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Last data update: 2014.03.03 |
US Consumption Data (1950–1993)DescriptionTime series data on US income and consumption expenditure, 1950–1993. Usagedata("USConsump1993")
FormatAn annual multiple time series from 1950 to 1993 with 2 variables.
SourceThe data is from Baltagi (2002). ReferencesBaltagi, B.H. (2002). Econometrics, 3rd ed. Berlin, Springer. See Also
Examples
## data from Baltagi (2002)
data("USConsump1993", package = "AER")
plot(USConsump1993, plot.type = "single", col = 1:2)
## Chapter 5 (p. 122-125)
fm <- lm(expenditure ~ income, data = USConsump1993)
summary(fm)
## Durbin-Watson test (p. 122)
dwtest(fm)
## Breusch-Godfrey test (Table 5.4, p. 124)
bgtest(fm)
## Newey-West standard errors (Table 5.5, p. 125)
coeftest(fm, vcov = NeweyWest(fm, lag = 3, prewhite = FALSE, adjust = TRUE))
## Chapter 8
library("strucchange")
## Recursive residuals
rr <- recresid(fm)
rr
## Recursive CUSUM test
rcus <- efp(expenditure ~ income, data = USConsump1993)
plot(rcus)
sctest(rcus)
## Harvey-Collier test
harvtest(fm)
## NOTE" Mistake in Baltagi (2002) who computes
## the t-statistic incorrectly as 0.0733 via
mean(rr)/sd(rr)/sqrt(length(rr))
## whereas it should be (as in harvtest)
mean(rr)/sd(rr) * sqrt(length(rr))
## Rainbow test
raintest(fm, center = 23)
## J test for non-nested models
library("dynlm")
fm1 <- dynlm(expenditure ~ income + L(income), data = USConsump1993)
fm2 <- dynlm(expenditure ~ income + L(expenditure), data = USConsump1993)
jtest(fm1, fm2)
## Chapter 14
## ACF and PACF for expenditures and first differences
exps <- USConsump1993[, "expenditure"]
(acf(exps))
(pacf(exps))
(acf(diff(exps)))
(pacf(diff(exps)))
## dynamic regressions, eq. (14.8)
fm <- dynlm(d(exps) ~ I(time(exps) - 1949) + L(exps))
summary(fm)
Results
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> library(AER)
Loading required package: car
Loading required package: lmtest
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Loading required package: survival
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AER/USConsump1993.Rd_%03d_medium.png", width=480, height=480)
> ### Name: USConsump1993
> ### Title: US Consumption Data (1950-1993)
> ### Aliases: USConsump1993
> ### Keywords: datasets
>
> ### ** Examples
>
> ## data from Baltagi (2002)
> data("USConsump1993", package = "AER")
> plot(USConsump1993, plot.type = "single", col = 1:2)
>
> ## Chapter 5 (p. 122-125)
> fm <- lm(expenditure ~ income, data = USConsump1993)
> summary(fm)
Call:
lm(formula = expenditure ~ income, data = USConsump1993)
Residuals:
Min 1Q Median 3Q Max
-294.52 -67.02 4.64 90.02 325.84
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -65.795821 90.990824 -0.723 0.474
income 0.915623 0.008648 105.874 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 153.6 on 42 degrees of freedom
Multiple R-squared: 0.9963, Adjusted R-squared: 0.9962
F-statistic: 1.121e+04 on 1 and 42 DF, p-value: < 2.2e-16
> ## Durbin-Watson test (p. 122)
> dwtest(fm)
Durbin-Watson test
data: fm
DW = 0.46078, p-value = 3.274e-11
alternative hypothesis: true autocorrelation is greater than 0
> ## Breusch-Godfrey test (Table 5.4, p. 124)
> bgtest(fm)
Breusch-Godfrey test for serial correlation of order up to 1
data: fm
LM test = 24.901, df = 1, p-value = 6.034e-07
> ## Newey-West standard errors (Table 5.5, p. 125)
> coeftest(fm, vcov = NeweyWest(fm, lag = 3, prewhite = FALSE, adjust = TRUE))
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -65.795821 133.345400 -0.4934 0.6243
income 0.915623 0.015458 59.2319 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> ## Chapter 8
> library("strucchange")
> ## Recursive residuals
> rr <- recresid(fm)
> rr
[1] 24.900681 30.354827 50.893291 63.260389 -49.805907 -28.404311
[7] -31.520559 53.194256 67.696114 -2.646556 9.679147 39.658827
[13] -40.126557 -30.260756 2.605633 -78.941467 27.185066 64.363195
[19] -64.906717 -71.641013 70.095867 -113.475323 -85.633171 -29.427630
[25] 128.328459 220.693133 126.591749 78.394247 -25.955574 -124.178686
[31] -90.845193 127.830581 -30.794629 159.780872 201.707127 405.310561
[37] 390.953841 373.370919 316.431235 188.109683 134.461285 339.300414
> ## Recursive CUSUM test
> rcus <- efp(expenditure ~ income, data = USConsump1993)
> plot(rcus)
> sctest(rcus)
Recursive CUSUM test
data: rcus
S = 1.0267, p-value = 0.02707
> ## Harvey-Collier test
> harvtest(fm)
Harvey-Collier test
data: fm
HC = 3.0802, df = 41, p-value = 0.003685
> ## NOTE" Mistake in Baltagi (2002) who computes
> ## the t-statistic incorrectly as 0.0733 via
> mean(rr)/sd(rr)/sqrt(length(rr))
[1] 0.07333754
> ## whereas it should be (as in harvtest)
> mean(rr)/sd(rr) * sqrt(length(rr))
[1] 3.080177
>
> ## Rainbow test
> raintest(fm, center = 23)
Rainbow test
data: fm
Rain = 4.1448, df1 = 22, df2 = 20, p-value = 0.001116
>
> ## J test for non-nested models
> library("dynlm")
> fm1 <- dynlm(expenditure ~ income + L(income), data = USConsump1993)
> fm2 <- dynlm(expenditure ~ income + L(expenditure), data = USConsump1993)
> jtest(fm1, fm2)
J test
Model 1: expenditure ~ income + L(income)
Model 2: expenditure ~ income + L(expenditure)
Estimate Std. Error t value Pr(>|t|)
M1 + fitted(M2) 1.6378 0.20984 7.8051 1.726e-09 ***
M2 + fitted(M1) -2.5419 0.61603 -4.1262 0.0001874 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> ## Chapter 14
> ## ACF and PACF for expenditures and first differences
> exps <- USConsump1993[, "expenditure"]
> (acf(exps))
Autocorrelations of series 'exps', by lag
0 1 2 3 4 5 6 7 8 9 10
1.000 0.941 0.880 0.820 0.753 0.683 0.614 0.547 0.479 0.412 0.348
11 12 13 14 15 16
0.288 0.230 0.171 0.110 0.049 -0.010
> (pacf(exps))
Partial autocorrelations of series 'exps', by lag
1 2 3 4 5 6 7 8 9 10 11
0.941 -0.045 -0.035 -0.083 -0.064 -0.034 -0.025 -0.049 -0.043 -0.011 -0.022
12 13 14 15 16
-0.025 -0.061 -0.066 -0.071 -0.030
> (acf(diff(exps)))
Autocorrelations of series 'diff(exps)', by lag
0 1 2 3 4 5 6 7 8 9 10
1.000 0.344 -0.067 -0.156 -0.105 -0.077 -0.072 0.026 -0.050 0.058 0.073
11 12 13 14 15 16
0.078 -0.033 -0.069 -0.158 -0.161 0.034
> (pacf(diff(exps)))
Partial autocorrelations of series 'diff(exps)', by lag
1 2 3 4 5 6 7 8 9 10 11
0.344 -0.209 -0.066 -0.038 -0.065 -0.060 0.056 -0.133 0.133 -0.014 0.058
12 13 14 15 16
-0.079 0.005 -0.175 -0.032 0.065
>
> ## dynamic regressions, eq. (14.8)
> fm <- dynlm(d(exps) ~ I(time(exps) - 1949) + L(exps))
> summary(fm)
Time series regression with "ts" data:
Start = 1951, End = 1993
Call:
dynlm(formula = d(exps) ~ I(time(exps) - 1949) + L(exps))
Residuals:
Min 1Q Median 3Q Max
-357.76 -78.18 22.49 108.97 201.06
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1048.96039 353.81291 2.965 0.00509 **
I(time(exps) - 1949) 39.90164 14.31344 2.788 0.00808 **
L(exps) -0.19561 0.07398 -2.644 0.01164 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 147.4 on 40 degrees of freedom
Multiple R-squared: 0.1784, Adjusted R-squared: 0.1373
F-statistic: 4.343 on 2 and 40 DF, p-value: 0.01963
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> dev.off()
null device
1
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