In [1]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import Quandl
import wbdata as wb
from scipy import stats
import runProcs
%matplotlib inline
In [2]:
# 0. Preliminaries
# 0.1 general plot settings
font = {'weight' : 'bold',
'size' : 15}
axes={'labelweight' : 'bold'}
plt.rc('font', **font)
plt.rc('axes', **axes)
plt.rcParams['xtick.major.pad']='8'
plt.rcParams['ytick.major.pad']='8'
In [3]:
# 1. Import country codes and organize
# 1.1 Import country codes and names from the country_codes file from Quandl's WB WDI documentation: https://www.quandl.com/data/WWDI/documentation/documentation
countryCodes = {}
try:
text_file = open('country_codes', 'r')
lines = text_file.readlines()
for line in lines:
split = line.split('|')
if len(split)>1:
if len(split[1])==4:
countryCodes[split[0]] = split[1][:-1]
except:
countryCodes = {
'Afghanistan': 'AFG',
'Africa': 'AFR',
'Albania': 'ALB',
'Algeria': 'DZA',
'American Samoa': 'ASM',
'Andorra': 'AND',
'Angola': 'AGO',
'Antigua and Barbuda': 'ATG',
'Arab World': 'ARB',
'Argentina': 'ARG',
'Armenia': 'ARM',
'Aruba': 'ABW',
'Australia': 'AUS',
'Austria': 'AUT',
'Azerbaijan': 'AZE',
'Bahamas, The': 'BHS',
'Bahrain': 'BHR',
'Bangladesh': 'BGD',
'Barbados': 'BRB',
'Belarus': 'BLR',
'Belgium': 'BEL',
'Belize': 'BLZ',
'Benin': 'BEN',
'Bermuda': 'BMU',
'Bhutan': 'BTN',
'Bolivia': 'BOL',
'Bosnia and Herzegovina': 'BIH',
'Botswana': 'BWA',
'Brazil': 'BRA',
'Brunei Darussalam': 'BRN',
'Bulgaria': 'BGR',
'Burkina Faso': 'BFA',
'Burundi': 'BDI',
'Cabo Verde': 'CPV',
'Cambodia': 'KHM',
'Cameroon': 'CMR',
'Canada': 'CAN',
'Caribbean small states': 'CSS',
'Cayman Islands': 'CYM',
'Central African Republic': 'CAF',
'Chad': 'TCD',
'Channel Islands': 'CHI',
'Chile': 'CHL',
'China': 'CHN',
'Colombia': 'COL',
'Comoros': 'COM',
'Congo, Dem. Rep.': 'COD',
'Congo, Rep.': 'COG',
'Costa Rica': 'CRI',
"Cote d'Ivoire": 'CIV',
'Croatia': 'HRV',
'Cuba': 'CUB',
'Curacao': 'CUW',
'Cyprus': 'CYP',
'Czech Republic': 'CZE',
'Denmark': 'DNK',
'Djibouti': 'DJI',
'Dominica': 'DMA',
'Dominican Republic': 'DOM',
'East Asia & Pacific (all income levels)': 'EAS',
'East Asia & Pacific (developing only)': 'EAP',
'East Asia and the Pacific (IFC classification)': 'CEA',
'Ecuador': 'ECU',
'Egypt, Arab Rep.': 'EGY',
'El Salvador': 'SLV',
'Equatorial Guinea': 'GNQ',
'Eritrea': 'ERI',
'Estonia': 'EST',
'Ethiopia': 'ETH',
'Euro area': 'EMU',
'Europe & Central Asia (all income levels)': 'ECS',
'Europe & Central Asia (developing only)': 'ECA',
'Europe and Central Asia (IFC classification)': 'CEU',
'European Union': 'EUU',
'Faeroe Islands': 'FRO',
'Fiji': 'FJI',
'Finland': 'FIN',
'France': 'FRA',
'French Polynesia': 'PYF',
'Gabon': 'GAB',
'Gambia, The': 'GMB',
'Georgia': 'GEO',
'Germany': 'DEU',
'Ghana': 'GHA',
'Greece': 'GRC',
'Greenland': 'GRL',
'Grenada': 'GRD',
'Guam': 'GUM',
'Guatemala': 'GTM',
'Guinea': 'GIN',
'Guinea-Bissau': 'GNB',
'Guyana': 'GUY',
'Haiti': 'HTI',
'Heavily indebted poor countries (HIPC)': 'HPC',
'High income': 'HIC',
'High income: OECD': 'OEC',
'High income: nonOECD': 'NOC',
'Honduras': 'HND',
'Hong Kong SAR, China': 'HKG',
'Hungary': 'HUN',
'Iceland': 'ISL',
'India': 'IND',
'Indonesia': 'IDN',
'Iran, Islamic Rep.': 'IRN',
'Iraq': 'IRQ',
'Ireland': 'IRL',
'Isle of Man': 'IMN',
'Israel': 'ISR',
'Italy': 'ITA',
'Jamaica': 'JAM',
'Japan': 'JPN',
'Jordan': 'JOR',
'Kazakhstan': 'KAZ',
'Kenya': 'KEN',
'Kiribati': 'KIR',
'Korea, Dem. Rep.': 'PRK',
'Korea, Rep.': 'KOR',
'Kosovo': 'KSV',
'Kuwait': 'KWT',
'Kyrgyz Republic': 'KGZ',
'Lao PDR': 'LAO',
'Latin America & Caribbean (all income levels)': 'LCN',
'Latin America & Caribbean (developing only)': 'LAC',
'Latin America and the Caribbean (IFC classification)': 'CLA',
'Latvia': 'LVA',
'Least developed countries: UN classification': 'LDC',
'Lebanon': 'LBN',
'Lesotho': 'LSO',
'Liberia': 'LBR',
'Libya': 'LBY',
'Liechtenstein': 'LIE',
'Lithuania': 'LTU',
'Low & middle income': 'LMY',
'Low income': 'LIC',
'Lower middle income': 'LMC',
'Luxembourg': 'LUX',
'Macao SAR, China': 'MAC',
'Macedonia, FYR': 'MKD',
'Madagascar': 'MDG',
'Malawi': 'MWI',
'Malaysia': 'MYS',
'Maldives': 'MDV',
'Mali': 'MLI',
'Malta': 'MLT',
'Marshall Islands': 'MHL',
'Mauritania': 'MRT',
'Mauritius': 'MUS',
'Mexico': 'MEX',
'Micronesia, Fed. Sts.': 'FSM',
'Middle East & North Africa (all income levels)': 'MEA',
'Middle East & North Africa (developing only)': 'MNA',
'Middle East and North Africa (IFC classification)': 'CME',
'Middle income': 'MIC',
'Moldova': 'MDA',
'Monaco': 'MCO',
'Mongolia': 'MNG',
'Montenegro': 'MNE',
'Morocco': 'MAR',
'Mozambique': 'MOZ',
'Myanmar': 'MMR',
'Namibia': 'NAM',
'Nepal': 'NPL',
'Netherlands': 'NLD',
'New Caledonia': 'NCL',
'New Zealand': 'NZL',
'Nicaragua': 'NIC',
'Niger': 'NER',
'Nigeria': 'NGA',
'North Africa': 'NAF',
'North America': 'NAC',
'Northern Mariana Islands': 'MNP',
'Norway': 'NOR',
'OECD members': 'OED',
'Oman': 'OMN',
'Other small states': 'OSS',
'Pacific island small states': 'PSS',
'Pakistan': 'PAK',
'Palau': 'PLW',
'Panama': 'PAN',
'Papua New Guinea': 'PNG',
'Paraguay': 'PRY',
'Peru': 'PER',
'Philippines': 'PHL',
'Poland': 'POL',
'Portugal': 'PRT',
'Puerto Rico': 'PRI',
'Qatar': 'QAT',
'Romania': 'ROU',
'Russian Federation': 'RUS',
'Rwanda': 'RWA',
'Samoa': 'WSM',
'San Marino': 'SMR',
'Sao Tome and Principe': 'STP',
'Saudi Arabia': 'SAU',
'Senegal': 'SEN',
'Serbia': 'SRB',
'Seychelles': 'SYC',
'Sierra Leone': 'SLE',
'Singapore': 'SGP',
'Sint Maarten (Dutch part)': 'SXM',
'Slovak Republic': 'SVK',
'Slovenia': 'SVN',
'Small states': 'SST',
'Solomon Islands': 'SLB',
'Somalia': 'SOM',
'South Africa': 'ZAF',
'South Asia': 'SAS',
'South Asia (IFC classification)': 'CSA',
'South Sudan': 'SSD',
'Spain': 'ESP',
'Sri Lanka': 'LKA',
'St. Kitts and Nevis': 'KNA',
'St. Lucia': 'LCA',
'St. Martin (French part)': 'MAF',
'St. Vincent and the Grenadines': 'VCT',
'Sub-Saharan Africa (IFC classification)': 'CAA',
'Sub-Saharan Africa (all income levels)': 'SSF',
'Sub-Saharan Africa (developing only)': 'SSA',
'Sub-Saharan Africa excluding South Africa': 'SXZ',
'Sub-Saharan Africa excluding South Africa and Nigeria': 'XZN',
'Sudan': 'SDN',
'Suriname': 'SUR',
'Swaziland': 'SWZ',
'Sweden': 'SWE',
'Switzerland': 'CHE',
'Syrian Arab Republic': 'SYR',
'Tajikistan': 'TJK',
'Tanzania': 'TZA',
'Thailand': 'THA',
'Timor-Leste': 'TLS',
'Togo': 'TGO',
'Tonga': 'TON',
'Trinidad and Tobago': 'TTO',
'Tunisia': 'TUN',
'Turkey': 'TUR',
'Turkmenistan': 'TKM',
'Turks and Caicos Islands': 'TCA',
'Tuvalu': 'TUV',
'Uganda': 'UGA',
'Ukraine': 'UKR',
'United Arab Emirates': 'ARE',
'United Kingdom': 'GBR',
'United States': 'USA',
'Upper middle income': 'UMC',
'Uruguay': 'URY',
'Uzbekistan': 'UZB',
'Vanuatu': 'VUT',
'Venezuela, RB': 'VEN',
'Vietnam': 'VNM',
'Virgin Islands (U.S.)': 'VIR',
'West Bank and Gaza': 'PSE',
'World': 'WLD',
'Yemen, Rep.': 'YEM',
'Zambia': 'ZMB',
'Zimbabwe': 'ZWE'}
#1.2 Use wbdata to get lists of country codes by income groups
countriesIncomeAll = [i['id'] for i in wb.get_country(incomelevel=['LIC','MIC','HIC'],display=False)]
countriesIncomeH = [i['id'] for i in wb.get_country(incomelevel=['HIC'],display=False)]
countriesIncomeM = [i['id'] for i in wb.get_country(incomelevel=['MIC'],display=False)]
countriesIncomeL = [i['id'] for i in wb.get_country(incomelevel=['LIC'],display=False)]
countriesIncomeOecd = [i['id'] for i in wb.get_country(incomelevel="OEC", display=False)]
In [4]:
# 2. Import data from Quandl
# 2.1 Money supply (LCU)
moneyDf = pd.DataFrame({})
for name,key in countryCodes.items():
try:
df = Quandl.get('WWDI/'+key+'_FM_LBL_MONY_CN',authtoken="QqLL1AFCjc31_MVo4qsU")
df.columns = [key]
moneyDf = pd.concat([moneyDf,df],axis=1)
except:
pass
# 2.2 GDP deflator
deflatorDf = pd.DataFrame({})
for name,key in countryCodes.items():
try:
df = Quandl.get('WWDI/'+key+'_NY_GDP_DEFL_ZS',authtoken="QqLL1AFCjc31_MVo4qsU")
df.columns = [key]
deflatorDf = pd.concat([deflatorDf,df],axis=1)
except:
pass
# 2.3 Real GDP
gdpDf = pd.DataFrame({})
for name,key in countryCodes.items():
try:
df = Quandl.get('WWDI/'+key+'_NY_GDP_MKTP_KD',authtoken="QqLL1AFCjc31_MVo4qsU")
df.columns = [key]
gdpDf = pd.concat([gdpDf,df],axis=1)
except:
pass
# 2.4 Exahange rate relative to USD
exchangeDf = pd.DataFrame({})
for name,key in countryCodes.items():
try:
df = Quandl.get('WWDI/'+key+'_PA_NUS_FCRF',authtoken="QqLL1AFCjc31_MVo4qsU")
df.columns = [key]
exchangeDf = pd.concat([exchangeDf,df],axis=1)
except:
pass
# 2.5 Nominal interest rate (lending rate)
interestDf = pd.DataFrame({})
for name,key in countryCodes.items():
try:
df = Quandl.get('WWDI/'+key+'_FR_INR_LEND',authtoken="QqLL1AFCjc31_MVo4qsU")
df.columns = [key]
interestDf = pd.concat([interestDf,df],axis=1)
except:
pass
In [5]:
# 3. Create datasets for quantity theory without interest and exchange rates
# 3.1 Dataframes to use
dataFrames = [moneyDf, deflatorDf, gdpDf]
# 3.2 Identify the codes for countries with at leaset 10 years of consecutive data for each series
availableCodes = []
for code in countryCodes.values():
if all(code in frame for frame in dataFrames):
if any( all(np.isnan(x) for x in frame[code]) for frame in dataFrames):
print(code)
else:
availableCodes.append(code)
print('Number of countries: ',len(availableCodes))
# 3.3 Construct the dataset including the average growth rates of variabes for each country
includedCodes = []
obs = []
mData = []
pData = []
yData = []
for c in availableCodes:
count = 0
ind = []
for i in dataFrames[0].index[1:]:
noneNan = all( not np.isnan(frame[c].loc[i]) for frame in dataFrames)
anyNan = any( np.isnan(frame[c].loc[i]) for frame in dataFrames)
if noneNan:
count+=1
ind.append(i)
elif anyNan and count>0:
break
if count >9:
m = (moneyDf[c].loc[ind[-1]]/moneyDf[c].loc[ind[0]])**(1/count)-1
p = (deflatorDf[c].loc[ind[-1]]/deflatorDf[c].loc[ind[0]])**(1/count)-1
y = (gdpDf[c].loc[ind[-1]]/gdpDf[c].loc[ind[0]])**(1/count)-1
includedCodes.append(c)
mData.append(np.around(m,5))
pData.append(np.around(p,5))
yData.append(np.around(y,5))
obs.append(count)
# 3.3 Identify the names of the countries that are incuded
includedNames = []
for c in includedCodes:
for name, code in countryCodes.items():
if c == code:
includedNames.append(name)
# 3.4 Create the main dataframe
qtyTheoryData = pd.DataFrame({'iso code':includedCodes,'observations':obs,'inflation':pData,'money growth':mData,'gdp growth':yData},index = includedNames)
qtyTheoryData = qtyTheoryData[['iso code','observations','inflation','money growth','gdp growth']]
qtyTheoryData = qtyTheoryData.sort_index()
# 3.5 Create dataframes organized by income levels
indexL=[]
indexM=[]
indexH=[]
indexOecd=[]
for country in qtyTheoryData.index:
code = qtyTheoryData['iso code'].loc[country]
if code in countriesIncomeL:
indexL.append(country)
if code in countriesIncomeM:
indexM.append(country)
if code in countriesIncomeH:
indexH.append(country)
if code in countriesIncomeOecd:
indexOecd.append(country)
qtyTheoryDataL = qtyTheoryData.loc[indexL]
qtyTheoryDataM = qtyTheoryData.loc[indexM]
qtyTheoryDataH = qtyTheoryData.loc[indexH]
qtyTheoryDataOecd = qtyTheoryData.loc[indexOecd]
# 3.6 Export dataframes to csv
qtyTheoryData.to_csv('qtyTheoryData.csv',index=True,index_label='country')
qtyTheoryDataL.to_csv('qtyTheoryDataL.csv',index=True,index_label='country')
qtyTheoryDataM.to_csv('qtyTheoryDataM.csv',index=True,index_label='country')
qtyTheoryDataH.to_csv('qtyTheoryDataH.csv',index=True,index_label='country')
qtyTheoryDataOecd.to_csv('qtyTheoryDataOecd.csv',index=True,index_label='country')
In [6]:
# 4. Graph of quantity theory data without exchange or interest rates
# 4.1 Money growth inflation with country codes
m = qtyTheoryData['money growth']
p = qtyTheoryData['inflation']
y = qtyTheoryData['gdp growth']
codes = qtyTheoryData['iso code']
a_p, b_p, r_p, p_value, std_err = stats.linregress(m,p)
xVals = np.arange(-10,10)
p_pred = b_p + a_p*xVals
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax1.plot(p_pred,p_pred,lw=1.5,label='OLS Regression')
ax1.legend(loc='lower right',fontsize=12)
for i in range(len(codes)):
if codes[i] =='USA':
usaIndex = i
# plt.text(m[i], p[i], codes[i], color='red',fontsize=12, clip_on=True,horizontalalignment='center',verticalalignment='center',alpha = 1)
else:
plt.text(m[i], p[i], codes[i], color="#11557c",fontsize=12, clip_on=True,horizontalalignment='center',verticalalignment='center',alpha = 1)
plt.text(m[usaIndex], p[usaIndex], codes[usaIndex], color='red',fontsize=12, clip_on=True,horizontalalignment='center',verticalalignment='center',alpha = 1)
ax1.axis([-0.2, 1.4, -0.2, 1.4])
ax1.set_xlabel('money growth')
ax1.set_ylabel('inflation')
plt.grid()
plt.tight_layout()
plt.savefig('fig_moneyInflationCoded.png',bbox_inches='tight',dpi=120)
print('regression coefficient:',np.round(a_p,2))
In [7]:
# 4.2 Money growth and inflation
xmin = -0.05
xmax = 1.4
ymin = -0.05
ymax = 1.4
x45 = np.arange(xmin,xmax,0.001)
y45 = x45 - np.mean(qtyTheoryData['gdp growth'])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax1.plot(x45,y45)
ax1.plot(qtyTheoryDataH['money growth'],qtyTheoryDataH['inflation'],'bo')
ax1.plot(qtyTheoryDataM['money growth'],qtyTheoryDataM['inflation'],'gs')
ax1.plot(qtyTheoryDataL['money growth'],qtyTheoryDataL['inflation'],'r^')
plt.grid(True)
ax1.set_xlabel('money growth')
ax1.set_ylabel('inflation')
ax1.set_xlim([xmin,xmax])
ax1.set_ylim([ymin,ymax])
plt.legend(['$45^\circ$'],loc='lower right',fontsize='15')
plt.tight_layout()
plt.savefig('fig_moneyInflation.png',bbox_inches='tight',dpi=120)
# 4.3 Money growth and real gdp growth
xmin = -0.05
xmax = 1.4
ymin = -0.05
ymax = .2
x0 = np.arange(xmin,xmax,0.001)
y0 = x0*0 + np.mean(qtyTheoryData['gdp growth'])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax1.plot(x0,y0)
plt.plot(qtyTheoryDataH['money growth'],qtyTheoryDataH['gdp growth'],'bo')
plt.plot(qtyTheoryDataM['money growth'],qtyTheoryDataM['gdp growth'],'gs')
plt.plot(qtyTheoryDataL['money growth'],qtyTheoryDataL['gdp growth'],'r^')
plt.grid(True)
# ax1.set_title('Money growth and real GDP growth',fontsize=15)
ax1.set_xlabel('money growth')
ax1.set_ylabel('real GDP growth')
ax1.set_xlim([xmin,xmax])
ax1.set_ylim([ymin,ymax])
plt.legend(['$0^\circ$'],loc='lower right',fontsize='15')
plt.tight_layout()
plt.savefig('fig_moneyGDP.png',bbox_inches='tight',dpi=120)
# 4.4 Money growth and real gdp growth
xmin = -0.05
xmax = 1.4
ymin = -0.05
ymax = .2
x0 = np.arange(xmin,xmax,0.001)
y0 = x0*0 + np.mean(qtyTheoryData['gdp growth'])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax1.plot(x0,y0)
plt.plot(qtyTheoryDataH['inflation'],qtyTheoryDataH['gdp growth'],'bo')
plt.plot(qtyTheoryDataM['inflation'],qtyTheoryDataM['gdp growth'],'gs')
plt.plot(qtyTheoryDataL['inflation'],qtyTheoryDataL['gdp growth'],'r^')
plt.grid(True)
# ax1.set_title('Inflation and real GDP growth',fontsize=15)
ax1.set_xlabel('inflation')
ax1.set_ylabel('real GDP growth')
ax1.set_xlim([xmin,xmax])
ax1.set_ylim([ymin,ymax])
plt.legend(['$0^\circ$'],loc='lower right',fontsize='15')
plt.tight_layout()
plt.savefig('fig_inflationGDP.png',bbox_inches='tight',dpi=120)
In [8]:
# 5. Make tex files for figures
# 5.0 Money growth and inflation with codes
nf = open('figure_moneyInflationCoded.tex', 'w')
nf.write('\\begin{figure}[h]\n')
nf.write('\\caption{\\label{fig_moneyInflationCoded} \\textbf{Money growth and deflator inflation for '),nf.write(str(len(qtyTheoryData))),nf.write(' countries.} High-income countries: blue circles, medium-income: green squares, and low-income: red triangles. {\\tiny Source: Quandl, World Development Indicators, World Bank}}\n')
nf.write('\\hspace*{-.5cm}\\includegraphics[height = 7.cm]{fig_moneyInflationCoded.png}\n')
nf.write('\\end{figure}')
nf.close()
# 5.1 Money growth and inflation
nf = open('figure_moneyInflation.tex', 'w')
nf.write('\\begin{figure}[h]\n')
nf.write('\\caption{\\label{fig_moneyInflation} \\textbf{Money growth and deflator inflation for '),nf.write(str(len(qtyTheoryData))),nf.write(' countries.} High-income countries: blue circles, medium-income: green squares, and low-income: red triangles. {\\tiny Source: Quandl, World Development Indicators, World Bank}}\n')
nf.write('\\hspace*{-.5cm}\\includegraphics[height = 7.cm]{fig_moneyInflation.png}\n')
nf.write('\\end{figure}')
nf.close()
# 5.2 Inflation and real GDP
nf = open('figure_inflationGDP.tex', 'w')
nf.write('\\begin{figure}[h]\n')
nf.write('\\caption{\\label{fig_moneyGDP} \\textbf{Deflator inflation and real GDP growth for '),nf.write(str(len(qtyTheoryData))),nf.write(' countries.} High-income countries: blue circles, medium-income: green squares, and low-income: red triangles. {\\tiny Source: Quandl, World Development Indicators, World Bank}}\n')
nf.write('\\hspace*{-.5cm}\\includegraphics[height = 7.cm]{fig_inflationGDP.png}\n')
nf.write('\\end{figure}')
nf.close()
# 5.3 Inflation and real GDP
nf = open('figure_moneyGDP.tex', 'w')
nf.write('\\begin{figure}[h]\n')
nf.write('\\caption{\\label{fig_inflationGDP} \\textbf{Money growth and real GDP growth for '),nf.write(str(len(qtyTheoryData))),nf.write(' countries.} High-income countries: blue circles, medium-income: green squares, and low-income: red triangles. {\\tiny Source: Quandl, World Development Indicators, World Bank}}\n')
nf.write('\\hspace*{-.5cm}\\includegraphics[height = 7.cm]{fig_moneyGDP.png}\n')
nf.write('\\end{figure}')
nf.close()
In [9]:
# 6. Correlations
print(qtyTheoryData[['money growth','inflation','gdp growth']].corr())
print(qtyTheoryDataH[['money growth','inflation','gdp growth']].corr())
print(qtyTheoryDataM[['money growth','inflation','gdp growth']].corr())
print(qtyTheoryDataL[['money growth','inflation','gdp growth']].corr())
print(qtyTheoryDataOecd[['money growth','inflation','gdp growth']].corr())
In [10]:
# 7. Create datasets for quantity theory with interest and exchange rates
# 7.1 Dataframes to use
dataFrames = [moneyDf, deflatorDf, gdpDf,interestDf,exchangeDf]
# 7.2 Identify the codes for countries with at leaset 10 years of consecutive data for each series
availableCodes = []
for code in countryCodes.values():
if all(code in frame for frame in dataFrames):
if any( all(np.isnan(x) for x in frame[code]) for frame in dataFrames):
print(code)
else:
availableCodes.append(code)
print('Number of countries: ',len(availableCodes))
# 7.3 Construct the dataset including the average growth rates of variabes for each country
includedCodes = []
obs = []
mData = []
pData = []
yData = []
iData = []
eData = []
for c in availableCodes:
count = 0
ind = []
for i in dataFrames[0].index[1:]:
noneNan = all( not np.isnan(frame[c].loc[i]) for frame in dataFrames)
anyNan = any( np.isnan(frame[c].loc[i]) for frame in dataFrames)
if noneNan:
count+=1
ind.append(i)
elif anyNan and count>0:
break
if count >9:
m = (moneyDf[c].loc[ind[-1]]/moneyDf[c].loc[ind[0]])**(1/count)-1
p = (deflatorDf[c].loc[ind[-1]]/deflatorDf[c].loc[ind[0]])**(1/count)-1
y = (gdpDf[c].loc[ind[-1]]/gdpDf[c].loc[ind[0]])**(1/count)-1
e = (exchangeDf[c].loc[ind[-1]]/exchangeDf[c].loc[ind[0]])**(1/count)-1
rate = np.mean(interestDf[c].iloc[1:])/100
includedCodes.append(c)
mData.append(np.around(m,5))
pData.append(np.around(p,5))
yData.append(np.around(y,5))
eData.append(np.around(e,5))
iData.append(np.around(rate,5))
obs.append(count)
# 7.3 Identify the names of the countries that are incuded
includedNames = []
for c in includedCodes:
for name, code in countryCodes.items():
if c == code:
includedNames.append(name)
# 7.4 Create the main dataframe
qtyTheoryData = pd.DataFrame({'iso code':includedCodes,'observations':obs,'inflation':pData,'money growth':mData,'gdp growth':yData,'nominal interest rate':iData,'exchange rate depreciation':eData},index = includedNames)
qtyTheoryData = qtyTheoryData[['iso code','observations','inflation','money growth','gdp growth','nominal interest rate','exchange rate depreciation']]
qtyTheoryData = qtyTheoryData.sort_index()
# 7.5 Create dataframes organized by income levels
indexL=[]
indexM=[]
indexH=[]
indexOecd=[]
for country in qtyTheoryData.index:
code = qtyTheoryData['iso code'].loc[country]
if code in countriesIncomeL:
indexL.append(country)
if code in countriesIncomeM:
indexM.append(country)
if code in countriesIncomeH:
indexH.append(country)
if code in countriesIncomeOecd:
indexOecd.append(country)
qtyTheoryDataL = qtyTheoryData.loc[indexL]
qtyTheoryDataM = qtyTheoryData.loc[indexM]
qtyTheoryDataH = qtyTheoryData.loc[indexH]
qtyTheoryDataOecd = qtyTheoryData.loc[indexOecd]
# 7.6 Export dataframes to csv
qtyTheoryData.to_csv('qtyTheoryOpenData.csv',index=True,index_label='country')
qtyTheoryDataL.to_csv('qtyTheoryOpenDataL.csv',index=True,index_label='country')
qtyTheoryDataM.to_csv('qtyTheoryOpenDataM.csv',index=True,index_label='country')
qtyTheoryDataH.to_csv('qtyTheoryOpenDataH.csv',index=True,index_label='country')
qtyTheoryDataOecd.to_csv('qtyTheoryOpenDataOecd.csv',index=True,index_label='country')
In [11]:
# 8.1 Money growth and inflation
xmin = -0.05
xmax = 1.4
ymin = -0.05
ymax = 1.4
x45 = np.arange(xmin,xmax,0.001)
y45 = x45 - np.mean(qtyTheoryData['gdp growth'])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax1.plot(x45,y45)
ax1.plot(qtyTheoryDataH['money growth'],qtyTheoryDataH['inflation'],'bo')
ax1.plot(qtyTheoryDataM['money growth'],qtyTheoryDataM['inflation'],'gs')
ax1.plot(qtyTheoryDataL['money growth'],qtyTheoryDataL['inflation'],'r^')
plt.grid(True)
ax1.set_xlabel('money growth')
ax1.set_ylabel('inflation')
ax1.set_xlim([xmin,xmax])
ax1.set_ylim([ymin,ymax])
plt.legend(['$45^\circ$'],loc='lower right',fontsize='15')
plt.tight_layout()
plt.savefig('fig_moneyInflationOpen.png',bbox_inches='tight',dpi=120)
# 8.2 Money growth and real gdp growth
xmin = -0.05
xmax = 1.4
ymin = -0.05
ymax = .2
x0 = np.arange(xmin,xmax,0.001)
y0 = x0*0 + np.mean(qtyTheoryData['gdp growth'])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax1.plot(x0,y0)
plt.plot(qtyTheoryDataH['money growth'],qtyTheoryDataH['gdp growth'],'bo')
plt.plot(qtyTheoryDataM['money growth'],qtyTheoryDataM['gdp growth'],'gs')
plt.plot(qtyTheoryDataL['money growth'],qtyTheoryDataL['gdp growth'],'r^')
plt.grid(True)
# ax1.set_title('Money growth and real GDP growth',fontsize=15)
ax1.set_xlabel('money growth')
ax1.set_ylabel('real GDP growth')
ax1.set_xlim([xmin,xmax])
ax1.set_ylim([ymin,ymax])
plt.legend(['$0^\circ$'],loc='lower right',fontsize='15')
plt.tight_layout()
plt.savefig('fig_moneyGDPOpen.png',bbox_inches='tight',dpi=120)
# 8.3 Money growth and real gdp growth
xmin = -0.05
xmax = 1.4
ymin = -0.05
ymax = .2
x0 = np.arange(xmin,xmax,0.001)
y0 = x0*0 + np.mean(qtyTheoryData['gdp growth'])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax1.plot(x0,y0)
plt.plot(qtyTheoryDataH['inflation'],qtyTheoryDataH['gdp growth'],'bo')
plt.plot(qtyTheoryDataM['inflation'],qtyTheoryDataM['gdp growth'],'gs')
plt.plot(qtyTheoryDataL['inflation'],qtyTheoryDataL['gdp growth'],'r^')
plt.grid(True)
# ax1.set_title('Inflation and real GDP growth',fontsize=15)
ax1.set_xlabel('inflation')
ax1.set_ylabel('real GDP growth')
ax1.set_xlim([xmin,xmax])
ax1.set_ylim([ymin,ymax])
plt.legend(['$0^\circ$'],loc='lower right',fontsize='15')
plt.tight_layout()
plt.savefig('fig_inflationGDPOpen.png',bbox_inches='tight',dpi=120)
# 8.4 Money growth differential depreciation
xmin = -0.2
xmax = 1.4
ymin = -0.2
ymax = 1.4
x45 = np.arange(xmin,xmax,0.001)
y45 = x45 - np.mean(qtyTheoryData['gdp growth'])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax1.plot(x45,y45)
ax1.plot(qtyTheoryDataH['inflation']- qtyTheoryData['inflation'].loc['United States'],qtyTheoryDataH['nominal interest rate']- qtyTheoryData['nominal interest rate'].loc['United States'],'bo')
ax1.plot(qtyTheoryDataM['inflation']- qtyTheoryData['inflation'].loc['United States'],qtyTheoryDataM['nominal interest rate']- qtyTheoryData['nominal interest rate'].loc['United States'],'gs')
ax1.plot(qtyTheoryDataL['inflation']- qtyTheoryData['inflation'].loc['United States'],qtyTheoryDataL['nominal interest rate']- qtyTheoryData['nominal interest rate'].loc['United States'],'r^')
plt.grid(True)
ax1.set_xlabel('inflation differential')
ax1.set_ylabel('interest differential')
ax1.set_xlim([xmin,xmax])
ax1.set_ylim([ymin,ymax])
plt.legend(['$45^\circ$'],loc='lower right',fontsize='15')
plt.tight_layout()
plt.savefig('fig_inflationInterestDifferentialsOpen.png',bbox_inches='tight',dpi=120)
# 8.5 Money growth differential depreciation
xmin = -0.2
xmax = 1.4
ymin = -0.2
ymax = 1.4
x45 = np.arange(xmin,xmax,0.001)
y45 = x45 - np.mean(qtyTheoryData['gdp growth'])
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
ax1.plot(x45,y45)
ax1.plot(qtyTheoryDataH['money growth']- qtyTheoryData['money growth'].loc['United States'],qtyTheoryDataH['exchange rate depreciation'],'bo')
ax1.plot(qtyTheoryDataM['money growth']- qtyTheoryData['money growth'].loc['United States'],qtyTheoryDataM['exchange rate depreciation'],'gs')
ax1.plot(qtyTheoryDataL['money growth']- qtyTheoryData['money growth'].loc['United States'],qtyTheoryDataL['exchange rate depreciation'],'r^')
plt.grid(True)
ax1.set_xlabel('money growth differential')
ax1.set_ylabel('depreciation')
ax1.set_xlim([xmin,xmax])
ax1.set_ylim([ymin,ymax])
plt.legend(['$45^\circ$'],loc='lower right',fontsize='15')
plt.tight_layout()
plt.savefig('fig_moneyDifferentialDepreciationOpen.png',bbox_inches='tight',dpi=120)
In [12]:
# 9. Make tex files for figures
# 9.1 Money growth and inflation
nf = open('figure_moneyInflationOpen.tex', 'w')
nf.write('\\begin{figure}[h]\n')
nf.write('\\caption{\\label{fig_moneyInflationOpen} \\textbf{Money growth and deflator inflation for '),nf.write(str(len(qtyTheoryData))),nf.write(' countries.} High-income countries: blue circles, medium-income: green squares, and low-income: red triangles. {\\tiny Source: Quandl, World Development Indicators, World Bank}}\n')
nf.write('\\hspace*{-.5cm}\\includegraphics[height = 7.cm]{fig_moneyInflationOpen.png}\n')
nf.write('\\end{figure}')
nf.close()
# 9.2 Inflation and real GDP
nf = open('figure_inflationGDPOpen.tex', 'w')
nf.write('\\begin{figure}[h]\n')
nf.write('\\caption{\\label{fig_moneyGDPOpen} \\textbf{Deflator inflation and real GDP growth for '),nf.write(str(len(qtyTheoryData))),nf.write(' countries.} High-income countries: blue circles, medium-income: green squares, and low-income: red triangles. {\\tiny Source: Quandl, World Development Indicators, World Bank}}\n')
nf.write('\\hspace*{-.5cm}\\includegraphics[height = 7.cm]{fig_inflationGDPOpen.png}\n')
nf.write('\\end{figure}')
nf.close()
# 9.3 Inflation and real GDP
nf = open('figure_moneyGDPOpen.tex', 'w')
nf.write('\\begin{figure}[h]\n')
nf.write('\\caption{\\label{fig_inflationGDPOpen} \\textbf{Money growth and real GDP growth for '),nf.write(str(len(qtyTheoryData))),nf.write(' countries.} High-income countries: blue circles, medium-income: green squares, and low-income: red triangles. {\\tiny Source: Quandl, World Development Indicators, World Bank}}\n')
nf.write('\\hspace*{-.5cm}\\includegraphics[height = 7.cm]{fig_moneyGDPOpen.png}\n')
nf.write('\\end{figure}')
nf.close()
# 9.4 Inflation and interest rate differentials
nf = open('figure_inflationInterestDifferentialsOpen.tex', 'w')
nf.write('\\begin{figure}[h]\n')
nf.write('\\caption{\\label{fig_inflationInterestDifferentialsOpen} \\textbf{Interest and inflation relative to US for '),nf.write(str(len(qtyTheoryData))),nf.write(' countries.} High-income countries: blue circles, medium-income: green squares, and low-income: red triangles. {\\tiny Source: Quandl, World Development Indicators, World Bank}}\n')
nf.write('\\hspace*{-.5cm}\\includegraphics[height = 7.cm]{fig_inflationInterestDifferentialsOpen.png}\n')
nf.write('\\end{figure}')
nf.close()
# 9.5 Money growth and exchange rate depreciation
nf = open('figure_moneyDifferentialDepreciationOpen.tex', 'w')
nf.write('\\begin{figure}[h]\n')
nf.write('\\caption{\\label{fig_moneyDifferentialDepreciationOpen} \\textbf{Money growth and depreciation for '),nf.write(str(len(qtyTheoryData))),nf.write(' countries.} High-income countries: blue circles, medium-income: green squares, and low-income: red triangles. {\\tiny Source: Quandl, World Development Indicators, World Bank}}\n')
nf.write('\\hspace*{-.5cm}\\includegraphics[height = 7.cm]{fig_moneyDifferentialDepreciationOpen.png}\n')
nf.write('\\end{figure}')
nf.close()
In [13]:
# 10. Correlations
print(qtyTheoryData[['money growth','inflation','gdp growth','nominal interest rate','exchange rate depreciation']].corr())
print(qtyTheoryDataH[['money growth','inflation','gdp growth','nominal interest rate','exchange rate depreciation']].corr())
print(qtyTheoryDataM[['money growth','inflation','gdp growth','nominal interest rate','exchange rate depreciation']].corr())
print(qtyTheoryDataL[['money growth','inflation','gdp growth','nominal interest rate','exchange rate depreciation']].corr())
print(qtyTheoryDataOecd[['money growth','inflation','gdp growth','nominal interest rate','exchange rate depreciation']].corr())
In [14]:
# 11. Export notebook to python script
runProcs.tex('qtyTheorySlides.tex')
runProcs.exportNb('quantityTheory')