HW5-2
Omar Alenezy
April 12, 2016
library("Quandl")## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary("vars")## Loading required package: MASS## Loading required package: strucchange## Loading required package: sandwich## Loading required package: urca## Loading required package: lmtestlibrary("gdata")## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:xts':
##
## first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.sizelibrary("stargazer")##
## Please cite as:## Hlavac, Marek (2015). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2. http://CRAN.R-project.org/package=stargazerlibrary("tseries")
pi <- Quandl("FRED/INDPRO", type="zoo")
cpi <- Quandl("FRED/CPIAUCSL", type="zoo")
pi <- window(pi,start="JAN 1947", end="Feb 2016")
cpi <- window(cpi,start="Jan 1947", end="Feb 2016")
lpi <- log(pi)
lcpi <- log(cpi)
dlpi <- diff(lpi,1)
dlcpi <- diff(lcpi,1)
par(mfrow=c(2,2))
plot(lpi,xlab="Period", ylab="Log of PI")
plot(lcpi, xlab="Period", ylab="Log of CPI")
plot(dlpi,xlab="Period", ylab="Diff of log PI")
plot(dlcpi, xlab="Period", ylab="Diff of log CPI")
adf.test(lpi)##
## Augmented Dickey-Fuller Test
##
## data: lpi
## Dickey-Fuller = -2.0144, Lag order = 9, p-value = 0.5722
## alternative hypothesis: stationaryadf.test(lcpi)##
## Augmented Dickey-Fuller Test
##
## data: lcpi
## Dickey-Fuller = -1.2868, Lag order = 9, p-value = 0.8802
## alternative hypothesis: stationaryadf.test(dlcpi)##
## Augmented Dickey-Fuller Test
##
## data: dlcpi
## Dickey-Fuller = -3.9607, Lag order = 9, p-value = 0.01096
## alternative hypothesis: stationaryB
y <- cbind(dlpi, dlcpi)
y <- na.trim(y)
y<- sweep(y,2,apply(y,2,mean))
VARselect(y,lag.max=10)## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 10 4 2 10
##
## $criteria
## 1 2 3 4
## AIC(n) -2.125239e+01 -2.129200e+01 -2.131248e+01 -2.132251e+01
## HQ(n) -2.123916e+01 -2.126994e+01 -2.128160e+01 -2.128280e+01
## SC(n) -2.121790e+01 -2.123452e+01 -2.123200e+01 -2.121903e+01
## FPE(n) 5.891187e-10 5.662403e-10 5.547638e-10 5.492289e-10
## 5 6 7 8
## AIC(n) -2.132836e+01 -2.133638e+01 -2.134489e+01 -2.134279e+01
## HQ(n) -2.127983e+01 -2.127903e+01 -2.127872e+01 -2.126779e+01
## SC(n) -2.120189e+01 -2.118692e+01 -2.117244e+01 -2.114734e+01
## FPE(n) 5.460237e-10 5.416618e-10 5.370741e-10 5.382056e-10
## 9 10
## AIC(n) -2.135631e+01 -2.136879e+01
## HQ(n) -2.127248e+01 -2.127614e+01
## SC(n) -2.113786e+01 -2.112735e+01
## FPE(n) 5.309843e-10 5.244008e-10var1 <- VAR(y,p=7, type="const")
summary(var1)##
## VAR Estimation Results:
## =========================
## Endogenous variables: dlpi, dlcpi
## Deterministic variables: const
## Sample size: 822
## Log Likelihood: 6452.474
## Roots of the characteristic polynomial:
## 0.9028 0.7865 0.7371 0.7371 0.7223 0.7223 0.6597 0.6597 0.6512 0.6512 0.6193 0.6193 0.4736 0.4736
## Call:
## VAR(y = y, p = 7, type = "const")
##
##
## Estimation results for equation dlpi:
## =====================================
## dlpi = dlpi.l1 + dlcpi.l1 + dlpi.l2 + dlcpi.l2 + dlpi.l3 + dlcpi.l3 + dlpi.l4 + dlcpi.l4 + dlpi.l5 + dlcpi.l5 + dlpi.l6 + dlcpi.l6 + dlpi.l7 + dlcpi.l7 + const
##
## Estimate Std. Error t value Pr(>|t|)
## dlpi.l1 3.119e-01 3.519e-02 8.865 < 2e-16 ***
## dlcpi.l1 2.182e-01 1.141e-01 1.911 0.05632 .
## dlpi.l2 9.524e-02 3.679e-02 2.589 0.00981 **
## dlcpi.l2 -9.303e-02 1.246e-01 -0.747 0.45544
## dlpi.l3 8.096e-02 3.699e-02 2.189 0.02891 *
## dlcpi.l3 -6.987e-04 1.243e-01 -0.006 0.99551
## dlpi.l4 4.045e-02 3.706e-02 1.091 0.27538
## dlcpi.l4 -1.110e-01 1.239e-01 -0.896 0.37048
## dlpi.l5 -7.732e-02 3.688e-02 -2.097 0.03633 *
## dlcpi.l5 -2.493e-02 1.232e-01 -0.202 0.83973
## dlpi.l6 1.333e-02 3.684e-02 0.362 0.71752
## dlcpi.l6 -2.069e-01 1.208e-01 -1.712 0.08721 .
## dlpi.l7 2.976e-02 3.509e-02 0.848 0.39661
## dlcpi.l7 -1.086e-01 1.116e-01 -0.973 0.33067
## const 8.453e-06 3.036e-04 0.028 0.97779
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 0.008703 on 807 degrees of freedom
## Multiple R-Squared: 0.1932, Adjusted R-squared: 0.1792
## F-statistic: 13.8 on 14 and 807 DF, p-value: < 2.2e-16
##
##
## Estimation results for equation dlcpi:
## ======================================
## dlcpi = dlpi.l1 + dlcpi.l1 + dlpi.l2 + dlcpi.l2 + dlpi.l3 + dlcpi.l3 + dlpi.l4 + dlcpi.l4 + dlpi.l5 + dlcpi.l5 + dlpi.l6 + dlcpi.l6 + dlpi.l7 + dlcpi.l7 + const
##
## Estimate Std. Error t value Pr(>|t|)
## dlpi.l1 -5.826e-03 1.081e-02 -0.539 0.58993
## dlcpi.l1 4.434e-01 3.506e-02 12.648 < 2e-16 ***
## dlpi.l2 2.907e-02 1.130e-02 2.572 0.01028 *
## dlcpi.l2 4.396e-02 3.826e-02 1.149 0.25095
## dlpi.l3 6.764e-03 1.136e-02 0.595 0.55173
## dlcpi.l3 6.811e-02 3.816e-02 1.785 0.07464 .
## dlpi.l4 -2.168e-03 1.138e-02 -0.190 0.84897
## dlcpi.l4 4.042e-02 3.805e-02 1.062 0.28847
## dlpi.l5 -1.991e-02 1.133e-02 -1.758 0.07912 .
## dlcpi.l5 5.481e-02 3.784e-02 1.449 0.14785
## dlpi.l6 2.413e-02 1.131e-02 2.133 0.03323 *
## dlcpi.l6 5.981e-02 3.711e-02 1.611 0.10746
## dlpi.l7 3.861e-03 1.077e-02 0.358 0.72017
## dlcpi.l7 9.329e-02 3.428e-02 2.722 0.00663 **
## const -2.298e-05 9.323e-05 -0.246 0.80537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 0.002673 on 807 degrees of freedom
## Multiple R-Squared: 0.4078, Adjusted R-squared: 0.3975
## F-statistic: 39.69 on 14 and 807 DF, p-value: < 2.2e-16
##
##
##
## Covariance matrix of residuals:
## dlpi dlcpi
## dlpi 7.575e-05 7.511e-07
## dlcpi 7.511e-07 7.144e-06
##
## Correlation matrix of residuals:
## dlpi dlcpi
## dlpi 1.00000 0.03229
## dlcpi 0.03229 1.00000C
svar1 <- VAR(y,ic="AIC", lag.max=10)
svar1.r <-BQ(svar1)
summary(svar1.r)##
## SVAR Estimation Results:
## ========================
##
## Call:
## BQ(x = svar1)
##
## Type: Blanchard-Quah
## Sample size: 819
## Log Likelihood: 6447.023
##
## Estimated contemporaneous impact matrix:
## dlpi dlcpi
## dlpi 0.006485 0.005824
## dlcpi -0.001631 0.001978
##
## Estimated identified long run impact matrix:
## dlpi dlcpi
## dlpi 0.015360 0.00000
## dlcpi -0.004352 0.01474
##
## Covariance matrix of reduced form residuals (*100):
## dlpi dlcpi
## dlpi 7.597e-03 9.415e-05
## dlcpi 9.415e-05 6.574e-04D
var1.irfs <- irf(var1, n.ahead=30, cumulative=T)
par(mfcol=c(2,2))
plot(var1.irfs, plot.type="single")
F
var1.fevd <- fevd(var1, n.ahead=30)
var1.fevd[[1]][c(1,4,8,30)]## [1] 1.0000000 0.9955300 0.9836736 0.9616605plot(var1.fevd)
plot(var1.fevd, addbars=8)