[harrison_kreps] Update new lecture on Bayesian Learning and Speculative Behavior

lectures/_static/quant-econ.bib

@@ -3,6 +3,62 @@
 Note: Extended Information (like abstracts, doi, url's etc.) can be found in quant-econ-extendedinfo.bib file in _static/
 ###
 
+
+@article{harsanyi1968games,
+  title={Games with Incomplete Information Played by ``{B}ayesian'' Players, {I}--{III} Part {II}. {B}ayesian Equilibrium Points},
+  author={Harsanyi, John C.},
+  journal={Management Science},
+  volume={14},
+  number={5},
+  pages={320--334},
+  year={1968},
+  publisher={INFORMS}
+}
+
+@article{harsanyi1968games3,
+  title={Games with Incomplete Information Played by ``{B}ayesian'' Players, {I}--{III} Part {III}. {T}he Basic Probability Distribution of the Game},
+  author={Harsanyi, John C.},
+  journal={Management Science},
+  volume={14},
+  number={7},
+  pages={486--502},
+  year={1968},
+  publisher={INFORMS}
+}
+
+@article{harsanyi1967games,
+  title={Games with Incomplete Information Played by ``{B}ayesian'' Players, {I}--{III} Part {I}. {T}he Basic Model},
+  author={Harsanyi, John C.},
+  journal={Management Science},
+  volume={14},
+  number={3},
+  pages={159--182},
+  year={1967},
+  publisher={INFORMS}
+}
+
+@article{miller1977risk,
+  title={Risk, uncertainty, and divergence of opinion},
+  author={Miller, Edward M},
+  journal={The Journal of finance},
+  volume={32},
+  number={4},
+  pages={1151--1168},
+  year={1977},
+  publisher={JSTOR}
+}
+
+@article{jeffreys1946invariant,
+  title={An invariant form for the prior probability in estimation problems},
+  author={Jeffreys, Harold},
+  journal={Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences},
+  volume={186},
+  number={1007},
+  pages={453--461},
+  year={1946},
+  publisher={The Royal Society London}
+}
+
 @article{blume2018case,
   title={A case for incomplete markets},
   author={Blume, Lawrence E and Cogley, Timothy and Easley, David A and Sargent, Thomas J and Tsyrennikov, Viktor},
@@ -1378,6 +1434,16 @@ @article{HarrKreps1978
   pages   = {323-336}
 }
 
+@article{Morris1996,
+  author  = {Stephen Morris},
+  title   = {Speculative Investor Behavior and Learning},
+  journal = {The Quarterly Journal of Economics},
+  year    = {1996},
+  volume  = {111},
+  number  = {4},
+  pages   = {1111-1133}
+}
+
 @article{pal2013,
   title     = {Fitted value function iteration with probability one contractions},
   author    = {P{\'a}l, Jen{\H{o}} and Stachurski, John},

lectures/_toc.yml

@@ -112,6 +112,7 @@ parts:
   - file: markov_asset
   - file: ge_arrow
   - file: harrison_kreps
+  - file: morris_learn
 - caption: Data and Empirics
   numbered: true
   chapters:

lectures/harrison_kreps.md

@@ -3,10 +3,12 @@ jupytext:
   text_representation:
     extension: .md
     format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.17.1
 kernelspec:
-  display_name: Python 3
-  language: python
   name: python3
+  display_name: Python 3 (ipykernel)
+  language: python
 ---
 
 (harrison_kreps)=
@@ -29,10 +31,9 @@ kernelspec:
 
 In addition to what's in Anaconda, this lecture uses following libraries:
 
-```{code-cell} ipython
----
-tags: [hide-output]
----
+```{code-cell} ipython3
+:tags: [hide-output]
+
 !pip install quantecon
 ```
 
@@ -51,7 +52,7 @@ The model features
 
 Let's start with some standard imports:
 
-```{code-cell} ipython
+```{code-cell} ipython3
 import numpy as np
 import quantecon as qe
 import scipy.linalg as la
@@ -131,15 +132,15 @@ But in state $1$,  a type $a$ investor is more pessimistic  about next period's
 
 The stationary (i.e., invariant) distributions of these two matrices can be calculated as follows:
 
-```{code-cell} python3
+```{code-cell} ipython3
 qa = np.array([[1/2, 1/2], [2/3, 1/3]])
 qb = np.array([[2/3, 1/3], [1/4, 3/4]])
 mca = qe.MarkovChain(qa)
 mcb = qe.MarkovChain(qb)
 mca.stationary_distributions
 ```
 
-```{code-cell} python3
+```{code-cell} ipython3
 mcb.stationary_distributions
 ```
 
@@ -270,7 +271,7 @@ The first two rows of the table report $p_a(s)$ and $p_b(s)$.
 
 Here's a function that can be used to compute these values
 
-```{code-cell} python3
+```{code-cell} ipython3
 def price_single_beliefs(transition, dividend_payoff, β=.75):
     """
     Function to Solve Single Beliefs
@@ -399,7 +400,7 @@ Investors of type $a$ want to sell the asset in state $1$ while investors of typ
 
 Here's code to solve for $\bar p$, $\hat p_a$ and $\hat p_b$ using the iterative method described above
 
-```{code-cell} python3
+```{code-cell} ipython3
 def price_optimistic_beliefs(transitions, dividend_payoff, β=.75,
                             max_iter=50000, tol=1e-16):
     """
@@ -444,8 +445,8 @@ Instead of equation {eq}`hakr2`, the equilibrium price satisfies
 \check p(s)
 = \beta \min
 \left\{
-    P_a(s,1)  \check  p(0) + P_a(s,1) ( 1 +   \check  p(1)) ,\;
-    P_b(s,1)  \check p(0) + P_b(s,1) ( 1 + \check p(1))
+    P_a(s,0)  \check  p(0) + P_a(s,1) ( 1 +   \check  p(1)) ,\;
+    P_b(s,0)  \check p(0) + P_b(s,1) ( 1 + \check p(1))
 \right\}
 ```
 
@@ -467,7 +468,7 @@ Constraints on short sales prevent that.
 
 Here's code to solve for $\check p$ using iteration
 
-```{code-cell} python3
+```{code-cell} ipython3
 def price_pessimistic_beliefs(transitions, dividend_payoff, β=.75,
                             max_iter=50000, tol=1e-16):
     """
@@ -512,8 +513,6 @@ Scheinkman extracts insights about the effects of financial regulations on bubbl
 
 He emphasizes how limiting short sales and limiting leverage have opposite effects.
 
-## Exercises
-
 ```{exercise-start}
 :label: hk_ex1
 ```
@@ -570,7 +569,7 @@ We'll use these transition matrices when we present our solution of exercise 1 b
 First, we will obtain equilibrium price vectors with homogeneous beliefs, including when all
 investors are optimistic or pessimistic.
 
-```{code-cell} python3
+```{code-cell} ipython3
 qa = np.array([[1/2, 1/2], [2/3, 1/3]])    # Type a transition matrix
 qb = np.array([[2/3, 1/3], [1/4, 3/4]])    # Type b transition matrix
 # Optimistic investor transition matrix
@@ -595,7 +594,7 @@ for transition, label in zip(transitions, labels):
 We will use the price_optimistic_beliefs function to find the price under
 heterogeneous beliefs.
 
-```{code-cell} python3
+```{code-cell} ipython3
 opt_beliefs = price_optimistic_beliefs([qa, qb], dividendreturn)
 labels = ['p_optimistic', 'p_hat_a', 'p_hat_b']