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🌐 [translation-sync] [hansen_singleton_1983.md] Update np.random → Generator API #226

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mmcky merged 2 commits into from
Aug 13, 2026
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🌐 [translation-sync] [hansen_singleton_1983.md] Update np.random → Generator API #226

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5386746
Update translation: lectures/hansen_singleton_1983.md
mmcky Jul 31, 2026
7ebb015
Update translation: .translate/state/hansen_singleton_1983.md.yml
mmcky Jul 31, 2026
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10 changes: 5 additions & 5 deletions .translate/state/hansen_singleton_1983.md.yml
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@@ -1,6 +1,6 @@
source-sha: 4ab3365729d5afd5f25c62d73c505034e090a75c
synced-at: "2026-07-22"
model: claude-opus-4-8
mode: NEW
source-sha: 25044edf163f6ffb3e4647f80e3c738671f8d8ea
synced-at: "2026-07-31"
model: claude-sonnet-5
mode: UPDATE
section-count: 12
tool-version: 0.20.0
tool-version: 0.24.0
11 changes: 4 additions & 7 deletions lectures/hansen_singleton_1983.md
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Expand Up @@ -530,8 +530,7 @@ def simulate_restricted_var(
"""
从受限模型模拟 [对数消费增长, 对数回报]。
"""
if seed is not None:
np.random.seed(seed)
rng = np.random.default_rng(seed)

if len(params) != 6 + 2 * n_lags:
raise ValueError("Parameter vector length must be 6 + 2 * n_lags.")
Expand Down Expand Up @@ -566,7 +565,7 @@ def simulate_restricted_var(
for lag in range(1, n_lags + 1):
lag_stack.append(y[t - lag, :])
lag_vec = np.concatenate(lag_stack)
shock = np.random.multivariate_normal(np.zeros(2), Σ_v)
shock = rng.multivariate_normal(np.zeros(2), Σ_v)
y[t, :] = np.linalg.solve(A0, A1 @ lag_vec + μ + shock)

return y[burn_in:, :]
Expand Down Expand Up @@ -930,7 +929,7 @@ def estimate_mle(data, n_lags, verbose=False):
}
```

下面的残差诊断总结了正态性和序列相关性检查
残差诊断下面总结了正态性和序列相关性检查

```{code-cell} ipython3
def residual_diagnostics(resid):
Expand Down Expand Up @@ -1056,7 +1055,6 @@ display_table(sim_results, fmt={

点估计接近真实参数，而针对"真实值正确"这一原假设的 t 统计量在数量级上很小，与抽样变异一致。


## 偏好参数与似然比检验

受限系统通过跨方程限制嵌入了偏好参数。
Expand Down Expand Up @@ -1420,7 +1418,6 @@ display_table(spread_pretty, fmt={

现在我们将 {cite:t}`hansen1983stochastic` 的最大似然估计器应用于真实数据。


### 数据

本讲座和配套讲座 {doc}`hansen_singleton_1982` 使用相同的数据构造。
Expand Down Expand Up @@ -1841,4 +1838,4 @@ if diag_fit["converged"] and resid is not None:

他们构建了一个不完整的概率模型，止步于不指定似然函数。

为了推进，他们使用了广义矩估计法（GMM）估计器来估计出现在欧拉方程中的关键参数。
为了推进，他们使用了广义矩估计法（GMM）估计器来估计出现在欧拉方程中的关键参数。
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