Merge branch 'main' into download-nb

Author: mmcky

lectures/_toc.yml

@@ -1,7 +1,7 @@
 format: jb-book
 root: intro
 parts:
-- caption: 基础工具
+- caption: 工具与技术
   numbered: true
   chapters:
   - file: sir_model
@@ -11,6 +11,18 @@ parts:
   - file: svd_intro
   - file: var_dmd
   - file: newton_method
+- caption: 基础统计学
+  numbered: true 
+  chapters:
+  - file: prob_matrix
+  - file: lln_clt
+  - file: prob_meaning
+  - file: multi_hyper
+  - file: multivariate_normal
+  - file: hoist_failure
+  - file: back_prop
+  - file: rand_resp
+  - file: util_rand_resp
 - caption: 线性规划
   numbered: true
   chapters:
@@ -27,7 +39,7 @@ parts:
   - file: wealth_dynamics
   - file: kalman
   - file: kalman_2
-- caption: 搜索理论
+- caption: 搜索
   numbered: true
   chapters:
   - file: mccall_model
@@ -52,6 +64,12 @@ parts:
   - file: egm_policy_iter
   - file: ifp
   - file: ifp_advanced
+- caption: 贝叶斯定律
+  numbered: true
+  chapters:
+  - file:  bayes_nonconj
+  - file:  ar1_bayes
+  - file:  ar1_turningpts
 - caption: 信息论
   numbered: true
   chapters:
@@ -61,6 +79,7 @@ parts:
   - file: wald_friedman
   - file: exchangeable
   - file: likelihood_bayes
+  - file: mix_model
   - file: navy_captain
 - caption: LQ控制
   numbered: true
@@ -80,6 +99,23 @@ parts:
   - file: markov_perf
   - file: uncertainty_traps
   - file: aiyagari
+- caption: 资产定价与金融
+  numbered: true
+  chapters:
+  - file: markov_asset
+  - file: ge_arrow
+  - file: harrison_kreps
+- caption: 数据与实证
+  numbered: true
+  chapters:
+  - file: pandas_panel
+  - file: ols
+  - file: mle
+- caption: 拍卖
+  numbered: true 
+  chapters:
+  - file: two_auctions
+  - file: house_auction
 - caption: 其他
   numbered: true
   chapters:

lectures/harrison_kreps.md

@@ -138,7 +138,7 @@ mca.stationary_distributions
 ```
 
 ```{code-cell} ipython3
-mcb.平稳分布
+mcb.stationary_distributions
 ```
 
 $P_a$ 的平稳分布约为 $\pi_a = \begin{bmatrix} .57 & .43 \end{bmatrix}$。

lectures/house_auction.md

@@ -459,19 +459,19 @@ def check_terminal_condition(bid_info, p, v):
 ```
 
 ```{code-cell} ipython3
-分配,获胜者列表,失败者列表 = 检查终止条件(出价信息, p, v)
+allocation, winner_list, loser_list = check_terminal_condition(bid_info, p, v)
 ```
 
 ```{code-cell} ipython3
 present_dict(allocation)
 ```
 
 ```{code-cell} ipython3
-获胜者列表
+winner_list
 ```
 
 ```{code-cell} ipython3
-失败者列表
+loser_list
 ```
 
 ### 第二轮
@@ -549,7 +549,7 @@ present_dict(bid_info)
 ```
 
 ```{code-cell} ipython3
-分配,获胜者列表,失败者列表 = 检查终止条件(出价信息, p, v)
+allocation, winner_list, loser_list = check_terminal_condition(bid_info, p, v)
 ```
 
 ```{code-cell} ipython3
@@ -571,7 +571,7 @@ present_dict(bid_info)
 ```
 
 ```{code-cell} ipython3
-分配,获胜者列表,失败者列表 = 检查终止条件(出价信息, p, v)
+allocation, winner_list, loser_list = check_terminal_condition(bid_info, p, v)
 ```
 
 ```{code-cell} ipython3

lectures/markov_asset.md

@@ -1009,7 +1009,7 @@ consol_price(apm, ζ)
 ```
 
 ```{code-cell} ipython3
-看涨期权(apm, ζ, p_s)
+call_option(apm, ζ, p_s)
 ```
 
 让我们将最后两个函数绘制成图表

lectures/multivariate_normal.md

@@ -789,7 +789,6 @@ $$
 那么我们可以写作
 
 ```{math}
-
 :label: mnv_1
 
 \theta = \mu_{\theta} + c_1 \epsilon_1 + c_2 \epsilon_2 + \dots + c_n \epsilon_n + c_{n+1} \epsilon_{n+1}

lectures/ols.md

@@ -520,6 +520,7 @@ $$
 如果$\alpha$在统计上显著（p值<0.05），那么我们就拒绝原假设，得出$avexpr_i$是内生的结论。
 
 使用上述信息，估算豪斯曼检验并解释你的结果。
+```
 
 ```{solution-start} ols_ex1
 :class: dropdown
@@ -589,6 +590,7 @@ $$
 使用上述信息，计算模型1中的 $\hat{\beta}$
 
 使用 `numpy` - 你的结果应该与讲座前面 `statsmodels` 的输出结果相同。
+```
 
 ```{solution-start} ols_ex2
 :class: dropdown