@@ -4,7 +4,7 @@ jupytext:
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extension : .md
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format_name : myst
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format_version : 0.13
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- jupytext_version : 1.16.6
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+ jupytext_version : 1.16.7
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kernelspec :
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display_name : Python 3 (ipykernel)
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language : python
@@ -750,7 +750,7 @@ def plot_results(solution, k_ss, c_ss, shocks, shock_param,
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R_bar_path = compute_R_bar_path(shocks, k_path, model, S)
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axes[2].plot(R_bar_path[:T], linestyle=linestyle, label=label)
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- axes[2].set_title('$\overline{R}$')
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+ axes[2].set_title(r '$\overline{R}$')
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axes[2].axhline(1 / model.β, linestyle='--', color='black')
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η_path = compute_η_path(k_path, model, S=T)
@@ -1041,7 +1041,7 @@ Indeed, {eq}`eq:euler_house` or {eq}`eq:diff_second` indicates that a foreseen i
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crease in $\tau_{ct}$ (i.e., a decrease in $(1+\tau_{ct})$
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$(1+\tau_{ct+1})$) operates like an increase in $\tau_{kt}$.
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- The following figure portrays the response to a foreseen increase in the consumption tax $\tau_c$.
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+ The following figure portrays the response to a foreseen increase in the consumption tax $\tau_c$.
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```{code-cell} ipython3
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shocks = {
@@ -1101,7 +1101,6 @@ The figure shows that:
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- Transition dynamics push $k_t$ (capital stock) toward a new, lower steady-state level. In the new steady state:
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- Consumption is lower due to reduced output from the lower capital stock.
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- Smoother consumption paths occur when $\gamma = 2$ than when $\gamma = 0.2$.
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@@ -1111,8 +1110,6 @@ foreseen one-time change in a policy variable (a "pulse").
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**Experiment 4: Foreseen one-time increase in $g$ from 0.2 to 0.4 in period 10, after which $g$ returns to 0.2 forever**
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-
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```{code-cell} ipython3
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g_path = np.repeat(0.2, S + 1)
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g_path[10] = 0.4
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- Before $t = 10$, capital accumulates as interest rate changes induce households to prepare for the anticipated increase in government spending.
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- At $t = 10$, the capital stock sharply decreases as the government consumes part of it.
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- $\bar{R}$ jumps above its steady-state value due to the capital reduction and then gradually declines toward its steady-state level.
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+
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+++
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### Method 2: Residual Minimization
@@ -1386,6 +1384,3 @@ shocks = {
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experiment_model(shocks, S, model, run_min, plot_results, 'g')
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```
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-
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- ``` {solution-end}
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- ```
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