merged.ipynb

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    "<div style=\"text-align: left;\">\n",
    "    <h1>Uber Versus Public Transit</h1>\n",
    "    <h2>Final Project</h2>\n",
    "    <h4>ECON 434 - Machine Learning and Big Data for Economists</h3>\n",
    "    <div style=\"padding: 20px 0;\">\n",
    "        <hr style=\"border: 0; height: 1px; background-image: linear-gradient(to right, rgba(0, 0, 0, 0), rgba(0, 0, 0, 0.75), rgba(0, 0, 0, 0));\">\n",
    "        <p><em>Luis-Alejandro Samayoa-Alvarado</em><br>\n",
    "        <p><em>Mauricio Vargas-Estrada</em><br>\n",
    "        <p><em>Nikolaos Papadatos</em><br>\n",
    "        <p><em>William Borelli Ebert</em><br>\n",
    "        <p>Master of Quantitative Economics<br>\n",
    "        University of California - Los Angeles</p>\n",
    "        <hr style=\"border: 0; height: 1px; background-image: linear-gradient(to right, rgba(0, 0, 0, 0), rgba(0, 0, 0, 0.75), rgba(0, 0, 0, 0));\">\n",
    "    </div>\n",
    "</div>"
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    "In this project, we study whether Uber complements (helps) or substitutes (hurts) public transit. On the one hand, Uber can substitute public transit if riders decide to choose Uber instead of public transit. On the other hand, Uber can complement public transit if riders take Uber from home to public transit stop, which can make public transit more attractive than driving a car. The net effect is unclear and is a subject of intense policy debate.\n",
    "\n",
    "We will expand on the original set of results presented in Hall, Palsson, and Price (2018), “Is Uber a substitute or a complement for public transit,” *Journal of Urban Economics*, which is available on the class website. We will use their dataset, which is also available on the class website. In the dataset, a unit of ob- servation is a public transit agency in a given year-month. The dataset includes information on both the transit agencies and on the Metropolitan Statistical Areas (MSA) where they operate. For each time period, the dataset contains values for the following variables:\n",
    "\n",
    "1. $UPTTotal$ – the number of rides for the public transit agency;\n",
    "2. $treatUberX$ - a dummy for Uber presence in the corresponding MSA;\n",
    "3. $treatGTNotStd$ - a variable measuring google search intensity for Uber in the corresponding MSA;\n",
    "4. $popestimate$ - population in the corresponding MSA;\n",
    "5. $employment$ - employment in the corresponding MSA;\n",
    "6. $aveFareTotal$ - average fare for the public transit agency;\n",
    "7. $VRHTTotal$ - vehicle hours for the public transit agency;\n",
    "8. $VOMSTotal$ - number of vehicles employed by the public transit agency;\n",
    "9. $VRMTotal$ - vehicle miles for the public transit agency;\n",
    "10. $gasPrice$ - gas price in the corresponding MSA.\n",
    "\n",
    "In this dataset, $treatUberX$ and $treatGTNotStd$ is qualitative and quantitative measures for the same thing: Uber presence in the MSA. We can run regressions using either of these two variables and then check whether results are robust if the other variable is used.\n",
    "\n",
    "There are two variations in this dataset that allow us to study the effect of Uber on public transit. First, in any given time period, Uber is present in some MSAs but not others. We can thus study the effect of Uber by comparing these MSAs. Second, for any given MSA, we have data on time periods both before and after Uber was introduced in this MSA. We can thus study the effect of Uber by comparing these time periods. By working with panel data, we are able to employ both variations at the same time.\n",
    "\n",
    "To study the effect of Uber on public transit, we let $Y_{it}$ be $UPTTotal$, $D_{it}$ be either $treatUberX$ or $treatGTNotStd$, and $W_{it}$ be the vector including remaining variables: $popestimate$, $employment$, $aveFareTotal$, $VRHTTotal$, $VOMSTotal$, $VRMTotal$, $gasPrice$. We then run the following regressions:"
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "from scipy.stats import norm\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from linearmodels.panel import PanelOLS\n",
    "\n",
    "from src.BCCH import BCCH\n",
    "from src.double_lasso import double_lasso\n",
    "from src.double_lasso_OLS import double_lasso_OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3f10f679",
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   "source": [
    "# Load data\n",
    "data = pd.read_csv(\"data/uber_dataset.csv\", index_col=0)\n",
    "\n",
    "# Drop treatGTNotStd variable\n",
    "data = data.drop(columns=\"treatGTNotStd\")\n",
    "\n",
    "# Drop rows with missing values\n",
    "data = data.dropna()\n",
    "\n",
    "# If treatUberX is greater than 0.5, set it to 1, if not, set it to 0\n",
    "data[\"treatUberX\"] = (data[\"treatUberX\"] > 0.5).astype(int)\n",
    "\n",
    "# Create interaction between agency and city\n",
    "data[\"agency_city\"] = data[\"agency\"] + data[\"city\"]\n",
    "\n",
    "# Calculate the median population\n",
    "data_copy = data[[\"UPTTotal\", \"popestimate\", \"city\"]].copy()\n",
    "p = data_copy.groupby([\"city\"]).median()\n",
    "median_population = p[\"popestimate\"].median()\n",
    "\n",
    "# Create the dummy variable P_{it}\n",
    "data[\"P\"] = (data[\"popestimate\"] > median_population).astype(int)\n",
    "\n",
    "# Calculate the median rides\n",
    "median_rides = p[\"UPTTotal\"].median()\n",
    "\n",
    "# Create the dummy variable F_{it}\n",
    "data[\"F\"] = (data[\"UPTTotal\"] > median_rides).astype(int)\n",
    "\n",
    "# Create the interaction term P_{it} * D_{it}\n",
    "data[\"PxD\"] = data[\"P\"] * data[\"treatUberX\"]\n",
    "\n",
    "# Create the interaction term F_{it} * D_{it}\n",
    "data[\"FxD\"] = data[\"F\"] * data[\"treatUberX\"]"
   ]
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    "In line with the approach proposed by Hall, Palsson, and Price (2018), logarithmic transformations were applied to the set of control variables. The variables considered were the average fare, the maximum number of vehicles in operation within the month, vehicle-hours of service, vehicle-miles of service, regional gas prices, employment statistic, and population size. The motivation is linked to the fluctuation of magnitudes in the control variables, reducing the impact of possible outlier impact, and handle the skewness of distributions."
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    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        1.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        OLS: log \\(Y_{it} = \\alpha + D_{it} \\beta + W_{it}' \\gamma + \\epsilon_{it}\\).\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f39e9bef",
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   "source": [
    "# Convert 'dateSurvey' to datetime format\n",
    "data[\"dateSurvey\"] = pd.to_datetime(data[\"dateSurvey\"], errors=\"coerce\")\n",
    "\n",
    "# Set the index to be a MultiIndex for panel data\n",
    "data = data.set_index([\"agency_city\", \"dateSurvey\"])\n",
    "\n",
    "# Define the dependent variable and independent variables\n",
    "Y = np.log(data[\"UPTTotal\"])\n",
    "D = data[\"treatUberX\"]\n",
    "W = data[\n",
    "    [\n",
    "        \"popestimate\",\n",
    "        \"employment\",\n",
    "        \"aveFareTotal\",\n",
    "        \"VRHTotal\",\n",
    "        \"VOMSTotal\",\n",
    "        \"VRMTotal\",\n",
    "        \"gasPrice\",\n",
    "    ]\n",
    "]\n",
    "PxD = data[\"PxD\"]\n",
    "FxD = data[\"FxD\"]\n",
    "\n",
    "# Scale the independent variables with log transformation\n",
    "W_scaled_df = np.log(W)\n",
    "\n",
    "# Create the design matrices\n",
    "X = pd.concat([D, W_scaled_df], axis=1)\n",
    "\n",
    "# Add constant to the models\n",
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "38a8e744",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               UPTTotal   R-squared:                       0.833\n",
      "Model:                            OLS   Adj. R-squared:                  0.833\n",
      "Method:                 Least Squares   F-statistic:                 4.463e+04\n",
      "Date:                Sat, 08 Jun 2024   Prob (F-statistic):               0.00\n",
      "Time:                        19:27:17   Log-Likelihood:                -84548.\n",
      "No. Observations:               71768   AIC:                         1.691e+05\n",
      "Df Residuals:                   71759   BIC:                         1.692e+05\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "const           -0.7976      0.068    -11.676      0.000      -0.931      -0.664\n",
      "treatUberX       0.0382      0.010      3.890      0.000       0.019       0.057\n",
      "popestimate     -0.9271      0.034    -27.371      0.000      -0.994      -0.861\n",
      "employment       0.9909      0.034     29.363      0.000       0.925       1.057\n",
      "aveFareTotal    -0.1277      0.004    -32.388      0.000      -0.135      -0.120\n",
      "VRHTotal         1.3417      0.009    143.916      0.000       1.323       1.360\n",
      "VOMSTotal       -0.2376      0.008    -30.390      0.000      -0.253      -0.222\n",
      "VRMTotal         0.0688      0.011      6.400      0.000       0.048       0.090\n",
      "gasPrice         0.2136      0.013     16.540      0.000       0.188       0.239\n",
      "==============================================================================\n",
      "Omnibus:                     9050.948   Durbin-Watson:                   0.080\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           106297.994\n",
      "Skew:                          -0.089   Prob(JB):                         0.00\n",
      "Kurtosis:                       8.959   Cond. No.                         606.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Fit the OLS model\n",
    "model1 = sm.OLS(Y, X).fit()\n",
    "\n",
    "# Print the results\n",
    "print(model1.summary())"
   ]
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    "This regression gives us an initial estimation of the effects of Uber presence on public transit ridership, showing a 3.82% increase in ridership, suggesting that Uber complements public transit. Higher population estimates are associated with a decrease in ridership, while higher employment levels are linked to a significant increase. Higher average fares negatively affect ridership, while more vehicle hours and miles significantly increase ridership. The results also suggest that more vehicles in service decrease ridership, and higher gas prices lead to a significant increase in ridership."
   ]
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   "cell_type": "markdown",
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    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        2.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        OLS: log \\(Y_{it} = \\eta_i + \\delta_t + D_{it} \\beta + W_{it}' \\gamma + \\epsilon_{it}\\).\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a5612340",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:               UPTTotal   R-squared:                        0.3295\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.9395\n",
      "No. Observations:               71768   R-squared (Within):               0.3476\n",
      "Date:                Sat, Jun 08 2024   R-squared (Overall):              0.9503\n",
      "Time:                        19:27:18   Log-likelihood                   -5543.7\n",
      "Cov. Estimator:            Unadjusted                                           \n",
      "                                        F-statistic:                      4357.3\n",
      "Entities:                         676   P-value                           0.0000\n",
      "Avg Obs:                       106.17   Distribution:                 F(8,70941)\n",
      "Min Obs:                       6.0000                                           \n",
      "Max Obs:                       144.00   F-statistic (robust):             4357.3\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                     144   Distribution:                 F(8,70941)\n",
      "Avg Obs:                       498.39                                           \n",
      "Min Obs:                       161.00                                           \n",
      "Max Obs:                       586.00                                           \n",
      "                                                                                \n",
      "                              Parameter Estimates                               \n",
      "================================================================================\n",
      "              Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "--------------------------------------------------------------------------------\n",
      "treatUberX      -0.0354     0.0051    -6.9140     0.0000     -0.0454     -0.0254\n",
      "popestimate      0.2789     0.0556     5.0147     0.0000      0.1699      0.3878\n",
      "employment       0.2677     0.0397     6.7474     0.0000      0.1899      0.3455\n",
      "aveFareTotal    -0.0996     0.0030    -33.542     0.0000     -0.1054     -0.0938\n",
      "VRHTotal         0.3052     0.0071     43.084     0.0000      0.2913      0.3190\n",
      "VOMSTotal        0.2314     0.0058     40.090     0.0000      0.2201      0.2427\n",
      "VRMTotal         0.2664     0.0071     37.501     0.0000      0.2525      0.2803\n",
      "gasPrice        -0.0407     0.0394    -1.0337     0.3013     -0.1179      0.0365\n",
      "================================================================================\n",
      "\n",
      "F-test for Poolability: 697.27\n",
      "P-value: 0.0000\n",
      "Distribution: F(818,70941)\n",
      "\n",
      "Included effects: Entity, Time\n"
     ]
    }
   ],
   "source": [
    "# Ensure Y is a Series rather than a DataFrame\n",
    "Y = Y.squeeze()\n",
    "\n",
    "# Create the design matrices\n",
    "X = pd.concat([D, W_scaled_df], axis=1)\n",
    "\n",
    "# Fit the Panel OLS models with individual and time fixed effects\n",
    "model2 = PanelOLS(Y, X, entity_effects=True, time_effects=True, drop_absorbed=True)\n",
    "result2 = model2.fit()\n",
    "\n",
    "# Print the summaries to check the fixed effects inclusion\n",
    "print(result2.summary)"
   ]
  },
  {
   "cell_type": "markdown",
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    "To account for bias across agencies and within time, we use fixed effects. Using this model, we estimate a 3.54% decrease in ridership, suggesting that Uber substitutes public transit. Higher population estimates are associated with an increase in ridership, in contrast to the previous model, while higher employment levels are linked to an increase. Higher average fares negatively affect ridership, while more vehicle hours and miles significantly increase ridership. The results also suggest that more vehicles in service increase ridership, and, paradoxically, gas prices have a negative but significant effect, unlike in the first OLS model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144c2269",
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   "source": [
    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        3.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        OLS: log \\(Y_{it} = \\eta_i + \\delta_t + D_{it} \\beta_1 + D_{it} Pit_i \\beta_2 + W_{it}' \\gamma + \\epsilon_{it}\\), where \\(Pit_i\\) is a dummy that takes value 1 if the corresponding MSA has population larger than the median population in the dataset and 0 otherwise.\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d48975ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:               UPTTotal   R-squared:                        0.0103\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.0023\n",
      "No. Observations:               71768   R-squared (Within):              -0.0002\n",
      "Date:                Sat, Jun 08 2024   R-squared (Overall):              0.0062\n",
      "Time:                        19:27:19   Log-likelihood                -1.951e+04\n",
      "Cov. Estimator:            Unadjusted                                           \n",
      "                                        F-statistic:                      82.066\n",
      "Entities:                         676   P-value                           0.0000\n",
      "Avg Obs:                       106.17   Distribution:                 F(9,70940)\n",
      "Min Obs:                       6.0000                                           \n",
      "Max Obs:                       144.00   F-statistic (robust):             82.066\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                     144   Distribution:                 F(9,70940)\n",
      "Avg Obs:                       498.39                                           \n",
      "Min Obs:                       161.00                                           \n",
      "Max Obs:                       586.00                                           \n",
      "                                                                                \n",
      "                              Parameter Estimates                               \n",
      "================================================================================\n",
      "              Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "--------------------------------------------------------------------------------\n",
      "treatUberX       0.0075     0.0177     0.4253     0.6706     -0.0272      0.0423\n",
      "PxD             -0.0505     0.0177    -2.8449     0.0044     -0.0852     -0.0157\n",
      "popestimate   -9.98e-08  1.695e-08    -5.8896     0.0000   -1.33e-07  -6.659e-08\n",
      "employment    1.769e-07  2.226e-08     7.9470     0.0000   1.333e-07   2.206e-07\n",
      "aveFareTotal    -0.0012     0.0005    -2.5437     0.0110     -0.0021     -0.0003\n",
      "VRHTotal      8.564e-07  1.693e-07     5.0596     0.0000   5.247e-07   1.188e-06\n",
      "VOMSTotal        0.0005   2.27e-05     20.119     0.0000      0.0004      0.0005\n",
      "VRMTotal     -2.596e-08   7.54e-09    -3.4424     0.0006  -4.074e-08  -1.118e-08\n",
      "gasPrice        -0.0096     0.0167    -0.5764     0.5643     -0.0423      0.0231\n",
      "================================================================================\n",
      "\n",
      "F-test for Poolability: 2032.5\n",
      "P-value: 0.0000\n",
      "Distribution: F(818,70940)\n",
      "\n",
      "Included effects: Entity, Time\n"
     ]
    }
   ],
   "source": [
    "# Create the design matrices\n",
    "X1 = pd.concat([D, PxD, W], axis=1)\n",
    "\n",
    "# Fit the Panel OLS models with individual and time fixed effects\n",
    "model3 = PanelOLS(Y, X1, entity_effects=True, time_effects=True, drop_absorbed=True)\n",
    "result3 = model3.fit()\n",
    "\n",
    "print(result3.summary)"
   ]
  },
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    "We expand our analysis to examine how the effect of Uber differs based on the population of the MSA. We add $P_{it}$, a dummy variable that takes a value of 1 if the population is larger than the median population in the dataset and 0 otherwise. Using this model, we estimate a 0.75% increase in ridership, which is not statistically significant. Higher average fares negatively affect ridership, while more vehicle hours and miles seem to slightly increase ridership. The results also suggest that more vehicles in service significantly increase ridership, while more vehicle miles slightly decrease it. Again, gas prices do not have a significant effect in this model. Overall, we observe that the majority of the coefficients are very close to 0, with confidence intervals indicating that they could have either a positive or negative overall effect on ridership."
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        4.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        OLS: log \\(Y_{it} = \\eta_i + \\delta_t + D_{it} \\beta_1 + D_{it} Fit_i \\beta_2 + W_{it}' \\gamma + \\epsilon_{it}\\), where \\(Fit_i\\) is a dummy that takes value 1 if the number of rides of the public travel agency is larger than the median number of rides among all public transit agencies in the dataset.\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b7c9d433",
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     "text": [
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:               UPTTotal   R-squared:                        0.0102\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.0011\n",
      "No. Observations:               71768   R-squared (Within):              -0.0011\n",
      "Date:                Sat, Jun 08 2024   R-squared (Overall):              0.0029\n",
      "Time:                        19:27:20   Log-likelihood                -1.952e+04\n",
      "Cov. Estimator:            Unadjusted                                           \n",
      "                                        F-statistic:                      81.376\n",
      "Entities:                         676   P-value                           0.0000\n",
      "Avg Obs:                       106.17   Distribution:                 F(9,70940)\n",
      "Min Obs:                       6.0000                                           \n",
      "Max Obs:                       144.00   F-statistic (robust):             81.376\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                     144   Distribution:                 F(9,70940)\n",
      "Avg Obs:                       498.39                                           \n",
      "Min Obs:                       161.00                                           \n",
      "Max Obs:                       586.00                                           \n",
      "                                                                                \n",
      "                              Parameter Estimates                               \n",
      "================================================================================\n",
      "              Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "--------------------------------------------------------------------------------\n",
      "treatUberX      -0.0309     0.0089    -3.4692     0.0005     -0.0484     -0.0135\n",
      "FxD             -0.0124     0.0089    -1.3940     0.1633     -0.0299      0.0050\n",
      "popestimate  -1.038e-07  1.687e-08    -6.1525     0.0000  -1.369e-07  -7.074e-08\n",
      "employment    1.737e-07  2.223e-08     7.8136     0.0000   1.301e-07   2.173e-07\n",
      "aveFareTotal    -0.0012     0.0005    -2.5702     0.0102     -0.0021     -0.0003\n",
      "VRHTotal      8.576e-07  1.693e-07     5.0665     0.0000   5.259e-07   1.189e-06\n",
      "VOMSTotal        0.0005  2.273e-05     20.160     0.0000      0.0004      0.0005\n",
      "VRMTotal     -2.602e-08  7.541e-09    -3.4505     0.0006   -4.08e-08  -1.124e-08\n",
      "gasPrice        -0.0101     0.0167    -0.6082     0.5431     -0.0428      0.0226\n",
      "================================================================================\n",
      "\n",
      "F-test for Poolability: 1901.9\n",
      "P-value: 0.0000\n",
      "Distribution: F(818,70940)\n",
      "\n",
      "Included effects: Entity, Time\n"
     ]
    }
   ],
   "source": [
    "# Create the design matrices\n",
    "X2 = pd.concat([D, FxD, W], axis=1)\n",
    "\n",
    "# Fit the Panel OLS models with individual and time fixed effects\n",
    "model4 = PanelOLS(Y, X2, entity_effects=True, time_effects=True, drop_absorbed=True)\n",
    "result4 = model4.fit()\n",
    "\n",
    "print(result4.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3679d745",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create Residuals\n",
    "ols3_res= result3.resids\n",
    "ols4_res = result4.resids\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 7), constrained_layout=True)\n",
    "\n",
    "# First plot\n",
    "axes[0].scatter(ols3_res, np.log(data['UPTTotal']), label='Residuals', \n",
    "                color='b', alpha=0.6, edgecolor='w', s=80)\n",
    "axes[0].set_xlabel('Residuals', fontsize=12)\n",
    "axes[0].set_ylabel('Log of UPTTotal', fontsize=12)\n",
    "axes[0].set_title('Residuals vs. UPTTotal for OLS with DP Interaction', fontsize=14)\n",
    "axes[0].legend()\n",
    "axes[0].grid(True, which='both', linestyle='--', linewidth=0.7)\n",
    "\n",
    "# Second plot\n",
    "axes[1].scatter(ols4_res, np.log(data['UPTTotal']), label='Residuals', \n",
    "                color='r', alpha=0.6, edgecolor='w', s=80)\n",
    "axes[1].set_xlabel('Residuals', fontsize=12)\n",
    "axes[1].set_ylabel('Log of UPTTotal', fontsize=12)\n",
    "axes[1].set_title('Residuals vs. UPTTotal for OLS with DF Interaction', fontsize=14)\n",
    "axes[1].legend()\n",
    "axes[1].grid(True, which='both', linestyle='--', linewidth=0.7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "02b2a8fb",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Figure\n",
    "obs_count = list(range(1, len(ols3_res) + 1))\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 7), constrained_layout=True)\n",
    "\n",
    "# First plot\n",
    "axes[0].plot(obs_count, ols3_res, label='Residual', color='b', linewidth=2, \n",
    "             marker='o', markersize=5, markerfacecolor='white')\n",
    "axes[0].set_xlabel('Observation', fontsize=12)\n",
    "axes[0].set_ylabel('Residuals', fontsize=12)\n",
    "axes[0].set_title('Residuals with DP Interaction', fontsize=14)\n",
    "axes[0].legend()\n",
    "axes[0].grid(True, which='both', linestyle='--', linewidth=0.7)\n",
    "\n",
    "# Second plot\n",
    "axes[1].plot(obs_count, ols4_res, label='Residual', color='r', linewidth=2, \n",
    "             marker='o', markersize=5, markerfacecolor='white')\n",
    "axes[1].set_xlabel('Observation', fontsize=12)\n",
    "axes[1].set_ylabel('Residual', fontsize=12)\n",
    "axes[1].set_title('Residuals OLS with DF Interaction', fontsize=14)\n",
    "axes[1].legend()\n",
    "axes[1].grid(True, which='both', linestyle='--', linewidth=0.7)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a17952a8",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "Instead of examining how the effect of Uber differs based on the population, we now account for heterogeneity based on the number of riders using public transit before Uber arrived. We add $F_{it}$, a dummy variable that takes a value of 1 if the number of rides of the public transit agency is larger than the median number of rides among all public transit agencies in the dataset. Using this model, we estimate a 3.09% decrease in ridership, suggesting that Uber substitutes public transit. Apart from that important difference, the rest of the control variables are similar in both magnitude and sign to those observed in regression 3."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93832112",
   "metadata": {
    "cell_marker": "\"\"\""
   },
   "source": [
    "| Variable     | $OLS_1$ | $OLS_2$ | $OLS_3$    | $OLS_4$    |\n",
    "| ------------ | ------- | ------- | ---------- | ---------- |\n",
    "| Intercept    | -0.7976 | N/A     | N/A        | N/A        |\n",
    "| treatUberX   | 0.0382  | -0.0354 | 0.0075     | -0.0309    |\n",
    "| popestimate  | -0.9271 | 0.2789  | -9.98e-08  | -1.038e-07 |\n",
    "| employment   | 0.9909  | 0.2677  | 1.769e-07  | 1.737e-07  |\n",
    "| aveFareTotal | -0.1277 | -0.0996 | -0.0012    | -0.0012    |\n",
    "| VRHTotal     | 1.3417  | 0.3052  | 8.564e-07  | 8.576e-07  |\n",
    "| VOMSTotal    | -0.2376 | 0.2314  | 0.0005     | 0.0005     |\n",
    "| VRMTotal     | 0.0688  | 0.2664  | -2.596e-08 | -2.602e-08 |\n",
    "| gasPrice     | 0.2136  | -0.0407 | -0.0096    | -0.0101    |\n",
    "\n",
    "Overall, the OLS models indicate that we cannot definitively determine whether the effect of Uber on public transit was complementary or supplementary. The results are not robust across regressions, and therefore, we cannot establish causality. This inconsistency suggests that further analysis with more rigorous methods is needed to understand the true impact of Uber on public transit ridership. Below, we try to address these problems using LASSO and double LASSO regression techniques."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5d3685e",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        5.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        LASSO: log \\(Y_{it} = \\eta_i + \\delta_t + D_{it} \\beta_1 + D_{it} Pit \\beta_2 + W_{it}' \\gamma + \\epsilon_{it}\\).\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f3684183",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data.reset_index(inplace=True)\n",
    "\n",
    "# Define the dependent variable and independent variables\n",
    "Y = np.log(data[\"UPTTotal\"])\n",
    "D = data[\"treatUberX\"]\n",
    "W = data[\n",
    "    [\n",
    "        \"popestimate\",\n",
    "        \"employment\",\n",
    "        \"aveFareTotal\",\n",
    "        \"VRHTotal\",\n",
    "        \"VOMSTotal\",\n",
    "        \"VRMTotal\",\n",
    "        \"gasPrice\",\n",
    "    ]\n",
    "]\n",
    "PxD = data[\"PxD\"]\n",
    "FxD = data[\"FxD\"]\n",
    "\n",
    "# Scale the independent variables with log transformation\n",
    "W_scaled_df = np.log(W)\n",
    "\n",
    "# Create the design matrices\n",
    "X = pd.concat([D, W_scaled_df], axis=1)\n",
    "\n",
    "# Encode entity and time as dummy variables\n",
    "entity_dummies = pd.get_dummies(data[\"agency_city\"], drop_first=True)\n",
    "time_dummies = pd.get_dummies(data[\"dateSurvey\"], drop_first=True)\n",
    "\n",
    "# Create the design matrices\n",
    "X3 = np.column_stack((D, PxD, W_scaled_df, entity_dummies, time_dummies))\n",
    "Y = np.log(data[\"UPTTotal\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "22b532c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 1 Coefficients:\n",
      "        Feature  Coefficient\n",
      "0             D     0.000000\n",
      "1             P     0.000000\n",
      "2   popestimate     0.000000\n",
      "3    employment     0.009711\n",
      "4  aveFareTotal    -0.000000\n",
      "5      VRHTotal     1.170157\n",
      "6     VOMSTotal     0.000000\n",
      "7      VRMTotal     0.000000\n",
      "8      gasPrice     0.000000\n"
     ]
    }
   ],
   "source": [
    "# Fit Lasso regression models\n",
    "alpha1 = BCCH(X3, Y)\n",
    "lasso1 = Lasso(alpha=alpha1)  # You can adjust the alpha parameter as needed\n",
    "lasso1.fit(X3, Y)\n",
    "\n",
    "# Define the feature names\n",
    "feature_names = [\n",
    "    \"D\",\n",
    "    \"P\",\n",
    "    \"popestimate\",\n",
    "    \"employment\",\n",
    "    \"aveFareTotal\",\n",
    "    \"VRHTotal\",\n",
    "    \"VOMSTotal\",\n",
    "    \"VRMTotal\",\n",
    "    \"gasPrice\",\n",
    "]\n",
    "\n",
    "# Create DataFrame for Model 1\n",
    "coef1_df = pd.DataFrame({\"Feature\": feature_names, \"Coefficient\": lasso1.coef_[:9]})\n",
    "\n",
    "print(\"Model 1 Coefficients:\")\n",
    "print(coef1_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4bc6dd",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "We are running a LASSO regression to understand which of the covariates in the dataset are the most relevant predictors of public transit ridership. This model includes \\(P_{it}\\), a dummy variable that conditions on whether the population is larger than the median population in the dataset. The coefficients for Uber presence (D) and population (P) are both zero, suggesting they do not significantly affect ridership in this context. Employment and vehicle hours appear to be the only significant predictors, both contributing positively to ridership."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2450676",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        6.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        LASSO: log \\(Y_{it} = \\eta_i + \\delta_t + D_{it} \\beta_1 + D_{it} Fit \\beta_2 + W_{it}' \\gamma + \\epsilon_{it}\\).\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ef464b0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 2 Coefficients:\n",
      "        Feature  Coefficient\n",
      "0             D     0.000000\n",
      "1             F     0.000000\n",
      "2   popestimate     0.000000\n",
      "3    employment     0.009711\n",
      "4  aveFareTotal    -0.000000\n",
      "5      VRHTotal     1.170157\n",
      "6     VOMSTotal     0.000000\n",
      "7      VRMTotal     0.000000\n",
      "8      gasPrice     0.000000\n"
     ]
    }
   ],
   "source": [
    "# Create the design matrices\n",
    "X4 = np.column_stack((D, FxD, W_scaled_df, entity_dummies, time_dummies))\n",
    "\n",
    "# Fit Lasso regression models\n",
    "alpha2 = BCCH(X4, Y)\n",
    "lasso2 = Lasso(alpha=alpha1)  # You can adjust the alpha parameter as needed\n",
    "lasso2.fit(X4, Y)\n",
    "\n",
    "# Define the feature names\n",
    "feature_names = [\n",
    "    \"D\",\n",
    "    \"F\",\n",
    "    \"popestimate\",\n",
    "    \"employment\",\n",
    "    \"aveFareTotal\",\n",
    "    \"VRHTotal\",\n",
    "    \"VOMSTotal\",\n",
    "    \"VRMTotal\",\n",
    "    \"gasPrice\",\n",
    "]\n",
    "\n",
    "# Create DataFrame for Model 1\n",
    "coef2_df = pd.DataFrame({\"Feature\": feature_names, \"Coefficient\": lasso2.coef_[:9]})\n",
    "\n",
    "print(\"Model 2 Coefficients:\")\n",
    "print(coef2_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f061c0ed",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "This model includes $F_{it}, a dummy variable that conditions on whether the number of rides of the public transit agency is larger than the median number of rides among all public transit agencies before Uber was introduced. The coefficients for Uber presence (D) and the number of riders before Uber (F) are, again, zero, suggesting they do not significantly affect ridership in this context. As in the previous model, employment and vehicle hours appear to be the only significant predictors, both contributing positively to ridership. \n",
    "\n",
    "We observe that in both regressions, the non-zero coefficients are the same and identical in magnitude. Two factors contribute to this observation. Firstly, in both regressions, the penalty parameter calculated by the BCHH is identical. Secondly, despite having different treatments, we identify the same subset of variables as important. These factors also lead to the result that the treatments are disregarded, allowing us to conclude that they are not significant predictors of public transit ridership."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd4c8b61",
   "metadata": {
    "cell_marker": "\"\"\"",
    "lines_to_next_cell": 0
   },
   "source": [
    "| Variable     | $LASSO_5$ | $LASSO_6$ |\n",
    "| ------------ | --------- | --------- |\n",
    "| D            | 0         | 0         |\n",
    "| DP           | 0         | N/A       |\n",
    "| DF           | N/A       | 0         |\n",
    "| popestimate  | 0         | 0         |\n",
    "| employment   | 0.009711  | 0.009711  |\n",
    "| aveFareTotal | 0         | 0         |\n",
    "| VRHTotal     | 1.170157  | 1.170157  |\n",
    "| VOMSTotal    | 0         | 0         |\n",
    "| VRMTotal     | 0         | 0         |\n",
    "| gasPrice     | 0         | 0         |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ea8d47",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        7.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        Double-LASSO: log \\(Y_{it} = \\eta_i + \\delta_t + D_{it} \\beta_1 + D_{it} Pit \\beta_2 + \\tilde{W}_{it}' \\gamma + \\epsilon_{it}\\), where coefficients of interest are \\(\\beta_1\\) and \\(\\beta_2\\).\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "864f62e1",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated beta_1: 0.1249\n",
      "Estimated standard error: 0.0335\n",
      "Confidence interval: (0.0592, 0.1906)\n"
     ]
    }
   ],
   "source": [
    "Y = np.array(np.log(data[\"UPTTotal\"]), ndmin=1).T\n",
    "D = np.array(data[\"treatUberX\"], ndmin=1).T\n",
    "W = np.array(\n",
    "    np.log(\n",
    "        data[\n",
    "            [\n",
    "                \"popestimate\",\n",
    "                \"employment\",\n",
    "                \"aveFareTotal\",\n",
    "                \"VRHTotal\",\n",
    "                \"VOMSTotal\",\n",
    "                \"VRMTotal\",\n",
    "                \"gasPrice\",\n",
    "            ]\n",
    "        ]\n",
    "    )\n",
    ")\n",
    "P = np.array(data[\"P\"], ndmin=1).T\n",
    "W1 = np.column_stack((D * P, W))\n",
    "W2 = np.column_stack((D, W))\n",
    "DP = D * P\n",
    "\n",
    "# Convert dummy variables to numpy arrays\n",
    "entity_dummies_array = entity_dummies.to_numpy()\n",
    "time_dummies_array = time_dummies.to_numpy()\n",
    "\n",
    "# Concatenate the arrays\n",
    "W1_combined = np.concatenate([W1, entity_dummies_array, time_dummies_array], axis=1)\n",
    "W2_combined = np.concatenate([W2, entity_dummies_array, time_dummies_array], axis=1)\n",
    "\n",
    "\n",
    "# Run double LASSO regression to estimate alpha for D\n",
    "estimated_beta_1, estimated_std_error = double_lasso(Y, D, W1_combined)\n",
    "print(\"Estimated beta_1:\", estimated_beta_1.round(4))\n",
    "print(\"Estimated standard error:\", estimated_std_error.round(4))\n",
    "min = estimated_beta_1 - 1.96 * estimated_std_error\n",
    "max = estimated_beta_1 + 1.96 * estimated_std_error\n",
    "print(\"Confidence interval:\", (min.round(4), max.round(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "83b55bc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated beta_2: -0.0963\n",
      "Estimated standard error: 0.0375\n",
      "Confidence interval: (-0.1698, -0.0229)\n"
     ]
    }
   ],
   "source": [
    "# Run double LASSO regression to estimate alpha for D*P\n",
    "estimated_beta_2, estimated_std_error = double_lasso(Y, DP, W2_combined)\n",
    "print(\"Estimated beta_2:\", estimated_beta_2.round(4))\n",
    "print(\"Estimated standard error:\", estimated_std_error.round(4))\n",
    "min = estimated_beta_2 - 1.96 * estimated_std_error\n",
    "max = estimated_beta_2 + 1.96 * estimated_std_error\n",
    "print(\"Confidence interval:\", (min.round(4), max.round(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba95bff",
   "metadata": {
    "cell_marker": "\"\"\"",
    "lines_to_next_cell": 0
   },
   "source": [
    "In this model, $beta_1$ is estimated to be 0.1249 with a standard error of 0.0335. The confidence interval for $beta_1$ is (0.0592, 0.1906), indicating that the effect of Uber presence ($D_{it}$) on public transit ridership is positive and statistically significant. This suggests that Uber presence alone is associated with an increase in ridership, indicating that Uber complements public transit.\n",
    "\n",
    "Additionally, $beta_2$ is estimated to be -0.0963 with a standard error of 0.0375. The confidence interval for $beta_2$ is (-0.1698, -0.0229), indicating that the interaction effect of having a population above the median and Uber presence ($P_{it} \\times D_{it}$) on public transit ridership is negative and statistically significant. This suggests that in areas with a population above the median, Uber presence is associated with a 10% decrease in ridership compared to areas with below-median populations, indicating that Uber substitutes public transit in more populous areas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34b05a50",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        8.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        Double-LASSO: log \\(Y_{it} = \\eta_i + \\delta_t + D_{it} \\beta_1 + D_{it} Fit \\beta_2 + \\tilde{W}_{it}' \\gamma + \\epsilon_{it}\\), where coefficients of interest are \\(\\beta_1\\) and \\(\\beta_2\\).\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "95dd1147",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated beta_1: -0.2374\n",
      "Estimated standard error: 0.0188\n",
      "Confidence interval: (-0.2743, -0.2006)\n"
     ]
    }
   ],
   "source": [
    "F = np.array(data[\"F\"], ndmin=1).T\n",
    "W3 = np.column_stack((D * F, W))\n",
    "W3_combined = np.concatenate([W3, entity_dummies_array, time_dummies_array], axis=1)\n",
    "DF = D * F\n",
    "\n",
    "# Run double LASSO regression to estimate alpha for D, using F as an instrument\n",
    "estimated_beta_1, estimated_std_error = double_lasso(Y, D, W3_combined)\n",
    "print(\"Estimated beta_1:\", estimated_beta_1.round(4))\n",
    "print(\"Estimated standard error:\", estimated_std_error.round(4))\n",
    "min = estimated_beta_1 - 1.96 * estimated_std_error\n",
    "max = estimated_beta_1 + 1.96 * estimated_std_error\n",
    "print(\"Confidence interval:\", (min.round(4), max.round(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "76d32b50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated beta_2: 0.5964\n",
      "Estimated standard error: 0.0224\n",
      "Confidence interval: (0.5525, 0.6403)\n"
     ]
    }
   ],
   "source": [
    "# Run double LASSO regression to estimate alpha for D*F\n",
    "estimated_beta_2, estimated_std_error = double_lasso(Y, DF, W2_combined)\n",
    "print(\"Estimated beta_2:\", estimated_beta_2.round(4))\n",
    "print(\"Estimated standard error:\", estimated_std_error.round(4))\n",
    "min = estimated_beta_2 - 1.96 * estimated_std_error\n",
    "max = estimated_beta_2 + 1.96 * estimated_std_error\n",
    "print(\"Confidence interval:\", (min.round(4), max.round(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "371f1a24",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "In this model, $beta_1$ is estimated to be -0.2374 with a standard error of 0.0188. The confidence interval for $beta_1$ is (-0.2743, -0.2006), indicating that the effect of Uber presence ($D_{it}$) on public transit ridership is negative and statistically significant. This suggests that Uber presence alone is associated with a decrease in ridership, indicating that Uber substitutes public transit.\n",
    "\n",
    "Additionally, $beta_2$ is estimated to be 0.5964 with a standard error of 0.0224. The confidence interval for $beta_2$ is (0.5525, 0.6403), indicating that the interaction effect of having a number of riders above the median and Uber presence ($F_{it} \\times D_{it}$) on public transit ridership is positive and statistically significant. This suggests that in areas with a number of riders above the median, Uber presence is associated with a 60% increase in ridership compared to areas with below-median ridership, indicating that Uber complements public transit in areas with higher initial ridership."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68d4416",
   "metadata": {
    "cell_marker": "\"\"\"",
    "lines_to_next_cell": 0
   },
   "source": [
    "| Variable | $Double\\,Lasso_7$ | $Double\\,Lasso_8$ |\n",
    "| -------- | ----------------- | ----------------- |\n",
    "| D        | 0.1249            | -0.2374           |\n",
    "| D lower  | 0.0592            | -0.2743           |\n",
    "| D upper  | 0.1906            | -0.2006           |\n",
    "| PD       | -0.0963           | N/A               |\n",
    "| PD lower | -0.1698           | N/A               |\n",
    "| PD upper | -0.0229           | N/A               |\n",
    "| DF       | N/A               | 0.5964            |\n",
    "| DF lower | N/A               | 0.5525            |\n",
    "| DF upper | N/A               | 0.6403            |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2daa66aa",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        9.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        LASSO: log \\(Y_{it} = \\eta_i + \\delta_t + D_{it} \\beta_1 + D_{it} Pit \\beta_2 + \\tilde{W}_{it}' \\gamma + \\epsilon_{it}\\), where \\(\\tilde{W}_{it}\\) includes all interactions of order 5 of variables in the vector \\(W_{it}\\).\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "39ee1af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create polynomial features of 5th order\n",
    "poly = PolynomialFeatures(degree=5)\n",
    "W_poly = poly.fit_transform(W_scaled_df)\n",
    "W_p = pd.DataFrame(W_poly)\n",
    "W_p.drop(columns=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "450a7549",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 9 Coefficients:\n",
      "        Feature  Coefficient\n",
      "0             D         -0.0\n",
      "1            PD         -0.0\n",
      "2   popestimate         -0.0\n",
      "3    employment         -0.0\n",
      "4  aveFareTotal         -0.0\n",
      "5      VRHTotal          0.0\n",
      "6     VOMSTotal          0.0\n",
      "7      VRMTotal          0.0\n",
      "8      gasPrice          0.0\n"
     ]
    }
   ],
   "source": [
    "# Create the design matrices\n",
    "X9 = np.column_stack((D, PxD, W_p, entity_dummies, time_dummies))\n",
    "\n",
    "# Fit Lasso regression models\n",
    "alpha3 = BCCH(X9, Y)\n",
    "lasso9 = Lasso(alpha=alpha3)  # You can adjust the alpha parameter as needed\n",
    "lasso9.fit(X9, Y)\n",
    "\n",
    "# Define the feature names\n",
    "feature_names = [\n",
    "    \"D\",\n",
    "    \"PD\",\n",
    "    \"popestimate\",\n",
    "    \"employment\",\n",
    "    \"aveFareTotal\",\n",
    "    \"VRHTotal\",\n",
    "    \"VOMSTotal\",\n",
    "    \"VRMTotal\",\n",
    "    \"gasPrice\",\n",
    "]\n",
    "\n",
    "# Create DataFrame for Model 9\n",
    "coef9_df = pd.DataFrame({\"Feature\": feature_names, \"Coefficient\": lasso9.coef_[:9]})\n",
    "\n",
    "print(\"Model 9 Coefficients:\")\n",
    "print(coef9_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6329db",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "87298ce7",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        10.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        LASSO: log \\(Y_{it} = \\eta_i + \\delta_t + D_{it} \\beta_1 + D_{it} Fit \\beta_2 + \\tilde{W}_{it}' \\gamma + \\epsilon_{it}\\), where \\(\\tilde{W}_{it}\\) includes all interactions of order 5 of variables in the vector \\(W_{it}\\).\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1b039d5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 10 Coefficients:\n",
      "        Feature  Coefficient\n",
      "0             D         -0.0\n",
      "1            FD          0.0\n",
      "2   popestimate         -0.0\n",
      "3    employment         -0.0\n",
      "4  aveFareTotal         -0.0\n",
      "5      VRHTotal          0.0\n",
      "6     VOMSTotal          0.0\n",
      "7      VRMTotal          0.0\n",
      "8      gasPrice          0.0\n"
     ]
    }
   ],
   "source": [
    "# Create the design matrices\n",
    "X10 = np.column_stack((D, FxD, W_p, entity_dummies, time_dummies))\n",
    "\n",
    "# Fit Lasso regression models\n",
    "alpha4 = BCCH(X10, Y)\n",
    "lasso10 = Lasso(alpha=alpha4)  # You can adjust the alpha parameter as needed\n",
    "lasso10.fit(X10, Y)\n",
    "\n",
    "# Define the feature names\n",
    "feature_names = [\n",
    "    \"D\",\n",
    "    \"FD\",\n",
    "    \"popestimate\",\n",
    "    \"employment\",\n",
    "    \"aveFareTotal\",\n",
    "    \"VRHTotal\",\n",
    "    \"VOMSTotal\",\n",
    "    \"VRMTotal\",\n",
    "    \"gasPrice\",\n",
    "]\n",
    "\n",
    "# Create DataFrame for Model 10\n",
    "coef10_df = pd.DataFrame({\"Feature\": feature_names, \"Coefficient\": lasso10.coef_[:9]})\n",
    "\n",
    "print(\"Model 10 Coefficients:\")\n",
    "print(coef10_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b91fd5c",
   "metadata": {
    "lines_to_next_cell": 0
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   "source": [
    "In both regressions, LASSO penalized all variables, shrinking their coefficients to zero. This outcome suggests that none of the variables, nor their interactions up to the fifth order, are strong predictors of public transit ridership when considering Uber presence and the interaction terms ($P_{it} \\times D_{it}$ and $F_{it} \\times D_{it}$). The complexity added by considering high-order interactions among the control variables does not enhance the model's ability to predict public transit ridership.\n",
    "\n",
    "Given that neither Uber presence nor the high-order interactions of the control variables contribute significantly to the model, we can infer that the direct impact of Uber on public transit ridership, as captured by these models, is minimal. This conclusion aligns with the results from previously estimated, simpler models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0737bc59",
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    "| Variable     | $LASSO_5$ | $LASSO_6$ | $LASSO_9$ | $LASSO_{10}$ |\n",
    "| ------------ | --------- | --------- | --------- | ------------ |\n",
    "| D            | 0         | 0         | 0         | 0            |\n",
    "| DP           | 0         | N/A       | 0         | 0            |\n",
    "| DF           | N/A       | 0         | 0         | 0            |\n",
    "| popestimate  | 0         | 0         | 0         | 0            |\n",
    "| employment   | 0.009711  | 0.009711  | 0         | 0            |\n",
    "| aveFareTotal | 0         | 0         | 0         | 0            |\n",
    "| VRHTotal     | 1.170157  | 1.170157  | 0         | 0            |\n",
    "| VOMSTotal    | 0         | 0         | 0         | 0            |\n",
    "| VRMTotal     | 0         | 0         | 0         | 0            |\n",
    "| gasPrice     | 0         | 0         | 0         | 0            |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c624c9fb",
   "metadata": {
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   "source": [
    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        11.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        Double-LASSO: log \\(Y_{it} = \\eta_i + \\delta_t + D_{it} \\beta_1 + D_{it} Pit \\beta_2 + \\tilde{W}_{it}' \\gamma + \\epsilon_{it}\\), where coefficients of interest are \\(\\beta_1\\) and \\(\\beta_2\\) and \\(\\tilde{W}_{it}\\) includes all interactions of order 5 of variables in the vector \\(W_{it}\\).\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "95d56d47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated beta_1: 0.0254\n",
      "Estimated standard error: 0.0137\n",
      "Confidence interval: (-0.0015, 0.0523)\n"
     ]
    }
   ],
   "source": [
    "Y = np.array(np.log(data[\"UPTTotal\"]), ndmin=1).T\n",
    "D = np.array(data[\"treatUberX\"], ndmin=1).T\n",
    "W = np.array(\n",
    "    np.log(\n",
    "        data[\n",
    "            [\n",
    "                \"popestimate\",\n",
    "                \"employment\",\n",
    "                \"aveFareTotal\",\n",
    "                \"VRHTotal\",\n",
    "                \"VOMSTotal\",\n",
    "                \"VRMTotal\",\n",
    "                \"gasPrice\",\n",
    "            ]\n",
    "        ]\n",
    "    )\n",
    ")\n",
    "P = np.array(data[\"P\"], ndmin=1).T\n",
    "W1 = np.column_stack((D * P, W_p))\n",
    "W2 = np.column_stack((D, W_p))\n",
    "DP = D * P\n",
    "\n",
    "# Convert dummy variables to numpy arrays\n",
    "entity_dummies_array = entity_dummies.to_numpy()\n",
    "time_dummies_array = time_dummies.to_numpy()\n",
    "\n",
    "# Concatenate the arrays\n",
    "W1_combined = np.concatenate([W1, entity_dummies_array, time_dummies_array], axis=1)\n",
    "W2_combined = np.concatenate([W2, entity_dummies_array, time_dummies_array], axis=1)\n",
    "\n",
    "\n",
    "# Run double LASSO regression to estimate alpha for D\n",
    "estimated_beta_1, estimated_std_error = double_lasso(Y, D, W1_combined)\n",
    "print(\"Estimated beta_1:\", estimated_beta_1.round(4))\n",
    "print(\"Estimated standard error:\", estimated_std_error.round(4))\n",
    "min = estimated_beta_1 - 1.96 * estimated_std_error\n",
    "max = estimated_beta_1 + 1.96 * estimated_std_error\n",
    "print(\"Confidence interval:\", (min.round(4), max.round(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9cec95ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated beta_2: -0.0092\n",
      "Estimated standard error: 0.0142\n",
      "Confidence interval: (-0.0371, 0.0187)\n"
     ]
    }
   ],
   "source": [
    "# Run double LASSO regression to estimate alpha for D*P\n",
    "estimated_beta_2, estimated_std_error = double_lasso(Y, DP, W2_combined)\n",
    "print(\"Estimated beta_2:\", estimated_beta_2.round(4))\n",
    "print(\"Estimated standard error:\", estimated_std_error.round(4))\n",
    "min = estimated_beta_2 - 1.96 * estimated_std_error\n",
    "max = estimated_beta_2 + 1.96 * estimated_std_error\n",
    "print(\"Confidence interval:\", (min.round(4), max.round(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8516488",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "In this model, $\\beta_1$ is estimated to be 0.0254 with a standard error of 0.0137. The confidence interval for $\\beta_1$ is (-0.0015, 0.0523), indicating that the effect of Uber presence ($D_{it}$) on public transit ridership is not statistically significant. This suggests that Uber presence alone does not have a significant impact on ridership.\n",
    "\n",
    "Additionally, $\\beta_2$ is estimated to be -0.0092 with a standard error of 0.0142. The confidence interval for $\\beta_2$ is (-0.0371, 0.0187), indicating that the interaction effect of having a population above the median and Uber presence ($P_{it} \\times D_{it}$) on public transit ridership is also not statistically significant. This suggests that in areas with a population above the median, Uber presence does not significantly affect ridership."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ee64a2",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "<div style=\"border: 1px solid black; border-radius: 5px; overflow: hidden;\">\n",
    "    <div style=\"background-color: black; color: white; padding: 5px; text-align: left;\">\n",
    "        12.)\n",
    "    </div>\n",
    "    <div style=\"padding: 10px;\">\n",
    "        Double-LASSO: log \\(Y_{it} = \\eta_i + \\delta_t + D_{it} \\beta_1 + D_{it} Fit \\beta_2 + \\tilde{W}_{it}' \\gamma + \\epsilon_{it}\\), where coefficients of interest are \\(\\beta_1\\) and \\(\\beta_2\\) and \\(\\tilde{W}_{it}\\) includes all interactions of order 5 of variables in the vector \\(W_{it}\\).\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c614b499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated beta_1: 0.0254\n",
      "Estimated standard error: 0.0137\n",
      "Confidence interval: (-0.0015, 0.0523)\n"
     ]
    }
   ],
   "source": [
    "# Create Fit\n",
    "F = np.array(data[\"F\"], ndmin=1).T\n",
    "W3 = np.column_stack((D * F, W_p))\n",
    "DF = D * F\n",
    "\n",
    "# Convert dummy variables to numpy arrays\n",
    "entity_dummies_array = entity_dummies.to_numpy()\n",
    "time_dummies_array = time_dummies.to_numpy()\n",
    "\n",
    "# Concatenate the arrays\n",
    "W3_combined = np.concatenate([W3, entity_dummies_array, time_dummies_array], axis=1)\n",
    "\n",
    "# Run double LASSO regression to estimate alpha for D\n",
    "estimated_beta_1, estimated_std_error = double_lasso(Y, D, W3_combined)\n",
    "print(\"Estimated beta_1:\", estimated_beta_1.round(4))\n",
    "print(\"Estimated standard error:\", estimated_std_error.round(4))\n",
    "min = estimated_beta_1 - 1.96 * estimated_std_error\n",
    "max = estimated_beta_1 + 1.96 * estimated_std_error\n",
    "print(\"Confidence interval:\", (min.round(4), max.round(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fd853f1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated beta_2: 0.5033\n",
      "Estimated standard error: 0.0157\n",
      "Confidence interval: (0.4725, 0.534)\n"
     ]
    }
   ],
   "source": [
    "# Run double LASSO regression to estimate alpha for D*F\n",
    "estimated_beta_2, estimated_std_error = double_lasso(Y, DF, W2_combined)\n",
    "print(\"Estimated beta_2:\", estimated_beta_2.round(4))\n",
    "print(\"Estimated standard error:\", estimated_std_error.round(4))\n",
    "min = estimated_beta_2 - 1.96 * estimated_std_error\n",
    "max = estimated_beta_2 + 1.96 * estimated_std_error\n",
    "print(\"Confidence interval:\", (min.round(4), max.round(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "750f80e2",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "In this model, $\\beta_1$ is also estimated to be 0.0254 with a standard error of 0.0137. The confidence interval for $\\beta_1$ is (-0.0015, 0.0523), indicating that the effect of Uber presence ($D_{it}$) on public transit ridership is not statistically significant. This suggests that Uber presence alone does not have a significant impact on ridership.\n",
    "\n",
    "Additionally, $\\beta_2$ is estimated to be 0.5033 with a standard error of 0.0157. The confidence interval for $\\beta_2$ is (0.4725, 0.5340), indicating that the interaction effect of having a number of riders above the median and Uber presence ($F_{it} \\times D_{it}$) on public transit ridership is positive and statistically significant. This suggests that in areas with a higher number of initial riders, Uber presence is associated with a 50% increase in ridership compared to areas with below-median ridership, indicating that Uber complements public transit in areas with higher initial ridership. This result is similar to the one observed in Double-LASSO Regression 2, where we estimated this effect to be 60%.\n",
    "\n",
    "Finally, we observe that the coefficients for $\\beta_1$ from regressions 11 and 12 are identical. This phenomenon can be attributed to the inclusion of the polynomials which results to an exponential increase in the number of features included in the model. This approach adresses the issues of non linearities in the model and provides a robust estimation for the effect of Uber, which remains unaffected by change in the interaction effect fitted.s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "812d659e",
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   "source": [
    "| Variable | $Double\\,Lasso_7$ | $Double\\,Lasso_8$ | $Double\\,Lasso_{11}$ | $Double\\,Lasso_{12}$ |\n",
    "| -------- | ----------------- | ----------------- | -------------------- | -------------------- |\n",
    "| D        | 0.1249            | -0.2374           | 0.0254               | 0.0254               |\n",
    "| D lower  | 0.0592            | -0.2743           | -0.0015              | -0.0015              |\n",
    "| D upper  | 0.1906            | -0.2006           | 0.0523               | 0.0523               |\n",
    "| PD       | -0.0963           | N/A               | -0.0092              | N/A                  |\n",
    "| PD lower | -0.1698           | N/A               | -0.0371              | N/A                  |\n",
    "| PD upper | -0.0229           | N/A               | 0.0187               | N/A                  |\n",
    "| DF       | N/A               | 0.5964            | N/A                  | 0.5033               |\n",
    "| DF lower | N/A               | 0.5525            | N/A                  | 0.4725               |\n",
    "| DF upper | N/A               | 0.6403            | N/A                  | 0.534                |"
   ]
  },
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   "id": "5a15e0d2",
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   "source": [
    "## Conclusion\n",
    "\n",
    "The purpose of this study is to examine the causal effects of Uber presence on public transit ridership. The goal is to determine whether Uber can substitute for public transit, for example, if riders choose Uber instead of public transit, or whether it can complement public transit, for example, if riders take Uber from home to a public transit stop, thereby making public transit more attractive than driving a car.\n",
    "\n",
    "We use three different approaches for this problem: OLS, Lasso, and Double Lasso, all of which include fixed effects for public transport agencies and time. Along with the Uber treatment, in each approach we also test interactions for population and pre-Uber public rides, as well as different control variables.\n",
    "\n",
    "As mentioned in the relevant section, the OLS models indicate that we cannot definitively determine whether the effect of Uber on public transit was complementary or supplementary. In terms of causality, none of the coefficients can be directly interpreted in a causal form because of the endogeneity problem in the error term. This means there is no orthogonality between the error term and the treatment.\n",
    "\n",
    "The purpose of using Lasso combined with the BCCH λ selection method is to leverage the oracle property of this method. This means we select the most important covariates explaining changes in the dependent variable. We observe that in all the Lasso regressions (5, 6, 9, 10) the Uber treatment is excluded, along with the interactions of this covariate we have included. This suggests an undefinable relationship between the treatment and public transit ridership. However, we can't make any causal conclusions due to the same orthogonality issue with the error terms, which is now exacerbated by the inclusion of Lasso-generated bias.\n",
    "\n",
    "To address the issue of orthogonality with the error term, we apply Double Lasso. In the first setting $(Double\\,Lasso_7, Double\\,Lasso_8)$, we include only linear covariates, observing highly variable estimation of the Uber treatment. This variability is explained by the misspecification problem in the two Double Lasso regression stages. This happens because the quality of the Double Lasso also depends on the strength of the orthogonality between the error terms of the dependent and independent variables (ε) and the treatment effect and controls (ν) in the two stages of the method. This issue is addressed by including polynomial features to the 5th power of the control variables $(Double\\,Lasso_{11}, Double\\,Lasso_{12})$, recalling the function approximation property of Taylor Series expansions. By using this setting, we conclude that the Uber treatment is significantly positive and robust to the inclusion of interactions between the treatment and other covariates."
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