Chapter 10

Example-10-11.R

library(dynlm);library(lmtest)
data(barium, package='wooldridge')

# Define monthly time series beginning in Feb. 1978
tsdata <- ts(barium, start=c(1978,2), frequency=12)

res <- dynlm(log(chnimp) ~ log(chempi)+log(gas)+log(rtwex)+befile6+
                          affile6+afdec6+ season(tsdata) , data=tsdata )
coeftest(res)

Example-10-2.R

data(intdef, package='wooldridge')

# Linear regression of static model:
summary( lm(i3~inf+def,data=intdef)  )

Example-10-4-contd.R

# Calculating the LRP
b<-coef(res)
b["pe"]+b["L(pe)"]+b["L(pe, 2)"]

# F test. H0: LRP=0
linearHypothesis(res,"pe + L(pe) + L(pe, 2) = 0")

Example-10-4.R

# Libraries for dynamic lm, regression table and F tests
library(dynlm);library(lmtest);library(car)
data(fertil3, package='wooldridge')

# Define Yearly time series beginning in 1913
tsdata <- ts(fertil3, start=1913)

# Linear regression of model with lags:
res <- dynlm(gfr ~ pe + L(pe) + L(pe,2) + ww2 + pill, data=tsdata)
coeftest(res)

# F test. H0: all pe coefficients are=0
linearHypothesis(res, matchCoefs(res,"pe"))

Example-10-7.R

library(dynlm);library(stargazer)
data(hseinv, package='wooldridge')

# Define Yearly time series beginning in 1947
tsdata <- ts(hseinv, start=1947)

# Linear regression of model with lags:
res1 <- dynlm(log(invpc) ~ log(price)                , data=tsdata)
res2 <- dynlm(log(invpc) ~ log(price) + trend(tsdata), data=tsdata)

# Pretty regression table
stargazer(res1,res2, type="text")

Example-Barium.R

data(barium, package='wooldridge')

# Imports from China: Variable "chnimp" from data frame "data"
# Monthly time series starting Feb. 1978
impts <- ts(barium$chnimp, start=c(1978,2), frequency=12)

# plot time series
plot(impts)

Example-quantmod.R

library(quantmod)
# Which Yahoo Finance symbols? 
# See http://finance.yahoo.com/lookup:
# "F" = Ford Motor Company

# Download data
getSymbols("F", auto.assign=TRUE)

# first and last 6 rows of resulting data frame:
head(F)
tail(F)

# Time series plot of adjusted closing prices:
plot(F$F.Adjusted, las=2)

Example-zoo.R

data(intdef, package='wooldridge')

# Variable "year" as the time measure:
intdef$year

# define "zoo" object containing all data, time measure=year:
library(zoo)
zoodata <- zoo(intdef, order.by=intdef$year)

# Time series plot of inflation
plot(zoodata$i3)

Example-10-11.py

import wooldridge as woo
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf

barium = woo.dataWoo('barium')

# linear regression with seasonal effects:
reg = smf.ols(formula='np.log(chnimp) ~ np.log(chempi) + np.log(gas) +'
                      'np.log(rtwex) + befile6 + affile6 + afdec6 +'
                      'feb + mar + apr + may + jun + jul +'
                      'aug + sep + oct + nov + dec',
              data=barium)
results = reg.fit()

# print regression table:
table = pd.DataFrame({'b': round(results.params, 4),
                      'se': round(results.bse, 4),
                      't': round(results.tvalues, 4),
                      'pval': round(results.pvalues, 4)})
print(f'table: \n{table}\n')

Example-10-2.py

import wooldridge as woo
import pandas as pd
import statsmodels.formula.api as smf

intdef = woo.dataWoo('intdef')

# linear regression of static model (Q function avoids conflicts with keywords):
reg = smf.ols(formula='i3 ~ Q("inf") + Q("def")', data=intdef)
results = reg.fit()

# print regression table:
table = pd.DataFrame({'b': round(results.params, 4),
                      'se': round(results.bse, 4),
                      't': round(results.tvalues, 4),
                      'pval': round(results.pvalues, 4)})
print(f'table: \n{table}\n')

Example-10-4-cont.py

import wooldridge as woo
import pandas as pd
import statsmodels.formula.api as smf

fertil3 = woo.dataWoo('fertil3')
T = len(fertil3)

# define yearly time series beginning in 1913:
fertil3.index = pd.date_range(start='1913', periods=T, freq='Y').year

# add all lags of 'pe' up to order 2:
fertil3['pe_lag1'] = fertil3['pe'].shift(1)
fertil3['pe_lag2'] = fertil3['pe'].shift(2)

# linear regression of model with lags:
reg = smf.ols(formula='gfr ~ pe + pe_lag1 + pe_lag2 + ww2 + pill', data=fertil3)
results = reg.fit()

# F test (H0: all pe coefficients are=0):
hypotheses1 = ['pe = 0', 'pe_lag1 = 0', 'pe_lag2 = 0']
ftest1 = results.f_test(hypotheses1)
fstat1 = ftest1.statistic[0][0]
fpval1 = ftest1.pvalue

print(f'fstat1: {fstat1}\n')
print(f'fpval1: {fpval1}\n')

# calculating the LRP:
b = results.params
b_pe_tot = b['pe'] + b['pe_lag1'] + b['pe_lag2']
print(f'b_pe_tot: {b_pe_tot}\n')

# F test (H0: LRP=0):
hypotheses2 = ['pe + pe_lag1 + pe_lag2 = 0']
ftest2 = results.f_test(hypotheses2)
fstat2 = ftest2.statistic[0][0]
fpval2 = ftest2.pvalue

print(f'fstat2: {fstat2}\n')
print(f'fpval2: {fpval2}\n')

Example-10-4.py

import wooldridge as woo
import pandas as pd
import statsmodels.formula.api as smf

fertil3 = woo.dataWoo('fertil3')
T = len(fertil3)

# define yearly time series beginning in 1913:
fertil3.index = pd.date_range(start='1913', periods=T, freq='Y').year

# add all lags of 'pe' up to order 2:
fertil3['pe_lag1'] = fertil3['pe'].shift(1)
fertil3['pe_lag2'] = fertil3['pe'].shift(2)

# linear regression of model with lags:
reg = smf.ols(formula='gfr ~ pe + pe_lag1 + pe_lag2 + ww2 + pill', data=fertil3)
results = reg.fit()

# print regression table:
table = pd.DataFrame({'b': round(results.params, 4),
                      'se': round(results.bse, 4),
                      't': round(results.tvalues, 4),
                      'pval': round(results.pvalues, 4)})
print(f'table: \n{table}\n')

Example-10-7.py

import wooldridge as woo
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf

hseinv = woo.dataWoo('hseinv')

# linear regression without time trend:
reg_wot = smf.ols(formula='np.log(invpc) ~ np.log(price)', data=hseinv)
results_wot = reg_wot.fit()

# print regression table:
table_wot = pd.DataFrame({'b': round(results_wot.params, 4),
                          'se': round(results_wot.bse, 4),
                          't': round(results_wot.tvalues, 4),
                          'pval': round(results_wot.pvalues, 4)})
print(f'table_wot: \n{table_wot}\n')

# linear regression with time trend (data set includes a time variable t):
reg_wt = smf.ols(formula='np.log(invpc) ~ np.log(price) + t', data=hseinv)
results_wt = reg_wt.fit()

# print regression table:
table_wt = pd.DataFrame({'b': round(results_wt.params, 4),
                         'se': round(results_wt.bse, 4),
                         't': round(results_wt.tvalues, 4),
                         'pval': round(results_wt.pvalues, 4)})
print(f'table_wt: \n{table_wt}\n')

Example-Barium.py

import wooldridge as woo
import pandas as pd
import matplotlib.pyplot as plt

barium = woo.dataWoo('barium')
T = len(barium)

# monthly time series starting Feb. 1978:
barium.index = pd.date_range(start='1978-02', periods=T, freq='M')
print(f'barium["chnimp"].head(): \n{barium["chnimp"].head()}\n')

# plot chnimp (default: index on the x-axis):
plt.plot('chnimp', data=barium, color='black', linestyle='-')
plt.ylabel('chnimp')
plt.xlabel('time')
plt.savefig('PyGraphs/Example-Barium.pdf')

Example-StockData.py

import pandas_datareader as pdr
import matplotlib.pyplot as plt

# download data for 'F' (= Ford Motor Company) and define start and end:
tickers = ['F']
start_date = '2014-01-01'
end_date = '2015-12-31'

# use pandas_datareader for the import:
F_data = pdr.data.DataReader(tickers, 'yahoo', start_date, end_date)

# look at imported data:
print(f'F_data.head(): \n{F_data.head()}\n')
print(f'F_data.tail(): \n{F_data.tail()}\n')

# time series plot of adjusted closing prices:
plt.plot('Close', data=F_data, color='black', linestyle='-')
plt.ylabel('Ford Close Price')
plt.xlabel('time')
plt.savefig('PyGraphs/Example-StockData.pdf')

Example-10-11.jl

using WooldridgeDatasets, GLM, DataFrames

barium = DataFrame(wooldridge("barium"))

# linear regression with seasonal effects:
reg = lm(@formula(log(chnimp) ~ log(chempi) + log(gas) +
                                log(rtwex) + befile6 + affile6 + afdec6 +
                                feb + mar + apr + may + jun + jul +
                                aug + sep + oct + nov + dec), barium)
table_reg = coeftable(reg)
println("table_reg: \n$table_reg")

Example-10-2.jl

using WooldridgeDatasets, GLM, DataFrames

intdef = DataFrame(wooldridge("intdef"))

# linear regression of static model:
reg = lm(@formula(i3 ~ inf + def), intdef)
table_reg = coeftable(reg)
println("table_reg: \n$table_reg")

Example-10-4-cont.jl

using WooldridgeDatasets, GLM, DataFrames, Distributions

fertil3 = DataFrame(wooldridge("fertil3"))

# add all lags of pe up to order 2:
fertil3.pe_lag1 = lag(fertil3.pe, 1)
fertil3.pe_lag2 = lag(fertil3.pe, 2)

# handle missings due to lagged data manually (important for ftest):
fertil3 = fertil3[Not([1, 2]), :]

# linear regression of model with lags:
reg_ur = lm(@formula(gfr ~ pe + pe_lag1 + pe_lag2 + ww2 + pill), fertil3)

# F test (H0: all pe coefficients are zero):
reg_r = lm(@formula(gfr ~ ww2 + pill), fertil3)
ftest_res = ftest(reg_r.model, reg_ur.model)
fstat = ftest_res.fstat[2]
fpval = ftest_res.pval[2]
println("fstat = $fstat\n")
println("fpval = $fpval\n")

# calculating the LRP:
b_pe_tot = sum(coef(reg_ur)[[2, 3, 4]])
println("b_pe_tot = $b_pe_tot\n")

# testing LRP=0:
fertil3.ptm1pt = fertil3.pe_lag1 - fertil3.pe
fertil3.ptm2pt = fertil3.pe_lag2 - fertil3.pe
reg_LRP = lm(@formula(gfr ~ pe + ptm1pt + ptm2pt + ww2 + pill), fertil3)
table_res_LRP = coeftable(reg_LRP)
println("table_res_LRP: \n$table_res_LRP")

Example-10-4.jl

using WooldridgeDatasets, GLM, DataFrames

fertil3 = DataFrame(wooldridge("fertil3"))

# add all lags of pe up to order 2:
fertil3.pe_lag1 = lag(fertil3.pe, 1)
fertil3.pe_lag2 = lag(fertil3.pe, 2)

# linear regression of model with lags:
reg = lm(@formula(gfr ~ pe + pe_lag1 + pe_lag2 + ww2 + pill), fertil3)
table_reg = coeftable(reg)
println("table_reg: \n$table_reg")

Example-10-7.jl

using WooldridgeDatasets, GLM, DataFrames

hseinv = DataFrame(wooldridge("hseinv"))

# linear regression without time trend:
reg_wot = lm(@formula(log(invpc) ~ log(price)), hseinv)
table_reg_wot = coeftable(reg_wot)
println("table_reg_wot: \n$table_reg_wot\n")

# linear regression with time trend (data set includes a time variable t):
reg_wt = lm(@formula(log(invpc) ~ log(price) + t), hseinv)
table_reg_wt = coeftable(reg_wt)
println("table_reg_wt: \n$table_reg_wt")

Example-Barium.jl

using WooldridgeDatasets, DataFrames, Dates, Plots

barium = DataFrame(wooldridge("barium"))
T = nrow(barium)

# monthly time series starting Feb. 1978:
barium.date = range(Date(1978, 2, 1), step=Month(1), length=T)
preview = barium[1:5, ["date", "chnimp"]]
println("preview: \n$preview")

# plot chnimp:
plot(barium.date, barium.chnimp, legend=false, color="grey")
ylabel!("chnimp")
savefig("JlGraphs/Example-Barium.pdf")

Example-StockData.jl

using DataFrames, Dates, MarketData, Plots

# download data for "F" (= Ford Motor Company) and define start and end:
ticker = "F"
start_date = DateTime(2014, 1, 1)
end_date = DateTime(2016, 1, 1)

# import data:
F_data = yahoo(ticker, YahooOpt(period1=start_date, period2=end_date))

# look at imported data:
F_data_head = first(DataFrame(F_data), 5)
println("F_data_head: \n$F_data_head\n")

F_data_tail = last(DataFrame(F_data), 5)
println("F_data_tail: \n$F_data_tail")

# time series plot of adjusted closing prices:
plot(F_data.AdjClose, legend=false, color="grey")
ylabel!("AdjClose")
savefig("JlGraphs/Example-StockData.pdf")