest papers  that uses
+cobweb theorem to explain the prices of hog in the US. We will try to
+simulate and plot the graph that uses the rough data from the paper {cite}`hog_cycle`.
+
+We will use the following imports:
+
+```{code-cell} ipython3
+import numpy as np
+import matplotlib.pyplot as plt
+```
+
+We will use the following data for simulation:
+
+```{code-cell} ipython3
+:tags: [hide-input]
+hog_prices = [55, 57, 80, 70, 60, 65, 72, 65, 51, 49, 45, 80, 85,
+              78, 80, 68, 52, 65, 83, 78, 60, 62, 80, 87, 81, 70,
+              69, 65, 62, 85, 87, 65, 63, 75, 80, 62]
+years = np.arange(1924, 1960)
+```
+
+Let's plot the above data and observe the graph.
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(9, 4))
+ax.plot(years, hog_prices, '-o', ms=4)
+ax.set_xlabel('year')
+ax.set_ylabel('prices')
+plt.show()
+```
+
 The cobweb model puts these ideas into equations so we can try to quantify
 them, and to study conditions underw which cycles persist (or disappear).
 
@@ -52,12 +83,7 @@ assumptions regarding the way that produces form expectations.
 
 Our discussion and simulations draw on [high quality lectures](https://comp-econ.org/CEF_2013/downloads/Complex%20Econ%20Systems%20Lecture%20II.pdf) by [Cars Hommes](https://www.uva.nl/en/profile/h/o/c.h.hommes/c.h.hommes.html).
 
-We will use the following imports:
 
-```{code-cell} ipython3
-import numpy as np
-import matplotlib.pyplot as plt
-```
 
 ## The Model