Wages

Wages Data.

data(wages)

Format

A multivariate yearly time series from 1960 to 1979 with variables

w

wages,

CPI

consumer price index,

u

unemployment,

mw

minimum wage.

Source

The data was originally studied by Nicols (1983), the data set is given in Krämer and Sonnberger (1986). Below we replicate a few examples from their book. Some of these results differ more or less seriously and are sometimes parameterized differently.

References

D.A. Nicols (1983), Macroeconomic Determinants of Wage Adjustments in White Collar Occupations. Review of Economics and Statistics 65, 203–213

W. Krämer & H. Sonnberger (1986), The Linear Regression Model under Test. Heidelberg: Physica

Examples

data(wages)

## data transformation to include lagged series
mywages <- cbind(wages, lag(wages[,2], k = -1), lag(wages[,2], k = -2))
colnames(mywages) <- c(colnames(wages), "CPI2", "CPI3")
mywages <- window(mywages, start=1962, end=1979)

## page 142, fit Nichols OLS model
## equation (6.10)

modelNichols <- w ~ CPI + CPI2 + CPI3 + u + mw
lm(modelNichols, data = mywages)
#> 
#> Call:
#> lm(formula = modelNichols, data = mywages)
#> 
#> Coefficients:
#> (Intercept)          CPI         CPI2         CPI3            u           mw  
#>     4.27536      0.51882      0.12133      0.21404     -0.48786      0.03164  
#> 

## page 143, fit test statistics in table 6.11
##############################################

if(require(strucchange, quietly = TRUE)) {
## Chow 1972
sctest(modelNichols, point=c(1971,1), data=mywages, type="Chow") }
#> 
#> 	Chow test
#> 
#> data:  modelNichols
#> F = 1.5372, p-value = 0.3074
#> 

## Breusch-Pagan
bptest(modelNichols, data=mywages, studentize=FALSE)
#> 
#> 	Breusch-Pagan test
#> 
#> data:  modelNichols
#> BP = 3.6043, df = 5, p-value = 0.6077
#> 
bptest(modelNichols, data=mywages)
#> 
#> 	studentized Breusch-Pagan test
#> 
#> data:  modelNichols
#> BP = 2.5505, df = 5, p-value = 0.7689
#> 

## RESET (a)-(b)
reset(modelNichols, data=mywages)
#> 
#> 	RESET test
#> 
#> data:  modelNichols
#> RESET = 0.86419, df1 = 2, df2 = 10, p-value = 0.4506
#> 
reset(modelNichols, power=2, type="regressor", data=mywages)
#> 
#> 	RESET test
#> 
#> data:  modelNichols
#> RESET = 8.3265, df1 = 5, df2 = 7, p-value = 0.00735
#> 

## Harvey-Collier
harvtest(modelNichols, order.by = ~ CPI, data=mywages)
#> 
#> 	Harvey-Collier test
#> 
#> data:  modelNichols
#> HC = 2.0179, df = 11, p-value = 0.06866
#> 
harvtest(modelNichols, order.by = ~ CPI2, data=mywages)
#> 
#> 	Harvey-Collier test
#> 
#> data:  modelNichols
#> HC = 4.1448, df = 11, p-value = 0.001631
#> 
harvtest(modelNichols, order.by = ~ CPI3, data=mywages)
#> 
#> 	Harvey-Collier test
#> 
#> data:  modelNichols
#> HC = 2.2039, df = 11, p-value = 0.04975
#> 
harvtest(modelNichols, order.by = ~ u, data=mywages)
#> 
#> 	Harvey-Collier test
#> 
#> data:  modelNichols
#> HC = 0.20839, df = 11, p-value = 0.8387
#> 

## Rainbow
raintest(modelNichols, order.by = "mahalanobis", data=mywages)
#> 
#> 	Rainbow test
#> 
#> data:  modelNichols
#> Rain = 0.61074, df1 = 9, df2 = 3, p-value = 0.7512
#>