From 3a86160e852c15f64d01b73bca6305dbae2da70c Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Sat, 18 Jul 2026 20:46:55 +1000 Subject: [PATCH 1/6] =?UTF-8?q?=F0=9F=94=84=20resync=20mccall=5Fpersist=5F?= =?UTF-8?q?trans.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .translate/state/mccall_persist_trans.md.yml | 6 + lectures/mccall_persist_trans.md | 430 ++++++++++--------- 2 files changed, 232 insertions(+), 204 deletions(-) create mode 100644 .translate/state/mccall_persist_trans.md.yml diff --git a/.translate/state/mccall_persist_trans.md.yml b/.translate/state/mccall_persist_trans.md.yml new file mode 100644 index 00000000..a5ab674f --- /dev/null +++ b/.translate/state/mccall_persist_trans.md.yml @@ -0,0 +1,6 @@ +source-sha: 0bfcac8105ac8c11f06797579a9f4d565db7b53d +synced-at: "2026-07-18" +model: claude-sonnet-5 +mode: RESYNC +section-count: 5 +tool-version: 0.17.0 diff --git a/lectures/mccall_persist_trans.md b/lectures/mccall_persist_trans.md index 5fe9e29e..0b5ac413 100644 --- a/lectures/mccall_persist_trans.md +++ b/lectures/mccall_persist_trans.md @@ -7,6 +7,15 @@ kernelspec: display_name: Python 3 language: python name: python3 +translation: + title: 工作搜寻 V：持续性与暂时性工资冲击 + headings: + Overview: 概述 + The model: 模型 + The model::A simplification: 简化 + Implementation: 实现 + Unemployment duration: 失业持续时间 + Exercises: 练习 --- ```{raw} jupyter @@ -17,7 +26,10 @@ kernelspec: ``` -# 工作搜寻 IV：相关工资报价 +# 工作搜寻 V：持续性与暂时性工资冲击 + +```{include} _admonition/gpu.md +``` ```{contents} 目录 :depth: 2 @@ -26,36 +38,39 @@ kernelspec: 除了Anaconda中的内容外，本讲座还需要以下库： ```{code-cell} ipython ---- -tags: [hide-output] ---- -!pip install quantecon +:tags: ["hide-output"] + +!pip install quantecon jax ``` ## 概述 -在本讲座中，我们求解一个工资报价由持续性和暂时性成分组成的{doc}`McCall工作搜寻模型 `。 +在本讲座中，我们通过将工资报价分解为**持续性**和**暂时性**成分，扩展了{doc}`McCall工作搜寻模型 `。 + +在{doc}`基准模型 `中，工资报价在时间上是独立同分布的，这是不现实的。 + +在{doc}`工作搜寻 III `中，我们使用马尔可夫链引入了相关工资抽取，但同时也加入了离职因素。 -换句话说，我们放宽了工资随机性在时间上独立的假设。 +这里我们采取不同的方法：我们通过一个AR(1)过程来建模持续性成分，再加上一个暂时性冲击，同时回到假设工作是永久性的（如{doc}`基准模型 `中一样）。 -同时，我们将回到假设工作是永久性的，不会发生离职。 +这种持续性-暂时性分解方法： +- 对于建模实际工资过程更为现实 +- 在劳动经济学中被广泛使用（例如参见 {cite}`MaCurdy1982`、{cite}`Meghir2004`） +- 足够简单以便分析，同时又能捕捉工资动态的关键特征 -这是为了在研究相关性影响时保持模型相对简单。 +通过保持工作永久性这一假设，我们可以专注于理解持续性和暂时性工资冲击如何影响求职行为和保留工资。 + +我们将使用{doc}`工作搜寻 IV `中介绍的带线性插值的拟合价值函数迭代方法来求解模型。 我们将使用以下导入： -```{code-cell} ipython3 +```{code-cell} ipython import matplotlib.pyplot as plt -import matplotlib as mpl -FONTPATH = "fonts/SourceHanSerifSC-SemiBold.otf" -mpl.font_manager.fontManager.addfont(FONTPATH) -plt.rcParams['font.family'] = ['Source Han Serif SC'] - -import numpy as np +import jax +import jax.numpy as jnp +import jax.random import quantecon as qe -from numpy.random import randn -from numba import jit, prange, float64 -from numba.experimental import jitclass +from typing import NamedTuple ``` ## 模型 @@ -63,20 +78,20 @@ from numba.experimental import jitclass 每个时期的工资由下式给出： $$ -w_t = \exp(z_t) + y_t +W_t = \exp(Z_t) + Y_t $$ 其中 $$ -y_t \sim \exp(\mu + s \zeta_t) +Y_t \sim \exp(\mu + s \zeta_t) \quad \text{且} \quad -z_{t+1} = d + \rho z_t + \sigma \epsilon_{t+1} +Z_{t+1} = d + \rho Z_t + \sigma \epsilon_{t+1} $$ 这里 $\{ \zeta_t \}$ 和 $\{ \epsilon_t \}$ 都是独立同分布的标准正态随机变量。 -这里 $\{y_t\}$ 是暂时性成分，$\{z_t\}$ 是持续性成分。 +这里 $\{Y_t\}$ 是暂时性成分，$\{Z_t\}$ 是持续性成分。 如前所述，劳动者可以： @@ -129,7 +144,7 @@ $$ 在较弱的假设下，可以证明 $Q$ 是 $\mathbb R$ 上连续函数空间上的一个[压缩映射](https://baike.baidu.com/item/%E5%8E%8B%E7%BC%A9%E6%98%A0%E5%B0%84/5114126)。 -根据巴拿赫压缩映射定理，$f^*$ 是唯一的不动点，我们可以从任何合理的初始条件开始通过迭代 $Q$ 来得到$f^*$。 +根据巴拿赫压缩映射定理，这意味着 $f^*$ 是唯一的不动点，我们可以从任何合理的初始条件开始通过迭代 $Q$ 来计算它。 求得 $f^*$后，这一搜索问题的解就是当接受工作的收益超过延续价值时停止求职，即： @@ -137,7 +152,7 @@ $$ \frac{u(w)}{1-\beta} \geq f^*(z) $$ -对于效用函数，我们取 $u(c) = \ln(c)$。 +对于效用函数，我们取 $u(x) = \ln(x)$。 保留工资是最后一个表达式中等式成立的工资： @@ -155,151 +170,130 @@ $$ 在迭代时，我们使用{doc}`拟合价值函数迭代 `算法。 -特别地，$f$ 和所有后续迭代值都作为向量存储在一个网格。 +特别地，$f$ 和所有后续迭代值都作为向量存储在一个网格上。 -这些点通过分段线性插值转换为函数。 +这些点根据需要通过分段线性插值转换为函数。 -$Qf$ 定义中的期望项通过蒙特卡洛计算。 +$Qf$ 定义中的积分通过蒙特卡洛方法计算。 -以下类型声明帮助 Numba 进行类型推断： +以下是一个 `NamedTuple`，用于存储模型参数和数据。 -```{code-cell} python3 -job_search_data = [ - ('μ', float64), # 暂时性冲击对数均值 - ('s', float64), # 暂时性冲击对数方差 - ('d', float64), # 持续性状态位移系数 - ('ρ', float64), # 持续性状态相关系数 - ('σ', float64), # 状态波动率 - ('β', float64), # 折现因子 - ('c', float64), # 失业补助 - ('z_grid', float64[:]), # 状态空间网格 - ('e_draws', float64[:,:]) # 积分用的蒙特卡洛抽取 -] -``` +默认参数值嵌入在模型中。 + +```{code-cell} ipython +class Model(NamedTuple): + μ: float # 暂时性冲击对数均值 + s: float # 暂时性冲击对数方差 + d: float # 持续性状态位移系数 + ρ: float # 持续性状态相关系数 + σ: float # 状态波动率 + β: float # 折现因子 + c: float # 失业补助 + z_grid: jnp.ndarray + e_draws: jnp.ndarray + +def create_job_search_model(μ=0.0, s=1.0, d=0.0, ρ=0.9, σ=0.1, β=0.98, c=5.0, + mc_size=1000, grid_size=100, key=jax.random.PRNGKey(1234)): + """ + 创建一个包含计算好的网格和抽取值的 Model。 + """ + # 设置网格 + z_mean = d / (1 - ρ) + z_sd = σ / jnp.sqrt(1 - ρ**2) + k = 3 # 标准差倍数 + a, b = z_mean - k * z_sd, z_mean + k * z_sd + z_grid = jnp.linspace(a, b, grid_size) -这是一个存储数据和贝尔曼方程右侧项的类。 - -默认参数值嵌入在类中。 - -```{code-cell} ipython3 -@jitclass(job_search_data) -class JobSearch: - - def __init__(self, - μ=0.0, # 暂时性冲击对数均值 - s=1.0, # 暂时性冲击对数方差 - d=0.0, # 持续性状态位移系数 - ρ=0.9, # 持续性状态相关系数 - σ=0.1, # 状态波动率 - β=0.98, # 折现因子 - c=5, # 失业补助 - mc_size=1000, - grid_size=100): - - self.μ, self.s, self.d, = μ, s, d, - self.ρ, self.σ, self.β, self.c = ρ, σ, β, c - - # 设置网格 - z_mean = d / (1 - ρ) - z_sd = σ / np.sqrt(1 - ρ**2) - k = 3 # 标准差倍数 - a, b = z_mean - k * z_sd, z_mean + k * z_sd - self.z_grid = np.linspace(a, b, grid_size) - - # 生成并存储冲击 - np.random.seed(1234) - self.e_draws = randn(2, mc_size) - - def parameters(self): - """ - 返回所有参数作为元组。 - """ - return self.μ, self.s, self.d, \ - self.ρ, self.σ, self.β, self.c + # 生成并存储冲击 + e_draws = jax.random.normal(key, (2, mc_size)) + + return Model(μ, s, d, ρ, σ, β, c, z_grid, e_draws) ``` 接下来我们实现 $Q$ 算子。 -```{code-cell} ipython3 -@jit(parallel=True) -def Q(js, f_in, f_out): +```{code-cell} ipython +def Q(model, f_in): """ 应用算子Q。 - * js 是 JobSearch 的实例 - * f_in 和 f_out 是表示 f 和 Qf 的数组 + * model 是 Model 的一个实例 + * f_in 是表示 f 的数组 + * 返回 Qf """ - - μ, s, d, ρ, σ, β, c = js.parameters() - M = js.e_draws.shape[1] - - for i in prange(len(js.z_grid)): - z = js.z_grid[i] - expectation = 0.0 - for m in range(M): - e1, e2 = js.e_draws[:, m] + μ, s, d = model.μ, model.s, model.d + ρ, σ, β, c = model.ρ, model.σ, model.β, model.c + z_grid, e_draws = model.z_grid, model.e_draws + M = e_draws.shape[1] + + def compute_expectation(z): + def evaluate_shock(e): + e1, e2 = e[0], e[1] z_next = d + ρ * z + σ * e1 - go_val = np.interp(z_next, js.z_grid, f_in) # f(z') - y_next = np.exp(μ + s * e2) # 生成 y' - w_next = np.exp(z_next) + y_next # 生成 w' - stop_val = np.log(w_next) / (1 - β) - expectation += max(stop_val, go_val) - expectation = expectation / M - f_out[i] = np.log(c) + β * expectation + go_val = jnp.interp(z_next, z_grid, f_in) # f(z') + y_next = jnp.exp(μ + s * e2) # 生成 y' + w_next = jnp.exp(z_next) + y_next # 生成 w' + stop_val = jnp.log(w_next) / (1 - β) + return jnp.maximum(stop_val, go_val) + + expectations = jax.vmap(evaluate_shock)(e_draws.T) + return jnp.mean(expectations) + + expectations = jax.vmap(compute_expectation)(z_grid) + f_out = jnp.log(c) + β * expectations + return f_out ``` 这是一个计算 $Q$ 不动点近似值的函数。 -```{code-cell} ipython3 -def compute_fixed_point(js, - use_parallel=True, - tol=1e-4, - max_iter=1000, - verbose=True, - print_skip=25): - - f_init = np.full(len(js.z_grid), np.log(js.c)) - f_out = np.empty_like(f_init) - - # 设置循环 - f_in = f_init - i = 0 - error = tol + 1 - - while i < max_iter and error > tol: - Q(js, f_in, f_out) - error = np.max(np.abs(f_in - f_out)) - i += 1 - if verbose and i % print_skip == 0: - print(f"第 {i} 次迭代的误差为 {error}。") - f_in[:] = f_out - - if error > tol: - print("未能收敛！") - elif verbose: - print(f"\n在第 {i} 次迭代时收敛。") - - return f_out +```{code-cell} ipython +@jax.jit +def compute_fixed_point(model, tol=1e-4, max_iter=1000): + """ + 计算 Q 不动点的近似值。 + """ + + def cond_fun(loop_state): + f, i, error = loop_state + return jnp.logical_and(error > tol, i < max_iter) + + def body_fun(loop_state): + f, i, error = loop_state + f_new = Q(model, f) + error_new = jnp.max(jnp.abs(f_new - f)) + return f_new, i + 1, error_new + + # 初始状态 + f_init = jnp.full(len(model.z_grid), jnp.log(model.c)) + init_state = (f_init, 0, tol + 1) + + # 运行迭代 + f_final, iterations, final_error = jax.lax.while_loop( + cond_fun, body_fun, init_state + ) + + return f_final ``` 让我们尝试生成一个实例并求解模型。 -```{code-cell} ipython3 -js = JobSearch() +```{code-cell} ipython +model = create_job_search_model() -qe.tic() -f_star = compute_fixed_point(js, verbose=True) -qe.toc() +with qe.Timer(): + f_star = compute_fixed_point(model).block_until_ready() ``` 接下来我们将计算并绘制在{eq}`corr_mcm_barw`中定义的保留工资函数。 -```{code-cell} ipython3 -res_wage_function = np.exp(f_star * (1 - js.β)) +```{code-cell} ipython +res_wage_function = jnp.exp(f_star * (1 - model.β)) fig, ax = plt.subplots() -ax.plot(js.z_grid, res_wage_function, label="给定 $z$ 的保留工资") +ax.plot( + model.z_grid, res_wage_function, label="给定 $z$ 的保留工资" +) ax.set(xlabel="$z$", ylabel="工资") ax.legend() plt.show() @@ -307,20 +301,21 @@ plt.show() 注意保留工资随当前状态 $z$ 单调递增。 -这是因为更高的状态导致个体预测更高的未来工资，增加了等待的价值。 +这是因为更高的状态导致个体预测更高的未来工资，增加了等待的期权价值。 让我们尝试改变失业补偿金并观察其对保留工资的影响： -```{code-cell} ipython3 +```{code-cell} ipython c_vals = 1, 2, 3 fig, ax = plt.subplots() for c in c_vals: - js = JobSearch(c=c) - f_star = compute_fixed_point(js, verbose=False) - res_wage_function = np.exp(f_star * (1 - js.β)) - ax.plot(js.z_grid, res_wage_function, label=rf"$c = {c}$ 时的 $\bar w$") + model = create_job_search_model(c=c) + f_star = compute_fixed_point(model) + res_wage_function = jnp.exp(f_star * (1 - model.β)) + ax.plot(model.z_grid, res_wage_function, + label=rf"$\bar w$ at $c = {c}$") ax.set(xlabel="$z$", ylabel="工资") ax.legend() @@ -333,65 +328,90 @@ plt.show() 接下来我们研究平均失业持续时间如何随失业补偿金变化。 -为简单起见，我们将初始状态固定在 $z_t = 0$。 +为简单起见，我们将初始状态固定在 $Z_0 = 0$。 -```{code-cell} ipython3 -def compute_unemployment_duration(js, seed=1234): - - f_star = compute_fixed_point(js, verbose=False) - μ, s, d, ρ, σ, β, c = js.parameters() - z_grid = js.z_grid - np.random.seed(seed) +```{code-cell} ipython +@jax.jit +def draw_duration(key, μ, s, d, ρ, σ, β, z_grid, f_star, t_max=10_000): + """ + 为单次模拟抽取失业持续时间。 - @jit + """ def f_star_function(z): - return np.interp(z, z_grid, f_star) - - @jit - def draw_tau(t_max=10_000): - z = 0 - t = 0 - - unemployed = True - while unemployed and t < t_max: - # 生成当前工资 - y = np.exp(μ + s * np.random.randn()) - w = np.exp(z) + y - res_wage = np.exp(f_star_function(z) * (1 - β)) - # 如果最优选择是停止，记录t - if w >= res_wage: - unemployed = False - τ = t - # 否则增加数据和状态 - else: - z = ρ * z + d + σ * np.random.randn() - t += 1 - return τ - - @jit(parallel=True) - def compute_expected_tau(num_reps=100_000): - sum_value = 0 - for i in prange(num_reps): - sum_value += draw_tau() - return sum_value / num_reps - - return compute_expected_tau() + return jnp.interp(z, z_grid, f_star) + + def cond_fun(loop_state): + z, t, unemployed, key = loop_state + return jnp.logical_and(unemployed, t < t_max) + + def body_fun(loop_state): + z, t, unemployed, key = loop_state + key1, key2, key = jax.random.split(key, 3) + + # 生成当前工资 + y = jnp.exp(μ + s * jax.random.normal(key1)) + w = jnp.exp(z) + y + res_wage = jnp.exp(f_star_function(z) * (1 - β)) + + # 检查是否最优选择是停止 + accept = w >= res_wage + τ = jnp.where(accept, t, t_max) + + # 如果不接受，更新状态 + z_new = jnp.where(accept, z, + ρ * z + d + σ * jax.random.normal(key2)) + t_new = t + 1 + unemployed_new = jnp.logical_not(accept) + + return z_new, t_new, unemployed_new, key + + # 初始 loop_state: (z, t, unemployed, key) + init_state = (0.0, 0, True, key) + z_final, t_final, unemployed_final, _ = jax.lax.while_loop( + cond_fun, body_fun, init_state) + + # 如果找到工作，返回最终时间，否则返回 t_max + return jnp.where(unemployed_final, t_max, t_final) + + +def compute_unemployment_duration( + model, key=jax.random.PRNGKey(1234), num_reps=100_000 + ): + """ + 计算预期失业持续时间。 + + """ + f_star = compute_fixed_point(model) + μ, s, d = model.μ, model.s, model.d + ρ, σ, β = model.ρ, model.σ, model.β + z_grid = model.z_grid + + # 为所有模拟生成密钥 + keys = jax.random.split(key, num_reps) + + # 对模拟进行向量化处理 + τ_vals = jax.vmap( + lambda k: draw_duration(k, μ, s, d, ρ, σ, β, z_grid, f_star) + )(keys) + + return jnp.mean(τ_vals) ``` -让我们用一些可能的失业补偿金值来计算失业持续时间： +让我们用一些可能的失业补偿金值来测试一下： -```{code-cell} ipython3 -c_vals = np.linspace(1.0, 10.0, 8) -durations = np.empty_like(c_vals) +```{code-cell} ipython +c_vals = jnp.linspace(1.0, 10.0, 8) +durations = [] for i, c in enumerate(c_vals): - js = JobSearch(c=c) - τ = compute_unemployment_duration(js) - durations[i] = τ + model = create_job_search_model(c=c) + τ = compute_unemployment_duration(model, num_reps=10_000) + durations.append(τ) +durations = jnp.array(durations) ``` -这是可视化结果： +这是结果的图示。 -```{code-cell} ipython3 +```{code-cell} ipython fig, ax = plt.subplots() ax.plot(c_vals, durations) ax.set_xlabel("失业补偿金") @@ -418,18 +438,19 @@ plt.show() :class: dropdown ``` -这是一个解决方案 +这是一个解决方案： -```{code-cell} ipython3 -beta_vals = np.linspace(0.94, 0.99, 8) -durations = np.empty_like(beta_vals) +```{code-cell} ipython +beta_vals = jnp.linspace(0.94, 0.99, 8) +durations = [] for i, β in enumerate(beta_vals): - js = JobSearch(β=β) - τ = compute_unemployment_duration(js) - durations[i] = τ + model = create_job_search_model(β=β) + τ = compute_unemployment_duration(model, num_reps=10_000) + durations.append(τ) +durations = jnp.array(durations) ``` -```{code-cell} ipython3 +```{code-cell} ipython fig, ax = plt.subplots() ax.plot(beta_vals, durations) ax.set_xlabel(r"$\beta$") @@ -439,4 +460,5 @@ plt.show() 该图显示，更有耐心的个人倾向于等待更长时间才接受报价。 -```{solution-end} \ No newline at end of file +```{solution-end} +``` \ No newline at end of file From bf629d6b51d0d2d7caac23b84d9a70e77eae73b4 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Sat, 18 Jul 2026 21:20:56 +1000 Subject: [PATCH 2/6] Restore CJK font configuration dropped by the resync The resynced file keeps Chinese plot labels but reverted the import cell to the source's exact form, losing the Source Han Serif setup - Chinese in figures would render as missing glyphs. Residual #107 class found in the merge review; applied wave-wide. Co-Authored-By: Claude Fable 5 --- lectures/mccall_persist_trans.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/lectures/mccall_persist_trans.md b/lectures/mccall_persist_trans.md index 0b5ac413..ca47415e 100644 --- a/lectures/mccall_persist_trans.md +++ b/lectures/mccall_persist_trans.md @@ -66,6 +66,10 @@ translation: ```{code-cell} ipython import matplotlib.pyplot as plt +import matplotlib as mpl +FONTPATH = "fonts/SourceHanSerifSC-SemiBold.otf" +mpl.font_manager.fontManager.addfont(FONTPATH) +plt.rcParams['font.family'] = ['Source Han Serif SC'] import jax import jax.numpy as jnp import jax.random From af5fedf7d1e25ec475b146f0e4accb2475bb1f6b Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Sat, 18 Jul 2026 21:22:01 +1000 Subject: [PATCH 3/6] Point references to untranslated lectures at the English site These {doc} targets exist only in lecture-python.myst until Phase 2 translates them; qualifying with the intermediate: intersphinx prefix gives working links now, and a future resync restores local refs once the targets exist. Program decision recorded 2026-07-18 (Matt). Co-Authored-By: Claude Fable 5 --- lectures/mccall_persist_trans.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lectures/mccall_persist_trans.md b/lectures/mccall_persist_trans.md index ca47415e..edc06b57 100644 --- a/lectures/mccall_persist_trans.md +++ b/lectures/mccall_persist_trans.md @@ -49,7 +49,7 @@ translation: 在{doc}`基准模型 `中，工资报价在时间上是独立同分布的，这是不现实的。 -在{doc}`工作搜寻 III `中，我们使用马尔可夫链引入了相关工资抽取，但同时也加入了离职因素。 +在{doc}`工作搜寻 III `中，我们使用马尔可夫链引入了相关工资抽取，但同时也加入了离职因素。 这里我们采取不同的方法：我们通过一个AR(1)过程来建模持续性成分，再加上一个暂时性冲击，同时回到假设工作是永久性的（如{doc}`基准模型 `中一样）。 From b5f14a689faeb9c3fbd84a27b05576ca63729f51 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Sun, 19 Jul 2026 14:05:00 +1000 Subject: [PATCH 4/6] Restore trailing newline (engine issue tracked in QuantEcon/action-translation#116) Co-Authored-By: Claude Fable 5 --- lectures/mccall_persist_trans.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lectures/mccall_persist_trans.md b/lectures/mccall_persist_trans.md index edc06b57..25094c20 100644 --- a/lectures/mccall_persist_trans.md +++ b/lectures/mccall_persist_trans.md @@ -465,4 +465,4 @@ plt.show() 该图显示，更有耐心的个人倾向于等待更长时间才接受报价。 ```{solution-end} -``` \ No newline at end of file +``` From f483292549d38fdaab330d5860989bb689ce8299 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Sun, 19 Jul 2026 14:35:35 +1000 Subject: [PATCH 5/6] Add missing bibtex keys MaCurdy1982, Meghir2004 to fix dangling citations The resync of mccall_persist_trans.md pulled in {cite}`MaCurdy1982` and {cite}`Meghir2004` from upstream, but the shared quant-econ.bib was not synced, so the strict build (-W) failed on "could not find bibtex key". Entries copied verbatim from QuantEcon/lecture-python.myst. Co-Authored-By: Claude Fable 5 --- lectures/_static/quant-econ.bib | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/lectures/_static/quant-econ.bib b/lectures/_static/quant-econ.bib index 0fd46856..9aa52494 100644 --- a/lectures/_static/quant-econ.bib +++ b/lectures/_static/quant-econ.bib @@ -2645,3 +2645,26 @@ @article{fischer2024improving journal={arXiv preprint arXiv:2410.16076}, year={2024} } + +@article{MaCurdy1982, + title={The use of time series processes to model the error structure of earnings in a longitudinal data analysis}, + author={MaCurdy, Thomas E.}, + journal={Journal of Econometrics}, + volume={18}, + number={1}, + pages={83--114}, + year={1982}, + publisher={Elsevier} +} + +@article{Meghir2004, + title={Income variance dynamics and heterogeneity}, + author={Meghir, Costas and Pistaferri, Luigi}, + journal={Econometrica}, + volume={72}, + number={1}, + pages={1--32}, + year={2004}, + publisher={Wiley Online Library} +} + From 049cb33e782d46129a7c6bceda3361a580b05974 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Sun, 19 Jul 2026 15:26:27 +1000 Subject: [PATCH 6/6] Align quant-econ.bib to canonical union to avoid EOF merge conflicts All four bibtex keys the wave needs (Lucas_Prescott_1971, Blume_Easley2006, MaCurdy1982, Meghir2004) are now present identically across the affected branches so the resync PRs merge without append-at-EOF conflicts. Entries copied verbatim from QuantEcon/lecture-python.myst. Co-Authored-By: Claude Fable 5 --- lectures/_static/quant-econ.bib | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/lectures/_static/quant-econ.bib b/lectures/_static/quant-econ.bib index 9aa52494..7c481edf 100644 --- a/lectures/_static/quant-econ.bib +++ b/lectures/_static/quant-econ.bib @@ -2646,6 +2646,27 @@ @article{fischer2024improving year={2024} } +@article{Lucas_Prescott_1971, + author = {Lucas, Robert E., Jr. and Prescott, Edward C.}, + title = {Investment under Uncertainty}, + journal = {Econometrica}, + volume = {39}, + number = {5}, + pages = {659--681}, + year = {1971} +} + + +@article{Blume_Easley2006, + author = {Blume, Lawrence and Easley, David}, + title = {If You're So Smart, Why Aren't You Rich? {B}elief Selection in Complete and Incomplete Markets}, + journal = {Econometrica}, + volume = {74}, + number = {4}, + pages = {929--966}, + year = {2006} +} + @article{MaCurdy1982, title={The use of time series processes to model the error structure of earnings in a longitudinal data analysis}, author={MaCurdy, Thomas E.},