{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Sovereign Debt Crises\n",
    "\n",
    "> **Author**\n",
    "- Raphaël Grach (#49059165)\n",
    "\n",
    "**Objective**\n",
    "\n",
    "Use an artificial neural network (ANN) analysis to build a model trying to predict sovereign debt crises using joint data from the International Monetary fund (IMF) and the World Bank. The methodology will be following closely [Fioramanti (2008)](#nn-fioramanti2008) and build on the work of [Manasse, Roubini and Schimmelpfennig (2003)](#nn-MRS2003) (MRS 2003).\n",
    "\n",
    "### Outline\n",
    "\n",
    "- [Background: probit and logit](#Background:-Probit-and-logit)\n",
    "- [Early Warning Systems (EWS)](#Early-Warning-Systems-%28EWS%29)\n",
    "- [Data](#Data)\n",
    "    - [World Bank data](#World-Bank-data)\n",
    "    - [Exploring the data](#Exploring-the-data)\n",
    "- [The Model](#The-Model)\n",
    "    - [In-sample estimation](#In-sample-estimation-model-%28IS%29-%28Train:-1980-2004-/-Test:-2002-2004%29)\n",
    "    - [Out-of-sample estimation 1](#Out-of-sample-estimation-model-1-%28OS1%29-%28Train:-1981-2001-/-Test:-2002-2004%29)\n",
    "    - [Out-of-sample estimation 2](#Out-of-sample-estimation-model-2-%28OS2%29-%28Train:-1991-2001-/-Test:-2002-2004%29)\n",
    "- [Performance](#Performance)\n",
    "    - [Mean squared errors (MSE)](#Mean-squared-errors-%28MSE%29)\n",
    "    - [Hit rate](#Hit-rate)\n",
    "- [Conclusion](#Conclusion)\n",
    "- [References](#References)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background: probit and logit\n",
    "\n",
    "The previous episodes of sovereign debt crises has led international organisations to develop mechanisms aiming at predicting their occurence. The traditional parametric identification strategy used in the literature is the random effect probit (REP) estimator. Probit and logit estimations method (though more information can be retrieved [here](http://www.columbia.edu/~so33/SusDev/Lecture_9.pdf)) are basiacally mostly trying to estimate effect of independent variables on the *probability* of a certain event happening. They usually do that by transforming a discontinuous dependent variable $Y$ (that is in general a dummy) into a smoother function using transformations.\n",
    "\n",
    "The process is usually conducted by first tranform the original $Y$ (that takes values $\\{0,1\\}$) into a **probability** (that is distributed over the interval $[0,1]$) and then into a desired function.\n",
    "\n",
    "- The **probit** method usually uses the odd-ratio: $\\frac{p}{(1-p)}$ where p is the probability of event $Y$ happening. It is defined on $[0,+\\infty)$\n",
    "- The **logit** method uses the log-transform of the odd-ratio: $\\ln(\\frac{p}{(1-p)})$ that is defined on $(-\\infty,+\\infty)$\n",
    "\n",
    "The \"random effects\" hypothesis is an alternative to the \"fixed-effects\" hypothesis that is usually used in econometrics. The random effects assumption stipulates that the unobserved heterogeneity is continuous and **uncorrelated with the independent variables**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Early Warning Systems (EWS)\n",
    "\n",
    "In the past two decades, researchers have tried to develop models able to recognize signals annoucing a potential crisis, the so-called Early Warning Systems (EWS). These models sought to predict the likelihood of a financial crisis using both parametric and non-paramteric estimation methods. A more extensive discussion of methodologies is proposed in [Ciarlone and Trebeschi (2005)](#nn-ciarlone2005). \n",
    "\n",
    "A number of emerging countries have known what is referred as a \"sovereign debt crisis\" indicating that the level of sovereign debt reaches levels that the country alone cannot sustain. Facing such crises required the help of international organizations such as the IMF to adjust their situation. The objective of this project will be to identify early signs of a sovereign debt crisis and use them as a predictor.\n",
    "\n",
    "This study will be using an artificial neural network (ANN) and compare its performance with the REP estimators.\n",
    "\n",
    "**Disclaimer**: Since some of the referenced papers had access to greater resources that this current research, this study has room for improvement. Fioramanti's ANN *beats* the REP in terms of predictive power, it is not given that this one will."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "The data is composed of different macro-economic indicators for a variety of developing countries as outlined in [Fioramanti (2008)](#nn-fioramanti2008). The definition of a sovereign debt crisis will be the one in [MRS (2003)](#nn-MRS2003):\n",
    "\n",
    "> \"A country is defined to be in a debt crisis if it is classified as being in default by Standard & Poor’s or if it receives a large nonconcessional IMF loan defined as access in excess of 100% of quota.\" (p.8)\n",
    "\n",
    "The panel data is collected from the World Bank and the IMF databases. Let's first have a look at the episodes of sovereign debt crisis for each of these countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy and paste this to import all the packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mplc\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib import cm\n",
    "%matplotlib inline\n",
    "import math\n",
    "#!pip install qeds\n",
    "import qeds # QuantEcon collab\n",
    "qeds.themes.mpl_style();\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import preprocessing, pipeline, neural_network, metrics # Neural network package\n",
    "import statsmodels.formula.api as smf  # for doing statistical regression\n",
    "import statsmodels.api as sm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "crisis_episodes = pd.read_excel(\"Crisis_episodes.xlsx\",index_col=\"Countries\")\n",
    "countries_0 = ['China', 'Colombia', 'Cyprus', 'Egypt', 'Israel', 'Morocco', 'Oman'] # Countries with 0 years in crisis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = cm.coolwarm(crisis_episodes.drop(countries_0\n",
    "                                         ).sort_values(by=\"Number of years in crisis\")[\"Number of years in crisis\"]/22,\n",
    "                    bytes = False)\n",
    "\n",
    "fig = crisis_episodes.drop(countries_0\n",
    "                    ).sort_values(by=\"Number of years in crisis\"\n",
    "                                 ).plot(y = \"Number of years in crisis\", \n",
    "                                        kind = \"barh\", \n",
    "                                        figsize = (10,15),\n",
    "                                        color=colors,\n",
    "                                       legend = False)\n",
    "fig.set_title('Number of years in debt crisis by country (1980-2004)')\n",
    "fig.set_xlabel('Number of years in debt crisis')\n",
    "fig.set_facecolor('w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With some countries like Argentina spending  **22 years** in a debt crisis, a model that could help better anticipate those episodes can definitely benefit both the country and international organisations.\n",
    "\n",
    "### World Bank data\n",
    "\n",
    "[Table of indicators of the World Bank.](http://wdi.worldbank.org/tables) A handy feature of the World Bank data is that it uses different sources, notably the IMF to compute its own indicators. That is, data originally coming from the IMF can be accessed via the World Bank API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: world_bank_data in /anaconda3/lib/python3.7/site-packages (0.1.3)\n",
      "Requirement already satisfied: cachetools in /anaconda3/lib/python3.7/site-packages (from world_bank_data) (4.1.0)\n",
      "Requirement already satisfied: pandas in /anaconda3/lib/python3.7/site-packages (from world_bank_data) (0.23.4)\n",
      "Requirement already satisfied: requests in /anaconda3/lib/python3.7/site-packages (from world_bank_data) (2.21.0)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /anaconda3/lib/python3.7/site-packages (from pandas->world_bank_data) (2.7.5)\n",
      "Requirement already satisfied: pytz>=2011k in /anaconda3/lib/python3.7/site-packages (from pandas->world_bank_data) (2018.7)\n",
      "Requirement already satisfied: numpy>=1.9.0 in /anaconda3/lib/python3.7/site-packages (from pandas->world_bank_data) (1.15.4)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /anaconda3/lib/python3.7/site-packages (from requests->world_bank_data) (2.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /anaconda3/lib/python3.7/site-packages (from requests->world_bank_data) (2018.11.29)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /anaconda3/lib/python3.7/site-packages (from requests->world_bank_data) (1.24.1)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /anaconda3/lib/python3.7/site-packages (from requests->world_bank_data) (3.0.4)\n",
      "Requirement already satisfied: six>=1.5 in /anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.5.0->pandas->world_bank_data) (1.12.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install world_bank_data\n",
    "import world_bank_data as wb\n",
    "\n",
    "countries = [\"Algeria\", \"Argentina\", \"Bolivia\",\n",
    "\"Brazil\", \"Chile\", \"China\", \"Colombia\", \"Costa Rica\", \"Cyprus\", \"Czech Republic\", \"Dominican Republic\", \"Ecuador\",\n",
    "\"Egypt, Arab Rep.\", \"El Salvador\", \"Estonia\", \"Guatemala\", \"Hungary\", \"India\", \"Indonesia\", \"Israel\", \"Jamaica\",\n",
    "\"Jordan\", \"Kazakhstan\", \"Latvia\", \"Lithuania\", \"Malaysia\", \"Mexico\", \"Morocco\", \"Oman\", \"Pakistan\", \"Panama\",\n",
    "\"Paraguay\", \"Peru\", \"Philippines\",  \"Poland\", \"Romania\", \"Russian Federation\", \"Slovak Republic\", \"South Africa\",\n",
    "\"Thailand\", \"Trinidad and Tobago\", \"Tunisia\", \"Turkey\", \"Ukraine\", \"Uruguay\", \"Venezuela, RB\"]\n",
    "\n",
    "# 3-letter ISO code of each country\n",
    "iso = [\"DZA\",\n",
    "\"ARG\", \"BOL\", \"BRA\", \"CHL\", \"CHN\", \"COL\", \"CRI\", \"CYP\", \"CZE\", \"DOM\", \"ECU\", \"EGY\", \"SLV\", \"EST\", \"GTM\",\n",
    "\"HUN\", \"IND\", \"IDN\", \"ISR\", \"JAM\", \"JOR\", \"KAZ\", \"LVA\", \"LTU\", \"MYS\", \"MEX\", \"MAR\", \"OMN\", \"PAK\", \"PAN\",\n",
    "\"PRY\", \"PER\", \"PHL\", \"POL\", \"ROU\", \"RUS\", \"SVK\", \"ZAF\", \"THA\", \"TTO\", \"TUN\", \"TUR\", \"UKR\", \"URY\", \"VEN\"]\n",
    "\n",
    "dates = list(range(1980,2005)) # Dates range from 1980 to 2004\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wb.get_topics() # Displays the complete list of World Bank data topics (For consultative use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wb.get_sources() # Displays the full list of sources (For consultative use)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Here is an extensive list of World Bank indicators](https://data.worldbank.org/indicator?tab=all)\n",
    "\n",
    "The indicator can be found in the link of the resource when accessed. For example:\n",
    "\n",
    "Exports of goods and services: `https://data.worldbank.org/indicator/NE.EXP.GNFS.CD?view=chart`\n",
    "\n",
    "The indicator code is represented by the series of **UPPERCASE CHARACTERS**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wb.get_indicators() # Displays the full list of indicators (For consultative use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of indicators we'll use\n",
    "ind_use = [\n",
    "    #GNI Growth rate\n",
    "    \"NY.GNP.MKTP.KD.ZG\", \n",
    "    #GDP Growth Rate\n",
    "    \"NY.GDP.MKTP.KD.ZG\",\n",
    "    # Broad money growth (% annual)\n",
    "    \"FM.LBL.BMNY.ZG\",\n",
    "    # Inflation (consumer prices)\n",
    "    \"FP.CPI.TOTL.ZG\",\n",
    "    # Real interest rate\n",
    "    \"FR.INR.RINR\",\n",
    "    # Deposit interest rate\n",
    "    \"FR.INR.DPST\",\n",
    "    # Taxes on international trade (% of revenue) (proxy for openness, higher = less open)\n",
    "    \"GC.TAX.INTT.RV.ZS\",\n",
    "    # Net trade in goods and services (BoP, in USD) (Proxy for trade balance)\n",
    "    \"BN.GSR.GNFS.CD\",\n",
    "    # Present value of external debt (% of GNI and % of exports)\n",
    "    \"DT.DOD.PVLX.GN.ZS\", \"DT.DOD.PVLX.EX.ZS\",\n",
    "    # Total external debt (Current USD and % of GNI)\n",
    "    \"DT.DOD.DECT.CD\", \"DT.DOD.DECT.GN.ZS\",\n",
    "    # Short-term debt as % of total reserves\n",
    "    \"DT.DOD.DSTC.IR.ZS\",\n",
    "    # Short-term external debt stocks (Current USD)\n",
    "    \"DT.DOD.DSTC.CD\",\n",
    "    # Exports of goods and services (% of GDP and current USD)\n",
    "    \"NE.EXP.GNFS.ZS\", \"NE.EXP.GNFS.CD\",\n",
    "    # Total reserves (Current USD including gold) (and as % total external debt)\n",
    "    \"FI.RES.TOTL.CD\", \"FI.RES.TOTL.DT.ZS\",\n",
    "    # Total debt service (% of exports)\n",
    "    \"DT.TDS.DECT.EX.ZS\",\n",
    "    # Public debt service (% of GNI and % of exports)\n",
    "    \"DT.TDS.DPPG.GN.ZS\", \"DT.TDS.DPPG.XP.ZS\",\n",
    "    # Current account (% of GDP and in Current USD)\n",
    "    \"BN.CAB.XOKA.GD.ZS\", \"BN.CAB.XOKA.CD\",\n",
    "    # Portfolio equity, net inflows (Current USD)\n",
    "    \"BX.PEF.TOTL.CD.WD\",\n",
    "    # Foreign Direct Investment (net inflow in current USD and % of GDP)\n",
    "    \"BX.KLT.DINV.CD.WD\", \"BX.KLT.DINV.WD.GD.ZS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_countries = pd.read_excel(\"master_countries.xls\")\n",
    "master_countries[\"year\"] = master_countries[\"year\"].astype(str) # Set the correct type for \"year\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(master_countries.loc[0][0]) # should be \"str\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Query data through pandas_datareader (This might take some time)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas_datareader\n",
    "from pandas_datareader import wb\n",
    "wb_data = pandas_datareader.wb.WorldBankReader(symbols = ind_use, countries = iso, start = 1980, end = 2004).read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the lacking variables\n",
    "\n",
    "# Total external debt as % of reserves\n",
    "wb_data[\"ted_res\"] = (wb_data[\"DT.DOD.DECT.CD\"] / wb_data[\"FI.RES.TOTL.CD\"])*100\n",
    "# Short-term external debt as % of reserves\n",
    "wb_data[\"sted_res\"] = (wb_data[\"DT.DOD.DSTC.CD\"] / wb_data[\"FI.RES.TOTL.CD\"])*100\n",
    "# Short-term external debt as % of exports\n",
    "wb_data[\"sted_xgs\"] = (wb_data[\"DT.DOD.DSTC.CD\"] / wb_data[\"NE.EXP.GNFS.CD\"])*100\n",
    "# Short-term external debt as % of total external debt\n",
    "wb_data[\"sted_ted\"] = (wb_data[\"DT.DOD.DSTC.CD\"] / wb_data[\"DT.DOD.DECT.CD\"])*100\n",
    "# Current account as % of reserves\n",
    "wb_data[\"cac_res\"] = (wb_data[\"BN.CAB.XOKA.CD\"] / wb_data[\"FI.RES.TOTL.CD\"])*100\n",
    "# Current account as % of portfolio equity, net inflows\n",
    "wb_data[\"cac_pef\"] = (wb_data[\"BN.CAB.XOKA.CD\"] / wb_data[\"BX.PEF.TOTL.CD.WD\"])*100 # This produced 'inf' values\n",
    "# Current account as % of FDI\n",
    "wb_data[\"cac_fdi\"] = (wb_data[\"BN.CAB.XOKA.CD\"] / wb_data[\"BX.KLT.DINV.CD.WD\"])*100\n",
    "# Total external debt as % of exports \n",
    "wb_data[\"ted_xgs\"] = (wb_data[\"DT.DOD.DECT.CD\"] / wb_data[\"NE.EXP.GNFS.CD\"])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>NY.GNP.MKTP.KD.ZG</th>\n",
       "      <th>NY.GDP.MKTP.KD.ZG</th>\n",
       "      <th>FM.LBL.BMNY.ZG</th>\n",
       "      <th>FP.CPI.TOTL.ZG</th>\n",
       "      <th>FR.INR.RINR</th>\n",
       "      <th>FR.INR.DPST</th>\n",
       "      <th>GC.TAX.INTT.RV.ZS</th>\n",
       "      <th>BN.GSR.GNFS.CD</th>\n",
       "      <th>DT.DOD.PVLX.GN.ZS</th>\n",
       "      <th>DT.DOD.PVLX.EX.ZS</th>\n",
       "      <th>...</th>\n",
       "      <th>BX.KLT.DINV.WD.GD.ZS</th>\n",
       "      <th>ted_res</th>\n",
       "      <th>sted_res</th>\n",
       "      <th>sted_xgs</th>\n",
       "      <th>sted_ted</th>\n",
       "      <th>cac_res</th>\n",
       "      <th>cac_pef</th>\n",
       "      <th>cac_fdi</th>\n",
       "      <th>ted_xgs</th>\n",
       "      <th>crisis</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Argentina</th>\n",
       "      <th>2004</th>\n",
       "      <td>1.069367</td>\n",
       "      <td>9.029573</td>\n",
       "      <td>21.427310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.789158</td>\n",
       "      <td>2.614968</td>\n",
       "      <td>15.799545</td>\n",
       "      <td>1.193361e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.505018</td>\n",
       "      <td>854.542849</td>\n",
       "      <td>135.042097</td>\n",
       "      <td>67.610786</td>\n",
       "      <td>15.802847</td>\n",
       "      <td>16.337013</td>\n",
       "      <td>-3728.136970</td>\n",
       "      <td>77.867050</td>\n",
       "      <td>427.839281</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>10.201217</td>\n",
       "      <td>8.837041</td>\n",
       "      <td>29.634853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.828852</td>\n",
       "      <td>10.160701</td>\n",
       "      <td>16.470869</td>\n",
       "      <td>1.561150e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.294811</td>\n",
       "      <td>1154.477701</td>\n",
       "      <td>158.125464</td>\n",
       "      <td>67.663914</td>\n",
       "      <td>13.696710</td>\n",
       "      <td>57.496409</td>\n",
       "      <td>12455.899005</td>\n",
       "      <td>492.728858</td>\n",
       "      <td>494.015815</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>-13.204427</td>\n",
       "      <td>-10.894485</td>\n",
       "      <td>19.708854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.179804</td>\n",
       "      <td>39.248446</td>\n",
       "      <td>13.638532</td>\n",
       "      <td>1.571728e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.198958</td>\n",
       "      <td>1413.593093</td>\n",
       "      <td>141.385413</td>\n",
       "      <td>53.484395</td>\n",
       "      <td>10.001847</td>\n",
       "      <td>83.551802</td>\n",
       "      <td>-7565.248533</td>\n",
       "      <td>407.956127</td>\n",
       "      <td>534.745201</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>-4.613779</td>\n",
       "      <td>-4.408840</td>\n",
       "      <td>-19.436218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.120285</td>\n",
       "      <td>16.163597</td>\n",
       "      <td>4.284476</td>\n",
       "      <td>3.521811e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.806164</td>\n",
       "      <td>1048.740175</td>\n",
       "      <td>137.431550</td>\n",
       "      <td>64.295618</td>\n",
       "      <td>13.104442</td>\n",
       "      <td>-25.972378</td>\n",
       "      <td>-12145.175563</td>\n",
       "      <td>-174.523751</td>\n",
       "      <td>490.639871</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>-0.831529</td>\n",
       "      <td>-0.788999</td>\n",
       "      <td>1.531034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.945072</td>\n",
       "      <td>8.337878</td>\n",
       "      <td>4.893369</td>\n",
       "      <td>-1.831883e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.665791</td>\n",
       "      <td>596.621456</td>\n",
       "      <td>112.616044</td>\n",
       "      <td>90.717397</td>\n",
       "      <td>18.875627</td>\n",
       "      <td>-35.705216</td>\n",
       "      <td>278.280224</td>\n",
       "      <td>-86.200297</td>\n",
       "      <td>480.605996</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                NY.GNP.MKTP.KD.ZG  NY.GDP.MKTP.KD.ZG  FM.LBL.BMNY.ZG  \\\n",
       "country   year                                                         \n",
       "Argentina 2004           1.069367           9.029573       21.427310   \n",
       "          2003          10.201217           8.837041       29.634853   \n",
       "          2002         -13.204427         -10.894485       19.708854   \n",
       "          2001          -4.613779          -4.408840      -19.436218   \n",
       "          2000          -0.831529          -0.788999        1.531034   \n",
       "\n",
       "                FP.CPI.TOTL.ZG  FR.INR.RINR  FR.INR.DPST  GC.TAX.INTT.RV.ZS  \\\n",
       "country   year                                                                \n",
       "Argentina 2004             NaN    -9.789158     2.614968          15.799545   \n",
       "          2003             NaN     7.828852    10.160701          16.470869   \n",
       "          2002             NaN    16.179804    39.248446          13.638532   \n",
       "          2001             NaN    29.120285    16.163597           4.284476   \n",
       "          2000             NaN     9.945072     8.337878           4.893369   \n",
       "\n",
       "                BN.GSR.GNFS.CD  DT.DOD.PVLX.GN.ZS  DT.DOD.PVLX.EX.ZS   ...    \\\n",
       "country   year                                                         ...     \n",
       "Argentina 2004    1.193361e+10                NaN                NaN   ...     \n",
       "          2003    1.561150e+10                NaN                NaN   ...     \n",
       "          2002    1.571728e+10                NaN                NaN   ...     \n",
       "          2001    3.521811e+09                NaN                NaN   ...     \n",
       "          2000   -1.831883e+09                NaN                NaN   ...     \n",
       "\n",
       "                BX.KLT.DINV.WD.GD.ZS      ted_res    sted_res   sted_xgs  \\\n",
       "country   year                                                             \n",
       "Argentina 2004              2.505018   854.542849  135.042097  67.610786   \n",
       "          2003              1.294811  1154.477701  158.125464  67.663914   \n",
       "          2002              2.198958  1413.593093  141.385413  53.484395   \n",
       "          2001              0.806164  1048.740175  137.431550  64.295618   \n",
       "          2000              3.665791   596.621456  112.616044  90.717397   \n",
       "\n",
       "                 sted_ted    cac_res       cac_pef     cac_fdi     ted_xgs  \\\n",
       "country   year                                                               \n",
       "Argentina 2004  15.802847  16.337013  -3728.136970   77.867050  427.839281   \n",
       "          2003  13.696710  57.496409  12455.899005  492.728858  494.015815   \n",
       "          2002  10.001847  83.551802  -7565.248533  407.956127  534.745201   \n",
       "          2001  13.104442 -25.972378 -12145.175563 -174.523751  490.639871   \n",
       "          2000  18.875627 -35.705216    278.280224  -86.200297  480.605996   \n",
       "\n",
       "                crisis  \n",
       "country   year          \n",
       "Argentina 2004     1.0  \n",
       "          2003     1.0  \n",
       "          2002     1.0  \n",
       "          2001     1.0  \n",
       "          2000     1.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two data sets\n",
    "master = pd.merge(wb_data.reset_index(), master_countries.drop(\"iso\",axis=1), on = [\"country\",\"year\"], how=\"left\")\n",
    "master = master.set_index([\"country\",\"year\"])\n",
    "master.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before digging into the neural network model, we could have a look at how the data behaves.\n",
    "\n",
    "Variables that are known to have a significant effect on the likelihood of debt crisis is the total external debt as % of reserves and GDP growth. Let's see how GDP growth looks on total external debt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_country(x = \"NY.GDP.MKTP.KD.ZG\", y = \"DT.DOD.DECT.GN.ZS\", country = \"Argentina\"):\n",
    "    \n",
    "    \"\"\"\n",
    "    When called, returns the scatter plot of the desired indicators for the desired coutry.\n",
    "    By default, it plots GDP growth against total external debt as % of GNI for Argentina.\n",
    "    --\n",
    "    Arguments:\n",
    "    x: str. Indicator on the x axis, needs to be a World Bank indicator. Default: GDP growth\n",
    "    y: str. Indicator in the y axis, needs to be a World Bank indicator Default: Total external debt as % of GNI\n",
    "    country: str. Needs to be a country in the list 'countries'. Default: 'Argentina'\n",
    "    \"\"\"\n",
    "    fig,ax = plt.subplots()\n",
    "\n",
    "    df = wb_data.reset_index().groupby(\"country\").get_group(country)\n",
    "    df = df.replace([np.inf,-np.inf], np.nan)\n",
    "    df = df.fillna(value=0) # Replace NaN values by 0 (avoids errors)\n",
    "    df.plot(x, y, ax=ax, kind = \"scatter\")\n",
    "    \n",
    "    # Add linear regression line\n",
    "    lr = LinearRegression()\n",
    "    X = df[x].values.reshape(-1,1) # Note the uppercase X here (for linear fitting)\n",
    "    y = df[y].values.reshape(-1,1)\n",
    "    lr.fit(X,y)\n",
    "    \n",
    "    # If the graph is a flat line, it is likely that the data is not available\n",
    "    \n",
    "    _x = np.linspace(-10, 10,100).reshape(-1,1) # Note the lowercase _x here (for plotting)\n",
    "    y_pred = lr.predict(_x)\n",
    "    ax.plot(_x, y_pred, color=\"red\",linestyle=\"--\")\n",
    "    \n",
    "    \n",
    "    ax.set_title(f\"Plot for {country}\")\n",
    "    \n",
    "    \n",
    "    return fig,ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1c1d1b5eb8>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_country(country=\"Bolivia\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also have a look at which countries experience the highest debts by comparing them all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "wb1 = wb_data.reset_index()\n",
    "reduced = wb1[wb1[\"country\"].isin([\"Argentina\",\"Bolivia\",\"Costa Rica\",\"Indonesia\",\"Jamaica\",\"Jordan\"])]\n",
    "colors = qeds.themes.COLOR_CYCLE\n",
    "cmap = dict(zip(reduced[\"country\"].unique(), colors))\n",
    "nom_color = (0.8,0.8,0.8)\n",
    "wb1 = wb1.replace([np.inf,-np.inf], np.nan)\n",
    "wb1 = wb1.fillna(value=0) \n",
    "wb1 = wb1.sort_values(by=\"year\")\n",
    "wb1.index = wb1[\"year\"]\n",
    "gwb1 = wb1.groupby([\"country\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots(figsize = (16,8))\n",
    "for count,df in gwb1:\n",
    "    if df[\"DT.DOD.DECT.GN.ZS\"].max() >= 155:\n",
    "        df.plot(x=\"year\",y=\"DT.DOD.DECT.GN.ZS\", ax=ax1,legend=True, alpha = 1.0, label = count, color = cmap[count])\n",
    "    else:\n",
    "        df.plot(x=\"year\",y=\"DT.DOD.DECT.GN.ZS\", ax=ax1, legend=False, alpha = 0.4, color = nom_color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can note that four countries had an external debt superior to 155% of their GNI: Argentina, Bolivia, Indonesia, Jamaica and Jordan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Model\n",
    "### Network construction\n",
    "We will be using a multi-layer perceptron (MLP) ANN to conduct the prediction. Following [Fioramanti (2008)](#nn-fioramanti2008), the ANN in this study will be a two-layer neural network of the form:\n",
    "\n",
    "$$\n",
    "Y = F \\big(  F  (X \\omega_1 + b_1)\\omega_2 + b_2 \\big)  \\omega_3 + b_3\n",
    "$$\n",
    "\n",
    "Where the activation function $F(\\cdot)$ is the **sigmoid** function (also called logistic function): $F(x) = \\frac{1}{1+e^{-x}}$ which is smooth and differentiable. \n",
    "\n",
    "The $\\omega_i$ are a series of *weight matrices*, $\\omega_1$ multiplies the feature matrix $X$. Each layer is adjusted of a *bias matrix* $b_i$.\n",
    "\n",
    "This neural network has three units in the hidden layer and one unit in the output layer. The author tried additional models with more hidden units but there was not significant performance improvement. This rather simple architecture has, however, a pretty strong approximation power according to the [Universal approximation theorem](https://en.wikipedia.org/wiki/Universal_approximation_theorem) stipulating that neural nets with a single layer and a finite number of neurons can approximate a wide range of continuous functions on $\\mathbb{R}^n$.\n",
    "\n",
    "The network uses three different training sets (identical to the REP referenced in Fioramanti's paper): \n",
    "\n",
    "- It first uses the whole sample to train and uses 2002-2004 to test its prediction (in-sample forecast).\n",
    "- In a second time, the training set uses the years 1981-2001 to train and the rest to assess the performance (out-of-sample forecast).\n",
    "- The third try uses the same approach, but the training years are 1990-2001.\n",
    "\n",
    "Number of observations: 1150\n",
    "\n",
    "Number of independent variables: 35\n",
    "\n",
    "Count of $crisis = 1$: 421\n",
    "\n",
    "Measure of performance: mean-squared errors (MSE)\n",
    "\n",
    "The reference paper uses the [Levenberg–Marquardt](http://people.duke.edu/~hpgavin/ce281/lm.pdf) (LM) algorithm to minimise the MSE of its ANN model. Since the `sklearn` package does not allow for such an algorithm, the solver will be the [LBFGS](http://aria42.com/blog/2014/12/understanding-lbfgs) (for Limited-Memory Broyden–Fletcher–Goldfarb–Shanno) algorithm which is deemed appropriate for smaller datasets. The LBFGS algorithm is designed to optimise smooth function without constraints. We thus set the regularization parameter $\\alpha = 0$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In-sample estimation model (IS) (Train: 1980-2004 / Test: 2002-2004)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nn_in_sample():\n",
    "    \n",
    "    \"\"\"\n",
    "    When called, this function returns the artificial neural network model\n",
    "    using the in-sample training method.\n",
    "    It takes the whole sample as training (1980-2004) and the years 2002-2004 as testing.\n",
    "    \"\"\"\n",
    "    # Flag missing values with an indicator (=1 if value is missing)\n",
    "    indic = master.drop(\"crisis\",axis=1).isnull().replace(to_replace = [True, False], value = [1, 0])\n",
    "    for col in list(indic.columns):\n",
    "        indic = indic.rename(columns={f\"{col}\":f\"missing_{col}\" })\n",
    "    \n",
    "    _master = pd.merge(master, indic, left_index=True, right_index=True, how=\"left\")\n",
    "    \n",
    "    # Some variables spat out \"inf\" values, let's replace them by NaN\n",
    "    df = _master.replace([np.inf, -np.inf], np.nan).copy()\n",
    "    # Replace NaN values by 0 (drops them from the model)\n",
    "    df = df.fillna(value=0)\n",
    "    \n",
    "   \n",
    "    \n",
    "    # Training data\n",
    "    train_data = df # Takes the whole dataset as training\n",
    "    X_train = train_data.drop(\"crisis\", axis=1)\n",
    "    y_train = train_data[\"crisis\"]\n",
    "    \n",
    "    # Testing data\n",
    "    testing_dates = [\"2002\",\"2003\",\"2004\"] # Tests for dates 2002-2004\n",
    "    test_data = df.loc[pd.IndexSlice[:, testing_dates], :]\n",
    "    X_test = test_data.drop(\"crisis\", axis=1)\n",
    "    y_test = test_data[\"crisis\"]\n",
    "    \n",
    "    #Parameters of the model\n",
    "    nn_scaled_model = pipeline.make_pipeline(\n",
    "        preprocessing.StandardScaler(), # this will do the input scaling\n",
    "        neural_network.MLPRegressor((100, 100),\n",
    "                                   activation = \"logistic\",\n",
    "                                   solver = \"lbfgs\",\n",
    "                                   alpha = 0, # LBFGS is designed to work better without penalty\n",
    "                                   max_iter = 10_000, # Keep = 100 to save computing time, can get up to 10000\n",
    "                                   learning_rate = \"adaptive\")\n",
    "    )\n",
    "    \n",
    "    return (nn_scaled_model, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now on to compute the MSE\n",
    "def mse_in_sample():\n",
    "    \"\"\"\n",
    "   When called, this function returns the train and test MSE of the in-sample training method.\n",
    "    \"\"\"\n",
    "    nn_scaled_model, X_train, y_train, X_test, y_test = nn_in_sample() # Import the values from the in-sample model\n",
    "    nn_scaled_model.fit(X_train,y_train)\n",
    "    \n",
    "    #MSE on test and training\n",
    "    mse_train = metrics.mean_squared_error(y_train, nn_scaled_model.predict(X_train))\n",
    "    mse_test = metrics.mean_squared_error(y_test, nn_scaled_model.predict(X_test))\n",
    "    \n",
    "    return [mse_train , mse_test] # Return as a list to be displayed later"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out-of-sample estimation model 1 (OS1) (Train: 1981-2001 / Test: 2002-2004)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nn_out_sample_1():\n",
    "    \n",
    "    \"\"\"\n",
    "    When called, this function returns the artificial neural network model\n",
    "    using the first out-of-sample training model.\n",
    "    It takes the years 1981-2001 as training and 2002-2004 as testing.\n",
    "    \"\"\"\n",
    "    # Flag missing values with an indicator (=1 if value is missing)\n",
    "    indic = master.drop(\"crisis\",axis=1).isnull().replace(to_replace = [True, False], value = [1, 0])\n",
    "    for col in list(indic.columns):\n",
    "        indic = indic.rename(columns={f\"{col}\":f\"missing_{col}\" })\n",
    "    \n",
    "    _master = pd.merge(master, indic, left_index=True, right_index=True, how=\"left\")\n",
    "    \n",
    "    # Some variables spat out \"inf\" values, let's replace them by NaN\n",
    "    df = _master.replace([np.inf, -np.inf], np.nan).copy()\n",
    "    # Replace NaN values by 0 (drops them from the model)\n",
    "    df = df.fillna(value=0)\n",
    "    \n",
    "    \n",
    "    # Training data\n",
    "    training_dates = [\"1981\",\"1982\",\"1983\",\"1984\",\"1985\",\"1986\",\"1987\",\"1988\",\"1989\",\"1990\"\n",
    "                     \"1991\",\"1992\",\"1993\",\"1994\",\"1995\",\"1996\",\"1997\",\"1998\",\"1999\",\"2000\",\"2001\"]\n",
    "    train_data = df.loc[pd.IndexSlice[:, training_dates], :] \n",
    "    X_train = train_data.drop(\"crisis\", axis=1)\n",
    "    y_train = train_data[\"crisis\"]\n",
    "    \n",
    "    # Testing data\n",
    "    testing_dates = [\"2002\",\"2003\",\"2004\"] # Tests for dates 2002-2004\n",
    "    test_data = df.loc[pd.IndexSlice[:, testing_dates], :]\n",
    "    X_test = test_data.drop(\"crisis\", axis=1)\n",
    "    y_test = test_data[\"crisis\"]\n",
    "    \n",
    "    #Parameters of the model\n",
    "    nn_scaled_model = pipeline.make_pipeline(\n",
    "        preprocessing.StandardScaler(), # this will do the input scaling\n",
    "        neural_network.MLPRegressor((100, 100),\n",
    "                                   activation = \"logistic\",\n",
    "                                   solver = \"lbfgs\",\n",
    "                                   alpha = 0, # LBFGS is designed to work better without penalty\n",
    "                                   max_iter = 10_000, # Keep = 100 to save computing time, can get up to 10000\n",
    "                                   learning_rate = \"adaptive\")\n",
    "    )\n",
    "    \n",
    "    return (nn_scaled_model, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now on to compute the MSE\n",
    "def mse_out_sample_1():\n",
    "    \"\"\"\n",
    "   When called, this function returns the train and test MSE of the first out-of-sample training method.\n",
    "    \"\"\"\n",
    "    nn_scaled_model, X_train, y_train, X_test, y_test = nn_out_sample_1() # Import the values\n",
    "    nn_scaled_model.fit(X_train,y_train)\n",
    "    \n",
    "    #MSE on test and training\n",
    "    mse_train = metrics.mean_squared_error(y_train, nn_scaled_model.predict(X_train))\n",
    "    mse_test = metrics.mean_squared_error(y_test, nn_scaled_model.predict(X_test))\n",
    "    \n",
    "    return [mse_train , mse_test] # Return as a list to be displayed later"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out-of-sample estimation model 2 (OS2) (Train: 1991-2001 / Test: 2002-2004)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nn_out_sample_2():\n",
    "    \n",
    "    \"\"\"\n",
    "    When called, this function returns the artificial neural network model\n",
    "    using the second out-of-sample training model.\n",
    "    It takes the years 1991-2001 as training and 2002-2004 as testing.\n",
    "    \"\"\"\n",
    "    # Flag missing values with an indicator (=1 if value is missing)\n",
    "    indic = master.drop(\"crisis\",axis=1).isnull().replace(to_replace = [True, False], value = [1, 0])\n",
    "    for col in list(indic.columns):\n",
    "        indic = indic.rename(columns={f\"{col}\":f\"missing_{col}\" })\n",
    "    \n",
    "    _master = pd.merge(master, indic, left_index=True, right_index=True, how=\"left\")\n",
    "    \n",
    "    # Some variables spat out \"inf\" values, let's replace them by NaN\n",
    "    df = _master.replace([np.inf, -np.inf], np.nan).copy()\n",
    "    # Replace NaN values by 0 (drops them from the model)\n",
    "    df = df.fillna(value=0)\n",
    "    \n",
    "    \n",
    "    # Training data\n",
    "    training_dates = [\"1991\",\"1992\",\"1993\",\"1994\",\"1995\",\"1996\",\"1997\",\"1998\",\"1999\",\"2000\",\"2001\"]\n",
    "    train_data = df.loc[pd.IndexSlice[:, training_dates], :] \n",
    "    X_train = train_data.drop(\"crisis\", axis=1)\n",
    "    y_train = train_data[\"crisis\"]\n",
    "    \n",
    "    # Testing data\n",
    "    testing_dates = [\"2002\",\"2003\",\"2004\"] # Tests for dates 2002-2004\n",
    "    test_data = df.loc[pd.IndexSlice[:, testing_dates], :]\n",
    "    X_test = test_data.drop(\"crisis\", axis=1)\n",
    "    y_test = test_data[\"crisis\"]\n",
    "    \n",
    "    #Parameters of the model\n",
    "    nn_scaled_model = pipeline.make_pipeline(\n",
    "        preprocessing.StandardScaler(), # this will do the input scaling\n",
    "        neural_network.MLPRegressor((100, 100),\n",
    "                                   activation = \"logistic\",\n",
    "                                   solver = \"lbfgs\",\n",
    "                                   alpha = 0, # LBFGS is designed to work better without penalty\n",
    "                                   max_iter = 10_000, # Keep = 100 to save computing time, can get up to 10000\n",
    "                                   learning_rate = \"adaptive\")\n",
    "    )\n",
    "    \n",
    "    return (nn_scaled_model, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now on to compute the MSE\n",
    "def mse_out_sample_2():\n",
    "    \"\"\"\n",
    "   When called, this function returns the train and test MSE of the second out-of-sample training method.\n",
    "    \"\"\"\n",
    "    nn_scaled_model, X_train, y_train, X_test, y_test = nn_out_sample_2() # Import the values\n",
    "    nn_scaled_model.fit(X_train,y_train)\n",
    "    \n",
    "    #MSE on test and training\n",
    "    mse_train = metrics.mean_squared_error(y_train, nn_scaled_model.predict(X_train))\n",
    "    mse_test = metrics.mean_squared_error(y_test, nn_scaled_model.predict(X_test))\n",
    "    \n",
    "    return [mse_train , mse_test] # Return as a list to be displayed later"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean squared errors (MSE)\n",
    "\n",
    "Now let's finally measure the MSE of these three models. It displays the results of Training and Testing for the in-sample (IS), the out-of-sample 1 (OS1) and the out-of-sample 2 (OS2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IS</th>\n",
       "      <th>OS1</th>\n",
       "      <th>OS2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Training MSE</th>\n",
       "      <td>0.000984</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Testing MSE</th>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.168114</td>\n",
       "      <td>0.173089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    IS       OS1       OS2\n",
       "Training MSE  0.000984  0.000110  0.000297\n",
       "Testing MSE   0.000408  0.168114  0.173089"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_mse = pd.DataFrame(columns = [\"IS\", \"OS1\",\"OS2\"])\n",
    "table_mse[\"IS\"] = mse_in_sample()\n",
    "table_mse[\"OS1\"] = mse_out_sample_1()\n",
    "table_mse[\"OS2\"] = mse_out_sample_2()\n",
    "table_mse.index = [\"Training MSE\", \"Testing MSE\"]\n",
    "table_mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The outcome usually changes each time we run the code. However, the order of the results remain relatively the same. In terms of prediction (\"testing\"), the IS model performs better than the two others. This result follows quite logically since it uses more data as training and is used to predict outcomes that are included in its training set. The testing MSE of OS2 is larger because it uses less data as training.\n",
    "\n",
    "But more important than the MSE is the model's **predictive power**. It needs to be able to correctly predict the outcomes when the data is fed to it. The proportion of correct predictions is referred as the \"hit rate\". This hit rate is particularly interesting since it can be used for *comparison* between models. Here, the interest is to compare the ANN's hit rate with the REP in [Fioramanti (2008)](#nn-fioramanti2008)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hit rate "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since model predictions are continuous over roughly $[0,1]$ (I say roughly as some predictions might be slightly higher or lower than the bounds). Since predictions are not exactly 0 or 1 but are closer to a probability (though not exactly since some values are out of $[0,1]$), defining what is a \"success\" remains arbitrary. \n",
    "\n",
    "In this case, the chosen boundary for a success is in $\\pm 15\\%$ of the correct outcome. For instance:\n",
    "\n",
    "- A prediction **superior** to $0.85$ while $crisis = 1$ is considered as a success, the `upper_bound`.\n",
    "- A prediction **inferior** to $0.15$ while $crisis = 0$ is considered as a success, the `lower_bound`.\n",
    "- Every other configuration is considered as a failure.\n",
    "\n",
    "It is possible to change these bounds and see the predictive power of the model under different conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hit_rate(upper_bound = 0.85, lower_bound = 0.15):\n",
    "    \n",
    "    \"\"\"\n",
    "    When called, returns the proportion of correctly predicted outcomes for the three different ANN models.\n",
    "    ---\n",
    "    Arguments:\n",
    "    upper_bound: float. The bound above which a prediction is considered a success. Default = 0.85\n",
    "    lower_bound: float. The bound below which a prediction is considered a success. Default = 0.15\n",
    "    \"\"\"\n",
    "    \n",
    "    # Flag missing values with an indicator (=1 if value is missing)\n",
    "    indic = master.drop(\"crisis\",axis=1).isnull().replace(to_replace = [True, False], value = [1, 0])\n",
    "    for col in list(indic.columns):\n",
    "        indic = indic.rename(columns={f\"{col}\":f\"missing_{col}\" })\n",
    "    \n",
    "    _master = pd.merge(master, indic, left_index=True, right_index=True, how=\"left\")\n",
    "    \n",
    "    # Some variables spat out \"inf\" values, let's replace them by NaN\n",
    "    df = _master.replace([np.inf, -np.inf], np.nan).copy()\n",
    "    # Replace NaN values by 0 (drops them from the model)\n",
    "    df = df.fillna(value=0)\n",
    "    \n",
    "    \n",
    "    \n",
    "    # Import models\n",
    "    nn_scaled_is, X_train_is, y_train_is, X_test_is, y_test_is = nn_in_sample()\n",
    "    nn_scaled_os1, X_train_os1, y_train_os1, X_test_os1, y_test_os1 = nn_out_sample_1()\n",
    "    nn_scaled_os2, X_train_os2, y_train_os2, X_test_os2, y_test_os2 = nn_out_sample_2()\n",
    "    \n",
    "    # Fit models\n",
    "    nn_scaled_is.fit(X_train_is, y_train_is)\n",
    "    nn_scaled_os1.fit(X_train_os1, y_train_os1)\n",
    "    nn_scaled_os2.fit(X_train_os2, y_train_os2)\n",
    "    \n",
    "    # Predict outcome\n",
    "    y_predict_is = nn_scaled_is.predict(X_test_is)\n",
    "    y_predict_os1 = nn_scaled_os1.predict(X_test_os1)\n",
    "    y_predict_os2 = nn_scaled_os2.predict(X_test_os2)\n",
    "    \n",
    "    predict = df.loc[pd.IndexSlice[:,[\"2002\",\"2003\",\"2004\"]], :]\n",
    "    \n",
    "    predict[\"crisis_prediction_is\"] = y_predict_is\n",
    "    predict[\"crisis_prediction_os1\"] = y_predict_os1\n",
    "    predict[\"crisis_prediction_os2\"] = y_predict_os2\n",
    "    \n",
    "    \n",
    "    predict[\"success_is\"] = np.nan \n",
    "    predict[\"success_os1\"] = np.nan\n",
    "    predict[\"success_os2\"] = np.nan\n",
    "    \n",
    "    \n",
    "    for i in range(len(predict[\"crisis\"])): # There are 138 rows in the \"predict\" DataFrame\n",
    "        if (predict[\"crisis_prediction_is\"][i] >= upper_bound) & (predict[\"crisis\"][i] == 1):\n",
    "            predict[\"success_is\"][i] = 1 # Correctly predicted a crisis  \n",
    "        elif (predict[\"crisis_prediction_is\"][i] <= lower_bound) & (predict[\"crisis\"][i] == 0):\n",
    "            predict[\"success_is\"][i] = 1 # Correctly predicted no crisis\n",
    "        else:\n",
    "            predict[\"success_is\"][i] = 0\n",
    "\n",
    "    \n",
    "        if (predict[\"crisis_prediction_os1\"][i] >= upper_bound) & (predict[\"crisis\"][i] == 1):\n",
    "            predict[\"success_os1\"][i] = 1\n",
    "        elif (predict[\"crisis_prediction_os1\"][i] <= lower_bound) & (predict[\"crisis\"][i] == 0):\n",
    "            predict[\"success_os1\"][i] = 1\n",
    "        else:\n",
    "            predict[\"success_os1\"][i] = 0\n",
    "\n",
    "    \n",
    "        if (predict[\"crisis_prediction_os2\"][i] >= upper_bound) & (predict[\"crisis\"][i] == 1):\n",
    "            predict[\"success_os2\"][i] = 1\n",
    "        elif (predict[\"crisis_prediction_os2\"][i] <= lower_bound) & (predict[\"crisis\"][i] == 0):\n",
    "            predict[\"success_os2\"][i] = 1\n",
    "        else:\n",
    "            predict[\"success_os2\"][i] = 0\n",
    "\n",
    "    \n",
    "    hit_rate_is = (int(predict.loc[predict[\"success_is\"] == 1][\"success_is\"].value_counts()) / len(predict[\"crisis\"]))*100\n",
    "    \n",
    "    hit_rate_os1 = (int(predict.loc[predict[\"success_os1\"] == 1][\"success_os1\"].value_counts()) / len(predict[\"crisis\"]))*100\n",
    "    \n",
    "    hit_rate_os2 = (int(predict.loc[predict[\"success_os2\"] == 1][\"success_os2\"].value_counts()) / len(predict[\"crisis\"]))*100\n",
    "    \n",
    "    return [hit_rate_is, hit_rate_os1, hit_rate_os2]\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's compare our hit rate with the one of the REP. The performance of our neural network increases as we increase the maximum number of iterations. Abbreviations stand for random effect probit (REP), restricted REP (R_REP), in-sample model (IS), out-of-sample model 1 (OS1) and out-of-sample model 2 (OS2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REP</th>\n",
       "      <th>R_REP</th>\n",
       "      <th>IS</th>\n",
       "      <th>OS1</th>\n",
       "      <th>OS2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Prediction success (%)</th>\n",
       "      <td>87.62</td>\n",
       "      <td>81.55</td>\n",
       "      <td>100.0</td>\n",
       "      <td>75.362319</td>\n",
       "      <td>71.73913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          REP  R_REP     IS        OS1       OS2\n",
       "Prediction success (%)  87.62  81.55  100.0  75.362319  71.73913"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def comparison_rep():\n",
    "    \n",
    "    hit_rate_is, hit_rate_os1, hit_rate_os2 = hit_rate() # Import hit rates\n",
    "    \n",
    "    comp = pd.DataFrame(columns = [\"REP\", \"R_REP\", \"IS\", \"OS1\", \"OS2\"])\n",
    "    comp[\"REP\"] = [87.62] # Average computed from Fioramanti (2008)\n",
    "    comp[\"R_REP\"] = [81.55] # Average computed from Fioramanti (2008)\n",
    "    comp[\"IS\"] = [hit_rate_is]\n",
    "    comp[\"OS1\"] = [hit_rate_os1]\n",
    "    comp[\"OS2\"] = [hit_rate_os2]\n",
    "    comp.index = [\"Prediction success (%)\"]\n",
    "    return comp\n",
    "\n",
    "comparison_rep()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "It is possible to see that with a threshold of $\\pm 15\\%$ for success flagging, the model probably will not beat the REP and the restricted REP. Variying this threshold might change the results. Note that the prediction results of the IS model approach 100% (and sometimes hits 100%). Nonetheless, this result is not that exceptional since it was asked to predict data it was already trained on. A more interesting form of predicting power lies in models OS1 and OS2 where the ANN had to predict data it was not trained on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "<a id='nn-ciarlone2005'></a>\n",
    "Ciarlone, A., Trebeschi, G., 2005. Designing an Early Warning System for debt crises. *Emerging Makets Review*, No. 6 Vol. 4, pp. 376–395.\n",
    "\n",
    "<a id='nn-fioramanti2008'></a>\n",
    "Fioramanti, M., 2008. Predicting sovereign debt crises using artificial neural networks: A comparative approach. *Journal of Financial Stability*, Vol. 4, pp. 149-164.\n",
    "\n",
    "<a id='nn-MRS2003'></a>\n",
    "Manasse, P., Roubini, N., Schimmelpfenning, A., 2003. Predicting sovereign debt crises. *IMF Working Paper* No. 221."
   ]
  }
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