import pandas as pd
df = pd.read_csv('bdm2s2_nation_year_data_may2002.csv')
df = df[df['year']>1970]
print list(df.columns)['Unnamed: 0', 'year', 'AGEA', 'AGEI', 'AUT', 'BRITCOL', 'CATH', 'COMEX', 'EDT', 'EDTG', 'ELF60', 'ETHNIC', 'GINI', 'GXPDEDUC', 'GXPDHLTH', 'GXPDSSEC', 'GXPKTOTL', 'INEQ', 'INST', 'LEGSELEC', 'MOSLEM', 'OIL', 'PROT', 'REGION', 'REGION2', 'RELIGION', 'RIOTS', 'STRIKES', 'UNSTABLE', 'WAR', 'ccode', 'TAXGDP', 'TAXGNP', 'TREV', 'PRIVCON', 'INCOMEY', 'INCOMET', 'DIRT', 'INDIRT', 'democ', 'autoc', 'polity', 'polity2', 'durable', 'xrreg', 'xrcomp', 'xropen', 'xconst', 'parreg', 'parcomp', 'exrec', 'exconst', 'polcomp', 'bdate', 'bprec', 'change', 'd3', 'sf', 'regtrans', 'country', 'BANKScode', 'Radios', 'Newspapers', 'Assinations', 'GenStrikes', 'GovCrises', 'Purges', 'Riots', 'Revolutions', 'AntiGovDem', 'ConflictIndex', 'RegimeType', 'Coups', 'HeadofState', 'Guerilla', 'RegisteredVoters', 'MajConstChg', 'Premier', 'Legselec', 'NatGovRevenue', 'NatGovExpend', 'defenseovertotal', 'name', 'chcode', 'longname', 'coding_differs', 'WorldBankCode', 'WorldBankName', 'fatlev1', 'hostlev1', 'legselec', 'esolow', 'bhkavg', 'exchprem', 'sh_staa', 'sh_med_b', 'sh_med_p', 'sh_h2o_s', 'immig', 'emig', 'ID', 'NAME', 'PR', 'CL', 'cap', 'milper', 'milex', 'energy', 'irst', 'majpow', 'warnoEX', 'EXdeaths', 'EXintside', 'EXinitiate', 'sysstat', 'EX_war_start', 'EXduration', 'EX_WAR', 'number_of_EXwars', 'biggestEXwar', 'warno', 'duration', 'deaths', 'outcome', 'initiate', 'is_war_start', 'IS_WAR', 'number_of_ISwars', 'biggestISwar', 'warnoCL', 'CLdeaths', 'CLintside', 'CLinitiate', 'CL_war_start', 'CLduration', 'CL_WAR', 'number_of_CLwars', 'biggestCLwar', 'ai', 'sd', 'ainew', 'sdnew', 'idgurr', 'worldbankcode', 'WB_pop', 'WB_gdppc_constUS95', 'WB_growth', 'WB_gdp_curUS', 'WB_gdp95', 'Aid_pcUS', 'FemSec', 'Doctors', 'MilPerson', 'MilEx', 'MilExGNI', 'InfMort', 'LowBirth', 'LifeExp', 'Labor_PrimEd', 'ImmuneMEASLES', 'ImmuneDPT', 'Illiteracy', 'Beds', 'HealthEXP', 'HealthExpPPP', 'GrossNatSaving', 'GrossDomSaving', 'Capital_formation', 'Saving_dom', 'FDI', 'Foreign_Finnace', 'ConsumptionSpending', 'Expenditure', 'Deathrate', 'newspaper', 'HealthExpend', 'EducExpend', 'CapitalExpend_tot', 'CurRevenue', 'GrossDomInvest', 'GovDebt', 'Birthrate', 'total_aid', 'aid_cap_constUS', 'aid_gdp', 'CapitalExpend', 'pop', 'rgdpch', 'rgdpl', 'c', 'i', 'g', 'rgdptt', 'y', 'cgdp', 'cc', 'ci', 'cg', 'p', 'pc', 'pi', 'pg', 'xr', 'rgdpea', 'rgdpw', 'kapw', 'kdur', 'knres', 'kother', 'kres', 'ktranp', 'open', 'rgnp', 'ipri', 'stliv', 'finittrm', 'yrcurnt', 'military', 'auton', 'eda', 'strikes', 'nspolpr', 'agovdem', 'govtcris', 'corrupti80_89', 'purges', 'coups', 'riots', 'constchg', 'cabchg', 'genocide', 'worldbankname', 'NatYr', 'cowreg', 'regyr', 'demaut', 'lpop', 'lpop_WB', 's', 'S', 'W', 'W_extend', 'W_mod', 'polityW', 'polityWn', 'Parl_Pres', 'w2', 'demaut2', 'WoverS', 'dbigw', 'dW', 'dw30', 'lagW', 'lagW2', 'dwos30', 'grow', 'LongGR1', 'LongGR2', 'LongGR3', 'LongGR4', 'lagGrow', 'lrgdpc', 'laglrgdpc', 'lgdpWB', 'laglgdpWB', 'VarGrow', 'VarGrow_', 'build', 'bal_bud', 'VarBud', 'Var_bud', 'Klepto', 'nexcap']
df['Surplus'] = df['CurRevenue']-df['Expenditure']
df['Surplusl1'] = df['Surplus'].shift(1)
df['Surplusl2'] = df['Surplus'].shift(2)
df['Surplusl3'] = df['Surplus'].shift(3)
df['Surplusl4'] = df['Surplus'].shift(4)
df3 = df[['year','WB_growth','Surplus','Surplusl1','Surplusl2','Surplusl3','Surplusl4']]
df3 = df3.dropna()
print len(df3)
import statsmodels.formula.api as smf
results = smf.ols('WB_growth ~ Surplus + Surplusl2 + Surplusl3' , data=df3).fit()
print results.summary()1991
OLS Regression Results
==============================================================================
Dep. Variable: WB_growth R-squared: 0.033
Model: OLS Adj. R-squared: 0.031
Method: Least Squares F-statistic: 22.35
Date: Sun, 19 Jul 2015 Prob (F-statistic): 3.13e-14
Time: 00:02:52 Log-Likelihood: -5936.4
No. Observations: 1991 AIC: 1.188e+04
Df Residuals: 1987 BIC: 1.190e+04
Df Model: 3
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [95.0% Conf. Int.]
------------------------------------------------------------------------------
Intercept 3.7920 0.128 29.651 0.000 3.541 4.043
Surplus 0.1652 0.023 7.200 0.000 0.120 0.210
Surplusl2 -0.1275 0.019 -6.652 0.000 -0.165 -0.090
Surplusl3 0.0171 0.018 0.964 0.335 -0.018 0.052
==============================================================================
Omnibus: 193.751 Durbin-Watson: 1.416
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1342.622
Skew: -0.118 Prob(JB): 2.84e-292
Kurtosis: 7.016 Cond. No. 16.6
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
print df3 year WB_growth Surplus Surplusl1 Surplusl2 Surplusl3
175 1975 -0.356611 -2.564596 -0.194693 -1.083765 -1.426472
176 1976 5.562603 -3.547331 -2.564596 -0.194693 -1.083765
177 1977 4.640539 -2.136003 -3.547331 -2.564596 -0.194693
178 1978 5.513810 -1.809309 -2.136003 -3.547331 -2.564596
179 1979 3.182480 -0.693287 -1.809309 -2.136003 -3.547331
180 1980 -0.227902 -1.831499 -0.693287 -1.809309 -2.136003
181 1981 2.449727 -1.540344 -1.831499 -0.693287 -1.809309
182 1982 -2.021596 -3.260855 -1.540344 -1.831499 -0.693287
183 1983 4.329519 -5.404911 -3.260855 -1.540344 -1.831499
184 1984 7.260786 -4.198532 -5.404911 -3.260855 -1.540344
185 1985 3.848768 -4.451365 -4.198532 -5.404911 -3.260855
186 1986 3.418787 -4.746815 -4.451365 -4.198532 -5.404911
187 1987 3.397920 -3.070293 -4.746815 -4.451365 -4.198532
188 1988 4.171592 -3.086586 -3.070293 -4.746815 -4.451365
189 1989 3.509137 -2.721657 -3.086586 -3.070293 -4.746815
190 1990 1.761598 -3.806427 -2.721657 -3.086586 -3.070293
191 1991 -0.470322 -5.269194 -3.806427 -2.721657 -3.086586
192 1992 3.051290 -4.686991 -5.269194 -3.806427 -2.721657
193 1993 2.654793 -3.854988 -4.686991 -5.269194 -3.806427
194 1994 4.034653 -2.911644 -3.854988 -4.686991 -5.269194
195 1995 2.668278 -2.167093 -2.911644 -3.854988 -4.686991
196 1996 3.570261 -1.446373 -2.167093 -2.911644 -3.854988
197 1997 4.434240 -0.378687 -1.446373 -2.167093 -2.911644
198 1998 4.364516 0.741217 -0.378687 -1.446373 -2.167093
199 1999 3.600000 1.330404 0.741217 -0.378687 -1.446373
363 1977 3.361815 -3.123297 -0.997124 -0.650976 1.012888
364 1978 4.046591 -3.666927 -3.123297 -0.997124 -0.650976
365 1979 4.156019 -2.649281 -3.666927 -3.123297 -0.997124
366 1980 1.338302 -2.582191 -2.649281 -3.666927 -3.123297
367 1981 3.105074 -1.087029 -2.582191 -2.649281 -3.666927
... ... ... ... ... ... ...
19209 1985 0.848111 -8.796961 -4.055031 -11.018259 -10.563047
19210 1986 19.801224 -15.493069 -8.796961 -4.055031 -11.018259
19211 1987 2.425016 -17.109423 -15.493069 -8.796961 -4.055031
19212 1988 3.613707 -12.611235 -17.109423 -15.493069 -8.796961
19213 1989 8.117859 -5.914122 -12.611235 -17.109423 -15.493069
19214 1990 1.779755 -8.655430 -5.914122 -12.611235 -17.109423
19215 1991 3.585242 -15.843773 -8.655430 -5.914122 -12.611235
19319 1974 2.601156 -3.900000 -4.156075 -1.909681 -3.973572
19320 1975 0.530207 -1.376245 -3.900000 -4.156075 -1.909681
19321 1976 2.736164 -3.887733 -1.376245 -3.900000 -4.156075
19322 1977 5.904011 -5.524923 -3.887733 -1.376245 -3.900000
19323 1978 1.863400 -4.719452 -5.524923 -3.887733 -1.376245
19324 1979 12.224821 -3.622389 -4.719452 -5.524923 -3.887733
19325 1980 -1.603459 -3.549862 -3.622389 -4.719452 -5.524923
19326 1981 6.329993 -3.510698 -3.549862 -3.622389 -4.719452
19327 1982 -5.978491 -6.213959 -3.510698 -3.549862 -3.622389
19328 1983 -4.297454 -4.011557 -6.213959 -3.510698 -3.549862
19329 1984 8.502238 -3.684830 -4.011557 -6.213959 -3.510698
19330 1985 -4.008784 -3.231520 -3.684830 -4.011557 -6.213959
19331 1986 7.744247 -3.609173 -3.231520 -3.684830 -4.011557
19332 1987 -6.610626 -4.976286 -3.609173 -3.231520 -3.684830
19333 1988 0.953674 -1.862093 -4.976286 -3.609173 -3.231520
19334 1989 13.544423 -1.614185 -1.862093 -4.976286 -3.609173
19335 1990 2.600752 0.404890 -1.614185 -1.862093 -4.976286
19336 1991 -0.298523 -3.597425 0.404890 -1.614185 -1.862093
19337 1992 4.895730 -3.068319 -3.597425 0.404890 -1.614185
19338 1993 2.203415 -4.655903 -3.068319 -3.597425 0.404890
19339 1994 3.900240 -3.658665 -4.655903 -3.068319 -3.597425
19340 1995 1.400616 -3.551815 -3.658665 -4.655903 -3.068319
19341 1996 3.400000 -5.092405 -3.551815 -3.658665 -4.655903
[2153 rows x 6 columns]