U.S. macroeconomic time series (1948–1996) with inflation and unemployment
rates plus lags and first differences. This is the same dataset used in
Stata's ivreg2 help file for HAC and AC examples, originally from
Wooldridge (2020).
Format
A data frame with 49 observations and 11 variables:
- year
Calendar year (1948–1996).
- inf
Inflation rate (percent).
- unem
Unemployment rate (percent).
- cinf
Change in inflation (
inf - inf_1).- cunem
Change in unemployment (
unem - unem_1).- unem_1
Lagged unemployment (one year).
- inf_1
Lagged inflation (one year).
- unem_2
Lagged unemployment (two years).
- inf_2
Lagged inflation (two years).
- cinf_1
Lagged change in inflation.
- cunem_1
Lagged change in unemployment.
Source
Wooldridge, J.M. (2020). Introductory Econometrics: A Modern Approach, 7th ed. Cengage Learning. The underlying series are from the Economic Report of the President (a U.S. government work).
Downloaded from http://fmwww.bc.edu/ec-p/data/wooldridge/phillips.dta.
Redistribution basis: system.file("COPYRIGHTS", package = "ivreg2r").
Examples
data(phillips)
# OLS Phillips curve with AC standard errors: exact replication of the
# Stata ivreg2 help-file example at line 1501 (`ivreg2 cinf unem, bw(3)`;
# Bartlett is Stata's default kernel). Without vcov = "robust", a kernel
# gives AC (autocorrelation-consistent) inference, not HAC.
fit_ac <- ivreg2(cinf ~ unem, data = phillips,
kernel = "bartlett", bw = 3, tvar = "year")
summary(fit_ac)
#>
#> OLS Estimation
#>
#> Call:
#> ivreg2(formula = cinf ~ unem, data = phillips, kernel = "bartlett",
#> bw = 3, tvar = "year")
#>
#> Observations: 48
#> VCV type: AC (kernel=Bartlett; bandwidth=3)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 3.0306 1.2230 2.478 0.01321 *
#> unem -0.5426 0.2054 -2.642 0.00825 **
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> ---
#> R-squared: 0.1078
#> Adj. R-squared: 0.0884
#> Wald chi2(1): 6.7 (p = 0.0129)
#> Root MSE: 2.3992
#>
# Adding vcov = "robust" gives HAC (Newey-West), replicating the
# help-file example at line 1511.
fit_hac <- ivreg2(cinf ~ unem, data = phillips, vcov = "robust",
kernel = "bartlett", bw = 3, tvar = "year",
small = TRUE)
summary(fit_hac)
#>
#> OLS Estimation
#>
#> Call:
#> ivreg2(formula = cinf ~ unem, data = phillips, vcov = "robust",
#> small = TRUE, kernel = "bartlett", bw = 3, tvar = "year")
#>
#> Observations: 48
#> VCV type: HAC (kernel=Bartlett; bandwidth=3), small-sample corrected
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 3.0306 1.3895 2.181 0.0343 *
#> unem -0.5426 0.2215 -2.450 0.0182 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> ---
#> R-squared: 0.1078
#> Adj. R-squared: 0.0884
#> F(1, 46): 6.0 (p = 0.0182)
#> Root MSE: 2.4508
#>