Updated one NB due to HARK PR 1713 (#238)

Format for `calc_expectation` with `DiscreteDistributionLabeled` has changed. Also adjusted brackets in one equation.

Author: mnwhite

notebooks/LC-Model-Expected-Vs-Realized-Income-Growth.ipynb

@@ -173,7 +173,7 @@
     "Using this notation, we construct all the necessary inputs to the regressors. The main input is the expected income growth of every agent at every time period, which is given by\n",
     "\\begin{equation}\n",
     "\\begin{split}\n",
-    "\\Ex_t[\\yLvl_{t+1}/\\yLvl_{t}] &= \\mathbb{E}_t[\\left(\\frac{\\theta_{t+1}\\pLvl_{t} \\PermGroFac_{t+1} \\PermShk_{t+1}}{\\theta_{t}P_{t}}\\right)]\\\\\n",
+    "\\Ex_t[\\yLvl_{t+1}/\\yLvl_{t}] &= \\mathbb{E}_t \\left[ \\left(\\frac{\\theta_{t+1}\\pLvl_{t} \\PermGroFac_{t+1} \\PermShk_{t+1}}{\\theta_{t}P_{t}}\\right) \\right]\\\\\n",
     " &= \\left(\\frac{\\PermGroFac_{t+1}}{\\theta_{t}}\\right)\\\\\n",
     "\\Ex_t[\\yLog_{t+1} - \\yLog_{t}] & = \\log \\Gamma_{t+1}-\\log \\theta_t\n",
     "\\end{split}\n",
@@ -188,7 +188,7 @@
    "outputs": [],
    "source": [
     "exp = [\n",
-    "    calc_expectation(Agent.IncShkDstn[i], func=lambda x: x[0] * x[1])\n",
+    "    calc_expectation(Agent.IncShkDstn[i], func=lambda x: x[\"PermShk\"] * x[\"TranShk\"])\n",
     "    for i in range(Agent.T_cycle)\n",
     "]\n",
     "exp_df = pd.DataFrame(\n",
@@ -249,8 +249,8 @@
       "Dep. Variable:                 ExpBin   R-squared:                        0.1958\n",
       "Estimator:                   PanelOLS   R-squared (Between):             -0.0938\n",
       "No. Observations:              100000   R-squared (Within):               0.1958\n",
-      "Date:                Tue, Dec 02 2025   R-squared (Overall):              0.1888\n",
-      "Time:                        10:21:36   Log-likelihood                -1.288e+05\n",
+      "Date:                Mon, Feb 02 2026   R-squared (Overall):              0.1888\n",
+      "Time:                        21:29:53   Log-likelihood                -1.288e+05\n",
       "Cov. Estimator:            Unadjusted                                           \n",
       "                                        F-statistic:                   2.376e+04\n",
       "Entities:                        2435   P-value                           0.0000\n",
@@ -332,7 +332,7 @@
    "outputs": [],
    "source": [
     "exp = [\n",
-    "    calc_expectation(Agent_nt.IncShkDstn[i], func=lambda x: x[0] * x[1])\n",
+    "    calc_expectation(Agent_nt.IncShkDstn[i], func=lambda x: x[\"PermShk\"] * x[\"TranShk\"])\n",
     "    for i in range(Agent_nt.T_cycle)\n",
     "]\n",
     "exp_df = pd.DataFrame(\n",
@@ -381,8 +381,8 @@
       "Dep. Variable:                 ExpBin   R-squared:                        0.0084\n",
       "Estimator:                   PanelOLS   R-squared (Between):             -0.0257\n",
       "No. Observations:              100000   R-squared (Within):               0.0084\n",
-      "Date:                Tue, Dec 02 2025   R-squared (Overall):              0.0088\n",
-      "Time:                        10:22:07   Log-likelihood                -1.393e+05\n",
+      "Date:                Mon, Feb 02 2026   R-squared (Overall):              0.0088\n",
+      "Time:                        21:29:58   Log-likelihood                -1.393e+05\n",
       "Cov. Estimator:            Unadjusted                                           \n",
       "                                        F-statistic:                      826.23\n",
       "Entities:                        2435   P-value                           0.0000\n",