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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Eurozone economics"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We examine the usual suspects: unemployment, inflation, real interest rate, foreign exchange rate, comparative GDP. Appendix 1 concisely explains the *euro crisis* in a video."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*Dependencies:*\n",
      "\n",
      "    - Linux, bash [not crucial, cross-platform prefered]\n",
      "    - Python: matplotlib, pandas [recommend Anaconda distribution]\n",
      "    - Modules: yi_1tools, yi_plot, yi_timeseries, yi_fred\n",
      "     \n",
      "*CHANGE LOG*\n",
      "\n",
      "    2015-02-03  Code review and revision.\n",
      "    2014-09-22  First version."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  NOTEBOOK settings and system details:   [00-tpl v14.09.28]\n",
      "\n",
      "#  Assume that the backend is LINUX (our particular distro is Ubuntu, running bash shell):\n",
      "print '\\n ::  TIMESTAMP of last notebook execution:'\n",
      "!date\n",
      "print '\\n ::  IPython version:'\n",
      "!ipython --version\n",
      "\n",
      "#  Automatically reload modified modules:\n",
      "%load_ext autoreload\n",
      "%autoreload 2   \n",
      "#           0 will disable autoreload.\n",
      "#  Generate plots inside notebook:\n",
      "%matplotlib inline\n",
      "\n",
      "#  DISPLAY options\n",
      "from IPython.display import Image \n",
      "#  e.g. Image(filename='holt-winters-equations.png', embed=True)\n",
      "from IPython.display import YouTubeVideo\n",
      "#  e.g. YouTubeVideo('1j_HxD4iLn8')\n",
      "from IPython.display import HTML # useful for snippets\n",
      "#  e.g. HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')\n",
      "import pandas as pd\n",
      "print '\\n ::  pandas version:'\n",
      "print pd.__version__\n",
      "#      pandas DataFrames are represented as text by default; enable HTML representation:\n",
      "#      [Deprecated: pd.core.format.set_printoptions( notebook_repr_html=True ) ]\n",
      "pd.set_option( 'display.notebook_repr_html', False )\n",
      "\n",
      "#  MATH display, use %%latex, rather than the following:\n",
      "#                from IPython.display import Math\n",
      "#                from IPython.display import Latex\n",
      "\n",
      "print '\\n ::  Working directory (set as $workd):'\n",
      "workd, = !pwd\n",
      "print workd + '\\n'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " ::  TIMESTAMP of last notebook execution:\n",
        "Fri Feb  6 07:54:55 PST 2015"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " ::  IPython version:\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.3.0\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " ::  pandas version:\n",
        "0.15.0\n",
        "\n",
        " ::  Working directory (set as $workd):\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/yaya/Dropbox/ipy/fecon235/nb\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from yi_1tools import *\n",
      "from yi_fred import *\n",
      "from yi_plot import *\n",
      "from yi_timeseries import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "An economic crisis in the Eurozone shows up as civil discontent over unemployment."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  EU data has not been updated recently (one year lag!), \n",
      "#  thus we shall use France as PROXY for the time being:\n",
      "unempeu = getfred( m4unempfr )\n",
      "\n",
      "#  unempeu = getfred( m4unempeu )\n",
      "#  Uncomment when the update situation changes."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotfred(unempeu)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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qBMmwaEBYuM5wKXlquojMzHF411KvHxSuM1hcZ+CINi8C\nMS9RtOoBERkARGYRXlxnqARRS2QXYByW85jOlACuHxSuM1hcZ7AsE5FhqjodIDEyPB74ExCJZfYS\nuM4QCSJgP4otVNBqQVsReTaA6weF6wwW1xksp5O2Uo+qbhKRM4A/hCMpI64zRHxquuM4TkwIIg/b\ncRzHqQAesB3HcWKCB2zHcZyY4AHbcRwnJnjAdhzHiQn/D8UtxqXO/BegAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad3d0ccc>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we looked at youth unemployment, the numbers would look far worse. Likewise for single country unemployment, for example, from Greece or Portugal. The situation was more alarming in the late 1990's. **Employment seems to be improving over the longer term, as seen in the trend next.**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  What is the long-term unemployment trend?\n",
      "unemptrend = trend( unempeu )\n",
      "plotfred( unemptrend )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  regresstime slope = -0.00406865653928\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rwO/qu9BfbpLuATYE2gBfAt8BJgOHEHqgnZtgvK9Jeh/4D9AB+AMw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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad35ae4c>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Deviation from long-term trend:\n",
      "unempdev = todf( unempeu - unemptrend )\n",
      "plotfred(unempdev)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fcUbh541LK2vFCptsUFkZtpLGR1yugaZA3Zb2jjtaK3nYMJtyvn692Se77AKn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\njPCg7TiOEyP+P06/R2bae7EdAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad35acec>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The introduction of the euro currency seems to have improved employment post-1999, but then the Great Recession in America has global effects.\n",
      "\n",
      "2015-02-05: additional 1.5% unemployment could bring massive civil unrest."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Inflation in the Eurozone"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "infleu = getfred(m4infleu)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Compute inflation rate from levels: \n",
      "infreu = pcent(infleu, 12).dropna()\n",
      "plotfred(infreu)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DjoOBfeo8F/QARNRMmqR66KG5lzuHxDvv2CDRddc1bsD0s88s2LdsqbrjjqrT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\njgMU39NWhS++cE87G/362SK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gvSTrIvMuqAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad2ecb8c>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the 21st century, we see two deflationary periods: first, due to the Great Recession globally, second, due to the euro crisis. The latter showed that Euro monetary policy also needs an unified *fiscal* policy across the Eurozone. \n",
      "\n",
      "The euro crisis is renewed in 2015, again exemplified by alarming Greek debt and problems with forced austerity."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats(infreu)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                Y\n",
        "count  216.000000\n",
        "mean     1.875247\n",
        "std      0.910160\n",
        "min     -0.848638\n",
        "25%      1.285137\n",
        "50%      2.080548\n",
        "75%      2.394655\n",
        "max      4.117544\n",
        "\n",
        " ::  Index on min:\n",
        "Y   2009-10-01\n",
        "dtype: datetime64[ns]\n",
        "\n",
        " ::  Index on max:\n",
        "Y   2008-09-01\n",
        "dtype: datetime64[ns]\n",
        "\n",
        " ::  Head:\n",
        "                   Y\n",
        "T                   \n",
        "1997-01-01  2.202703\n",
        "1997-02-01  1.919892\n",
        "1997-03-01  1.657404\n",
        "1997-04-01  1.388867\n",
        "1997-05-01  1.216077\n",
        "1997-06-01  1.102625\n",
        "1997-07-01  1.065886\n",
        "\n",
        " ::  Tail:\n",
        "                   Y\n",
        "T                   \n",
        "2014-06-01  0.375086\n",
        "2014-07-01  0.319713\n",
        "2014-08-01  0.279173\n",
        "2014-09-01  0.239913\n",
        "2014-10-01  0.230103\n",
        "2014-11-01  0.205718\n",
        "2014-12-01  0.074302"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        " ::  Correlation matrix:\n",
        "   Y\n",
        "Y  1\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Inflation following Great Recession:\n",
      "georet(infleu[t06], 12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "[2.07, 2.07, 0.34, 12]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "The inflation rate plot looks a bit wild, but after the Great Recession we are still seeing inflation around long-term arithmetic average of 1.9%. The geometric rate is around 2.0%. Now recent declines have set off deflation alarms, so here's the forecast which says inflation will not go sub-zero within one year:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Holt-Winters forecast on inflation level is more suitable due to its trend properties:\n",
      "holtfred(infleu, 12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "    Forecast\n",
        "0   1.000000\n",
        "1   1.000729\n",
        "2   1.000893\n",
        "3   1.001056\n",
        "4   1.001220\n",
        "5   1.001383\n",
        "6   1.001546\n",
        "7   1.001710\n",
        "8   1.001873\n",
        "9   1.002037\n",
        "10  1.002200\n",
        "11  1.002364\n",
        "12  1.002527"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "2015-02-05: Using level data (not rates), inflation rate is **forecasted to be 0.25% over the next 12 months**. ECB just started QE, so we shall have to see if inflation will move towards the target rate of 2.0%."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Real interest rate for euros "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  3-month LIBOR for Euros, resampled monthly from daily data:\n",
      "libeur = monthly( getfred(d4libeur) )\n",
      "plotfred( libeur )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pOtOEoDMEjYUQ+WLvXK0Q71kvTbNmltDpttviVpIsbr/ddokvx141uGft5E7F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\nYB2Kj+U6o6Xcda67rs27njo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       "text": [
        "<matplotlib.figure.Figure at 0xad18bb4c>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  We compute the real rate for 3-month tenor:\n",
      "realrate = todf( libeur - infreu )\n",
      "plotfred(realrate)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CJpVCfe1y1r/s2xc++KC0c53o8KAdkAR/6/DD4f7766OWURBRXs/6evOyC8l6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\nAx60Hccpic02g7PPLn6hhKR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Bpar6VZPXBdhFVV+LRtmKuM5wcZ3hkhSdkCythRD6IgiO\n4zhO5QjTHnEcx3EqjAdtx3GcBOFB23EcJ0F40HYcx0kQHrQdx3ESxP8HpHCXqCDn/74AAAAASUVO\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad12118c>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The negative real rates after 2010 explains increasing gold price, and its decline as well. \n",
      "\n",
      "2014-09-22: Still no compelling reason to lend to Europe, unless bond prices rally on LSAP (unlikely since it's confined to ABS asset backed bonds, not sovereigns). \n",
      "\n",
      "2015-02-05: ECB will start QE, where sovereign bonds will be purchased by respective national central banks. There is no risk-sharing among the EU members."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Comparative GDP"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Real Eurozone GDP, resampled monthly:\n",
      "gdpeur = getfred( m4gdpeur )\n",
      "plotfred( gdpeur )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xad12de8c>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Let's now look the YoY growth rate:\n",
      "gdpeur_rate = pcent( gdpeur, 12).dropna()\n",
      "plotfred( gdpeur_rate )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gYk9JAwYsn1E5aJCl5B01Ctq2jVZfrdCrVzwW4J06FTp1Kt0asS1aWP9XNbS6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\nvjqs6zM0YofnKnGcGqJjRxg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       "text": [
        "<matplotlib.figure.Figure at 0xad1ff34c>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Real Eurozone GDP geometric growth after 2007 looks negative:\n",
      "georet( gdpeur['2007':], 12 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "[-0.62, -0.62, 0.94, 12]"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "EURUSD to reprice Eurozone GDP in dollars for global perspective"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Get the forex rate for EURUSD:\n",
      "eurusd = getfred( m4eurusd )\n",
      "plotfred( eurusd )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  EURUSD synthetically goes back monthly to 1971.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TndAY77bC7t0yFLp7d+Cqq4B586Lzke2Yx23TiVlUlNgbUaJ57jnJ4EiW511b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\n4z16tKQ1Bl3rWz3v8GPq0Ntn1nnjDff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Aa8z8n5hjaZ6goihKCsRLFUzmec8HMJKIygBs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\nKDmIGm9FUZQcRI23oihKDvL/ARKqKo+j5+BxAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad14760c>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Real Eurozone GDP in dollar terms:\n",
      "gdpeurusd = todf( eurusd * gdpeur )\n",
      "plotfred( gdpeurusd )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QwcqbN7d1dD/+OPy1vvnGgviuu5ZSr54NhU0cLXTLLTbR7frr7Qslm2sEFG2Q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\njuMUMR7kHcdxihgP8o7jOEXM/wdvsz5fU6T48wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad2231ac>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Real Eurozone GDP growth after 2007 in dollars is slightly positive:\n",
      "georet( gdpeurusd['2007':], 12 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "[0.25, 0.7, 9.52, 12]"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note the increase in volatility due to forex factor."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Comparison to real US GDP:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gdpusr = getfred( m4gdpusr )\n",
      "#  Real GDP data for US goes back further than EU, so for a comparative look:\n",
      "plotfred( gdpusr['1996':] )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xad1043ac>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Real US GDP growth in dollars after 2007 is superior to EU:\n",
      "georet( gdpusr['2007':], 12 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "[1.07, 1.08, 0.91, 12]"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Size of Eurozone / US economies in terms of real GDP:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "eu2us = todf( gdpeurusd / gdpusr )\n",
      "plotfred( eu2us )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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L66xNdZ184B57jnToUDdwwnEy5dNPrbXXp0+dz75uHRx5ZOladapw6qk2nduS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\n7I7jOBXG/wcWhpoDs/jnuAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad32688c>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "2015-02-05: After 2007, Eurozone grew only 0.25% in dollar terms, whereas the US grew at 1.07% -- thus one can see the relative size of the Eurozone economy shrinking recently. \n",
      "\n",
      "(Related fact: the allocation of Eurozone equities is also shrinking in global portfolios.)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotfred( trend( eu2us ) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ::  regresstime slope = 0.000102444675953\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xad3daeac>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Over the long-term horizon, the Eurozone economy will be roughly a fifth of the United States."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Appendix 1: Euro crisis explained concisely"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  Source: Bloomberg\n",
      "YouTubeVideo( 'C8xAXJx9WJ8' )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"https://www.youtube.com/embed/C8xAXJx9WJ8\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0xad14cf0c>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}