Questão 1 - Replicando os resultados de Nelson & Plosser (60
pontos):
Base de Dados e Pacotes de Análise:
library(readxl)
setwd("C:/Users/giord/OneDrive/Área de Trabalho/FGV/FGV - NOVO/FGV - 9º SEMESTRE/Indicadores Econômicos/Prova Parcial")
dados = read_xls("dados_pp.xls")
dados = data.frame(dados, row.names = 1)
dados = log(dados)
library(urca)
A) Real GNP:
rgnp = dados$RGNP[50:111]
rgnp = ts(rgnp, start = 1909, end = 1970, frequency = 1)
rgnp_adf = ur.df(rgnp, type = "trend", lags = 2)
summary(rgnp_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.166182 -0.030344 0.008053 0.030462 0.150420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.869931 0.293307 2.966 0.00449 **
## z.lag.1 -0.188314 0.064365 -2.926 0.00502 **
## tt 0.006087 0.002042 2.981 0.00430 **
## z.diff.lag1 0.407611 0.127289 3.202 0.00229 **
## z.diff.lag2 0.048316 0.135340 0.357 0.72249
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05915 on 54 degrees of freedom
## Multiple R-squared: 0.2463, Adjusted R-squared: 0.1905
## F-statistic: 4.412 on 4 and 54 DF, p-value: 0.003697
##
##
## Value of test-statistic is: -2.9257 4.8801 4.448
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -4.04 -3.45 -3.15
## phi2 6.50 4.88 4.16
## phi3 8.73 6.49 5.47
B) Nominal GNP
gnp = dados$GNP[50:111]
gnp = ts(gnp, start = 1909, end = 1970, frequency = 1)
gnp_adf = ur.df(gnp, type = "trend", lags = 2)
summary(gnp_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.291858 -0.040234 0.006546 0.052642 0.195404
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.999551 0.475454 2.102 0.040204 *
## z.lag.1 -0.094783 0.046360 -2.044 0.045793 *
## tt 0.005239 0.002544 2.059 0.044328 *
## z.diff.lag1 0.508722 0.129926 3.915 0.000255 ***
## z.diff.lag2 -0.050976 0.136428 -0.374 0.710130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08849 on 54 degrees of freedom
## Multiple R-squared: 0.2661, Adjusted R-squared: 0.2118
## F-statistic: 4.896 on 4 and 54 DF, p-value: 0.001926
##
##
## Value of test-statistic is: -2.0445 3.6798 2.1453
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -4.04 -3.45 -3.15
## phi2 6.50 4.88 4.16
## phi3 8.73 6.49 5.47
C) Real per Capita GNP
pcgnp = dados$PCGNP[50:111]
pcgnp = ts(pcgnp, start = 1909, end = 1970, frequency = 1)
pcgnp_adf = ur.df(pcgnp, type = "trend", lags = 2)
summary(pcgnp_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.166951 -0.029138 0.004678 0.034746 0.148945
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.377094 0.457508 3.010 0.00397 **
## z.lag.1 -0.197243 0.065680 -3.003 0.00404 **
## tt 0.003826 0.001277 2.997 0.00411 **
## z.diff.lag1 0.398356 0.126901 3.139 0.00275 **
## z.diff.lag2 0.055838 0.134604 0.415 0.67991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0598 on 54 degrees of freedom
## Multiple R-squared: 0.2452, Adjusted R-squared: 0.1893
## F-statistic: 4.386 on 4 and 54 DF, p-value: 0.00383
##
##
## Value of test-statistic is: -3.0031 3.8165 4.6584
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -4.04 -3.45 -3.15
## phi2 6.50 4.88 4.16
## phi3 8.73 6.49 5.47
D) Industrial Production
ip = dados$IP
ip = ts(ip, start = 1860, end = 1970, frequency = 1)
ip_adf = ur.df(ip, type = "trend", lags = 6)
summary(ip_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33741 -0.04449 0.01665 0.05137 0.19021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.059528 0.025591 2.326 0.0221 *
## z.lag.1 -0.168435 0.067593 -2.492 0.0144 *
## tt 0.006884 0.002835 2.428 0.0170 *
## z.diff.lag1 0.124914 0.107550 1.161 0.2484
## z.diff.lag2 -0.023953 0.101820 -0.235 0.8145
## z.diff.lag3 0.045999 0.100253 0.459 0.6474
## z.diff.lag4 -0.030399 0.099522 -0.305 0.7607
## z.diff.lag5 -0.217744 0.097979 -2.222 0.0286 *
## z.diff.lag6 0.085590 0.100460 0.852 0.3964
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09769 on 95 degrees of freedom
## Multiple R-squared: 0.1643, Adjusted R-squared: 0.09393
## F-statistic: 2.335 on 8 and 95 DF, p-value: 0.02463
##
##
## Value of test-statistic is: -2.4919 7.9757 3.1934
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.99 -3.43 -3.13
## phi2 6.22 4.75 4.07
## phi3 8.43 6.49 5.47
E) Employment:
emp = dados$EMP[31:111]
emp = ts(emp, start = 1890, end = 1970, frequency = 1)
emp_adf = ur.df(emp, type = "trend", lags = 3)
summary(emp_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08250 -0.01153 0.00171 0.01957 0.07632
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6871517 0.5169102 3.264 0.00169 **
## z.lag.1 -0.1657344 0.0513632 -3.227 0.00190 **
## tt 0.0023828 0.0008009 2.975 0.00400 **
## z.diff.lag1 0.4681950 0.1064498 4.398 3.76e-05 ***
## z.diff.lag2 -0.1119140 0.1126999 -0.993 0.32407
## z.diff.lag3 0.1028858 0.1088433 0.945 0.34773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03307 on 71 degrees of freedom
## Multiple R-squared: 0.2762, Adjusted R-squared: 0.2253
## F-statistic: 5.42 on 5 and 71 DF, p-value: 0.0002803
##
##
## Value of test-statistic is: -3.2267 6.7239 5.5581
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -4.04 -3.45 -3.15
## phi2 6.50 4.88 4.16
## phi3 8.73 6.49 5.47
F) Unemployment:
un = dados$UN[31:111]
un = ts(un, start = 1890, end = 1970, frequency = 1)
un_adf = ur.df(un, type = "trend", lags = 4)
summary(un_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.31059 -0.20280 -0.00699 0.23062 0.83850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4960271 0.2004062 2.475 0.01578 *
## z.lag.1 -0.2810985 0.0910128 -3.089 0.00290 **
## tt -0.0005256 0.0021883 -0.240 0.81088
## z.diff.lag1 0.3873118 0.1197937 3.233 0.00188 **
## z.diff.lag2 -0.2247957 0.1212136 -1.855 0.06793 .
## z.diff.lag3 0.2477131 0.1084116 2.285 0.02540 *
## z.diff.lag4 -0.0400062 0.1102087 -0.363 0.71771
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4122 on 69 degrees of freedom
## Multiple R-squared: 0.2839, Adjusted R-squared: 0.2216
## F-statistic: 4.559 on 6 and 69 DF, p-value: 0.0005967
##
##
## Value of test-statistic is: -3.0886 3.2203 4.7992
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -4.04 -3.45 -3.15
## phi2 6.50 4.88 4.16
## phi3 8.73 6.49 5.47
G) GNP Deflator:
prgnp = dados$PRGNP[30:111]
prgnp = ts(prgnp, start = 1889, end = 1970, frequency = 1)
prgnp_adf = ur.df(prgnp, type = "trend", lags = 2)
summary(prgnp_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.186319 -0.016316 0.000074 0.017632 0.149903
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2769162 0.1060928 2.610 0.010949 *
## z.lag.1 -0.0918472 0.0357116 -2.572 0.012120 *
## tt 0.0021845 0.0008171 2.674 0.009230 **
## z.diff.lag1 0.4206318 0.1113790 3.777 0.000319 ***
## z.diff.lag2 0.0855415 0.1144034 0.748 0.457000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04646 on 74 degrees of freedom
## Multiple R-squared: 0.2573, Adjusted R-squared: 0.2172
## F-statistic: 6.41 on 4 and 74 DF, p-value: 0.0001745
##
##
## Value of test-statistic is: -2.5719 3.9298 3.5743
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -4.04 -3.45 -3.15
## phi2 6.50 4.88 4.16
## phi3 8.73 6.49 5.47
H) Consumer Prices:
cpi = dados$CPI
cpi = ts(cpi, start = 1860, end = 1970, frequency = 1)
cpi_adf = ur.df(cpi, type = "trend", lags = 4)
summary(cpi_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.169538 -0.012808 0.000006 0.017828 0.105750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1093128 0.0472578 2.313 0.02279 *
## z.lag.1 -0.0409796 0.0149072 -2.749 0.00711 **
## tt 0.0008581 0.0002027 4.234 5.15e-05 ***
## z.diff.lag1 0.5519046 0.0885481 6.233 1.13e-08 ***
## z.diff.lag2 -0.2599878 0.1042206 -2.495 0.01427 *
## z.diff.lag3 0.1305110 0.1026455 1.271 0.20654
## z.diff.lag4 -0.0199930 0.0835096 -0.239 0.81128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.038 on 99 degrees of freedom
## Multiple R-squared: 0.4221, Adjusted R-squared: 0.3871
## F-statistic: 12.05 on 6 and 99 DF, p-value: 3.969e-10
##
##
## Value of test-statistic is: -2.749 6.7219 9.405
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.99 -3.43 -3.13
## phi2 6.22 4.75 4.07
## phi3 8.43 6.49 5.47
I) Wages:
wg = dados$WG[41:111]
wg = ts(wg, start = 1900, end = 1970, frequency = 1)
wg_adf = ur.df(wg, type = "trend", lags = 3)
summary(wg_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.160970 -0.026380 0.004898 0.024368 0.139041
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.588511 0.254990 2.308 0.0244 *
## z.lag.1 -0.095280 0.042551 -2.239 0.0288 *
## tt 0.003997 0.001746 2.288 0.0256 *
## z.diff.lag1 0.546002 0.123251 4.430 3.98e-05 ***
## z.diff.lag2 -0.096335 0.139529 -0.690 0.4925
## z.diff.lag3 0.062032 0.127595 0.486 0.6286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06075 on 61 degrees of freedom
## Multiple R-squared: 0.2944, Adjusted R-squared: 0.2366
## F-statistic: 5.09 on 5 and 61 DF, p-value: 0.000578
##
##
## Value of test-statistic is: -2.2392 4.1249 2.6192
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -4.04 -3.45 -3.15
## phi2 6.50 4.88 4.16
## phi3 8.73 6.49 5.47
J) Real Wages:
rwg = dados$RWG[41:111]
rwg = ts(rwg, start = 1900, end = 1970, frequency = 1)
rwg_adf = ur.df(rwg, type = "trend", lags = 2)
summary(rwg_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.090435 -0.017603 -0.002577 0.017592 0.088688
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.510118 0.168551 3.026 0.00358 **
## z.lag.1 -0.178520 0.060042 -2.973 0.00417 **
## tt 0.003761 0.001228 3.062 0.00323 **
## z.diff.lag1 0.245234 0.119343 2.055 0.04404 *
## z.diff.lag2 0.021920 0.122396 0.179 0.85844
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03509 on 63 degrees of freedom
## Multiple R-squared: 0.1669, Adjusted R-squared: 0.114
## F-statistic: 3.155 on 4 and 63 DF, p-value: 0.01988
##
##
## Value of test-statistic is: -2.9732 6.2316 4.7115
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -4.04 -3.45 -3.15
## phi2 6.50 4.88 4.16
## phi3 8.73 6.49 5.47
K) Money Stock:
m = dados$M[30:111]
m = ts(m, start = 1889, end = 1970, frequency = 1)
m_adf = ur.df(m, type = "trend", lags = 2)
summary(m_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.146482 -0.019578 -0.001253 0.019018 0.113876
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.132927 0.038119 3.487 0.000825 ***
## z.lag.1 -0.087035 0.029255 -2.975 0.003955 **
## tt 0.004994 0.001713 2.916 0.004692 **
## z.diff.lag1 0.601354 0.110028 5.465 5.95e-07 ***
## z.diff.lag2 0.077361 0.115714 0.669 0.505859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04691 on 74 degrees of freedom
## Multiple R-squared: 0.4348, Adjusted R-squared: 0.4042
## F-statistic: 14.23 on 4 and 74 DF, p-value: 1.161e-08
##
##
## Value of test-statistic is: -2.975 6.0443 4.4531
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -4.04 -3.45 -3.15
## phi2 6.50 4.88 4.16
## phi3 8.73 6.49 5.47
L) Velocity:
vel = dados$VEL[10:111]
vel = ts(vel, start = 1869, end = 1970, frequency = 1)
vel_adf = ur.df(vel, type = "trend", lags = 1)
summary(vel_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.156282 -0.035278 0.004818 0.042358 0.142090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0650269 0.0544974 1.193 0.2357
## z.lag.1 -0.0644365 0.0367516 -1.753 0.0827 .
## tt -0.0004586 0.0005115 -0.896 0.3722
## z.diff.lag 0.1210574 0.1017588 1.190 0.2371
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06724 on 96 degrees of freedom
## Multiple R-squared: 0.0622, Adjusted R-squared: 0.03289
## F-statistic: 2.122 on 3 and 96 DF, p-value: 0.1024
##
##
## Value of test-statistic is: -1.7533 2.3638 2.5503
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.99 -3.43 -3.13
## phi2 6.22 4.75 4.07
## phi3 8.43 6.49 5.47
M) Interest Rate:
bnd = exp(dados$BND[41:111])
bnd = ts(bnd, start = 1900, end = 1970, frequency = 1)
bnd_adf = ur.df(bnd, type = "trend", lags = 3)
summary(bnd_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.57711 -0.21400 -0.03212 0.18740 1.55434
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.484415 0.359266 -1.348 0.1825
## z.lag.1 0.088737 0.084712 1.048 0.2990
## tt 0.006489 0.003279 1.979 0.0523 .
## z.diff.lag1 -0.398203 0.158387 -2.514 0.0146 *
## z.diff.lag2 -0.191998 0.158134 -1.214 0.2294
## z.diff.lag3 -0.089862 0.145108 -0.619 0.5380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4989 on 61 degrees of freedom
## Multiple R-squared: 0.1237, Adjusted R-squared: 0.05187
## F-statistic: 1.722 on 5 and 61 DF, p-value: 0.1431
##
##
## Value of test-statistic is: 1.0475 2.0001 2.2042
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -4.04 -3.45 -3.15
## phi2 6.50 4.88 4.16
## phi3 8.73 6.49 5.47
N) Common Stock Prices:
sp500 = dados$SP500[12:111]
sp500 = ts(sp500, start = 1871, end = 1970, frequency = 1)
sp500_adf = ur.df(sp500, type = "trend", lags = 3)
summary(sp500_adf)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.63422 -0.11260 0.01245 0.11410 0.29904
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.095552 0.057131 1.673 0.09790 .
## z.lag.1 -0.097999 0.045802 -2.140 0.03509 *
## tt 0.003302 0.001383 2.387 0.01908 *
## z.diff.lag1 0.302346 0.106838 2.830 0.00574 **
## z.diff.lag2 -0.147538 0.107752 -1.369 0.17433
## z.diff.lag3 0.052388 0.107600 0.487 0.62753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1562 on 90 degrees of freedom
## Multiple R-squared: 0.1366, Adjusted R-squared: 0.08868
## F-statistic: 2.849 on 5 and 90 DF, p-value: 0.01959
##
##
## Value of test-statistic is: -2.1396 2.8281 2.8497
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -4.04 -3.45 -3.15
## phi2 6.50 4.88 4.16
## phi3 8.73 6.49 5.47
Tabela de Resultados:
#COLUNA 1 - NOME DAS VARIÁVEIS:
Series = c("Real GNP", "Nominal GNP", "Real per Capita GNP", "Industrial Production", "Employment", "Unemployment Rate", "GNP Deflator", "Consumer Prices", "Wages", "Real Wages", "Money Stock", "Velocity", "Interest Rate", "Common Stock Prices")
#COLUNA 2 - NÚMERO DE OBSERVAÇÕES:
T = c(length(rgnp_adf@y),
length(gnp_adf@y),
length(pcgnp_adf@y),
length(ip_adf@y),
length(emp_adf@y),
length(un_adf@y),
length(prgnp_adf@y),
length(cpi_adf@y),
length(wg_adf@y),
length(rwg_adf@y),
length(m_adf@y),
length(vel_adf@y),
length(bnd_adf@y),
length(sp500_adf@y))
#COLUNA 3 - NÚMERO DE LAGS:
k = c(rgnp_adf@lags,
gnp_adf@lags,
pcgnp_adf@lags,
ip_adf@lags,
emp_adf@lags,
un_adf@lags,
prgnp_adf@lags,
cpi_adf@lags,
wg_adf@lags,
rwg_adf@lags,
m_adf@lags,
vel_adf@lags,
bnd_adf@lags,
sp500_adf@lags)
#COLUNA 4 - ESTIMATIVA DO INTERCEPTO:
u = c(rgnp_adf@testreg$coefficients[1,1],
gnp_adf@testreg$coefficients[1,1],
pcgnp_adf@testreg$coefficients[1,1],
ip_adf@testreg$coefficients[1,1],
emp_adf@testreg$coefficients[1,1],
un_adf@testreg$coefficients[1,1],
prgnp_adf@testreg$coefficients[1,1],
cpi_adf@testreg$coefficients[1,1],
wg_adf@testreg$coefficients[1,1],
rwg_adf@testreg$coefficients[1,1],
m_adf@testreg$coefficients[1,1],
vel_adf@testreg$coefficients[1,1],
bnd_adf@testreg$coefficients[1,1],
sp500_adf@testreg$coefficients[1,1])
u = round(u,3)
#COLUNA 5 - ESTATÍSTICA DE TESTE DO INTERCEPTO:
t_u = c(rgnp_adf@testreg$coefficients[1,3],
gnp_adf@testreg$coefficients[1,3],
pcgnp_adf@testreg$coefficients[1,3],
ip_adf@testreg$coefficients[1,3],
emp_adf@testreg$coefficients[1,3],
un_adf@testreg$coefficients[1,3],
prgnp_adf@testreg$coefficients[1,3],
cpi_adf@testreg$coefficients[1,3],
wg_adf@testreg$coefficients[1,3],
rwg_adf@testreg$coefficients[1,3],
m_adf@testreg$coefficients[1,3],
vel_adf@testreg$coefficients[1,3],
bnd_adf@testreg$coefficients[1,3],
sp500_adf@testreg$coefficients[1,3])
t_u = round(t_u,2)
#COLUNA 6 - ESTIMATIVA DA TENDÊNCIA DETERMINÍSTICA:
y = c(rgnp_adf@testreg$coefficients[3,1],
gnp_adf@testreg$coefficients[3,1],
pcgnp_adf@testreg$coefficients[3,1],
ip_adf@testreg$coefficients[3,1],
emp_adf@testreg$coefficients[3,1],
un_adf@testreg$coefficients[3,1],
prgnp_adf@testreg$coefficients[3,1],
cpi_adf@testreg$coefficients[3,1],
wg_adf@testreg$coefficients[3,1],
rwg_adf@testreg$coefficients[3,1],
m_adf@testreg$coefficients[3,1],
vel_adf@testreg$coefficients[3,1],
bnd_adf@testreg$coefficients[3,1],
sp500_adf@testreg$coefficients[3,1])
y = round(y,3)
#COLUNA 7 - ESTATÍSTICA DE TESTE DA TENDÊNCIA DETERMINÍSTICA:
t_y = c(rgnp_adf@testreg$coefficients[3,3],
gnp_adf@testreg$coefficients[3,3],
pcgnp_adf@testreg$coefficients[3,3],
ip_adf@testreg$coefficients[3,3],
emp_adf@testreg$coefficients[3,3],
un_adf@testreg$coefficients[3,3],
prgnp_adf@testreg$coefficients[3,3],
cpi_adf@testreg$coefficients[3,3],
wg_adf@testreg$coefficients[3,3],
rwg_adf@testreg$coefficients[3,3],
m_adf@testreg$coefficients[3,3],
vel_adf@testreg$coefficients[3,3],
bnd_adf@testreg$coefficients[3,3],
sp500_adf@testreg$coefficients[3,3])
t_y = round(t_y,2)
#COLUNA 8 - ESTIMATIVA DA AUTOCORRELAÇÃO:
p = c(exp(rgnp_adf@testreg$coefficients[2,1]),
exp(gnp_adf@testreg$coefficients[2,1]),
exp(pcgnp_adf@testreg$coefficients[2,1]),
exp(ip_adf@testreg$coefficients[2,1]),
exp(emp_adf@testreg$coefficients[2,1]),
exp(un_adf@testreg$coefficients[2,1]),
exp(prgnp_adf@testreg$coefficients[2,1]),
exp(cpi_adf@testreg$coefficients[2,1]),
exp(wg_adf@testreg$coefficients[2,1]),
exp(rwg_adf@testreg$coefficients[2,1]),
exp(m_adf@testreg$coefficients[2,1]),
exp(vel_adf@testreg$coefficients[2,1]),
exp(bnd_adf@testreg$coefficients[2,1]),
exp(sp500_adf@testreg$coefficients[2,1]))
p = round(p,3)
#COLUNA 9 - ESTATÍSTICA DE TESTE DA AUTOCORRELAÇÃO:
t_p = c(rgnp_adf@testreg$coefficients[2,3],
gnp_adf@testreg$coefficients[2,3],
pcgnp_adf@testreg$coefficients[2,3],
ip_adf@testreg$coefficients[2,3],
emp_adf@testreg$coefficients[2,3],
un_adf@testreg$coefficients[2,3],
prgnp_adf@testreg$coefficients[2,3],
cpi_adf@testreg$coefficients[2,3],
wg_adf@testreg$coefficients[2,3],
rwg_adf@testreg$coefficients[2,3],
m_adf@testreg$coefficients[2,3],
vel_adf@testreg$coefficients[2,3],
bnd_adf@testreg$coefficients[2,3],
sp500_adf@testreg$coefficients[2,3])
t_p = round(t_p,2)
library(knitr)
tab = cbind(Series, T, k, u, t_u, y, t_y, p, t_p)
tab = data.frame(tab)
kable(tab, caption = "Teste para Raízes Unitárias Autoregressivas")
Teste para Raízes Unitárias Autoregressivas
| Real GNP |
62 |
2 |
0.87 |
2.97 |
0.006 |
2.98 |
0.828 |
-2.93 |
| Nominal GNP |
62 |
2 |
1 |
2.1 |
0.005 |
2.06 |
0.91 |
-2.04 |
| Real per Capita GNP |
62 |
2 |
1.377 |
3.01 |
0.004 |
3 |
0.821 |
-3 |
| Industrial Production |
111 |
6 |
0.06 |
2.33 |
0.007 |
2.43 |
0.845 |
-2.49 |
| Employment |
81 |
3 |
1.687 |
3.26 |
0.002 |
2.98 |
0.847 |
-3.23 |
| Unemployment Rate |
81 |
4 |
0.496 |
2.48 |
-0.001 |
-0.24 |
0.755 |
-3.09 |
| GNP Deflator |
82 |
2 |
0.277 |
2.61 |
0.002 |
2.67 |
0.912 |
-2.57 |
| Consumer Prices |
111 |
4 |
0.109 |
2.31 |
0.001 |
4.23 |
0.96 |
-2.75 |
| Wages |
71 |
3 |
0.589 |
2.31 |
0.004 |
2.29 |
0.909 |
-2.24 |
| Real Wages |
71 |
2 |
0.51 |
3.03 |
0.004 |
3.06 |
0.837 |
-2.97 |
| Money Stock |
82 |
2 |
0.133 |
3.49 |
0.005 |
2.92 |
0.917 |
-2.98 |
| Velocity |
102 |
1 |
0.065 |
1.19 |
0 |
-0.9 |
0.938 |
-1.75 |
| Interest Rate |
71 |
3 |
-0.484 |
-1.35 |
0.006 |
1.98 |
1.093 |
1.05 |
| Common Stock Prices |
100 |
3 |
0.096 |
1.67 |
0.003 |
2.39 |
0.907 |
-2.14 |