Strike Durations

Data on the duration of strikes in US manufacturing industries, 1968–1976.

Usage

data("StrikeDuration")

Format

A data frame containing 62 observations on 2 variables for the period 1968–1976.

duration

strike duration in days.

uoutput

unanticipated output (a measure of unanticipated aggregate industrial production net of seasonal and trend components).

Details

The original data provided by Kennan (1985) are on a monthly basis, for the period 1968(1) through 1976(12). Greene (2003) only provides the June data for each year. Also, the duration for observation 36 is given as 3 by Greene while Kennan has 2. Here we use Greene's version.

uoutput is the residual from a regression of the logarithm of industrial production in manufacturing on time, time squared, and monthly dummy variables.

Source

Online complements to Greene (2003).

https://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm

References

Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.

Kennan, J. (1985). The Duration of Contract Strikes in US Manufacturing. Journal of Econometrics, 28, 5–28.

See also

Greene2003

Examples

data("StrikeDuration")
library("MASS")

## Greene (2003), Table 22.10
fit_exp <- fitdistr(StrikeDuration$duration, "exponential")
fit_wei <- fitdistr(StrikeDuration$duration, "weibull")
fit_wei$estimate[2]^(-1)
#>      scale 
#> 0.02432637 
fit_lnorm <- fitdistr(StrikeDuration$duration, "lognormal")
1/fit_lnorm$estimate[2]
#>     sdlog 
#> 0.7787838 
exp(-fit_lnorm$estimate[1])
#>   meanlog 
#> 0.0448489 
## Weibull and lognormal distribution have
## different parameterizations, see Greene p. 794

## Greene (2003), Example 22.10
library("survival")
fm_wei <- survreg(Surv(duration) ~ uoutput, dist = "weibull", data = StrikeDuration)
summary(fm_wei)
#> 
#> Call:
#> survreg(formula = Surv(duration) ~ uoutput, data = StrikeDuration, 
#>     dist = "weibull")
#>                Value Std. Error     z      p
#> (Intercept)  3.77977    0.13670 27.65 <2e-16
#> uoutput     -9.33220    2.93756 -3.18 0.0015
#> Log(scale)  -0.00783    0.10050 -0.08 0.9379
#> 
#> Scale= 0.992 
#> 
#> Weibull distribution
#> Loglik(model)= -289.8   Loglik(intercept only)= -294.4
#> 	Chisq= 9.28 on 1 degrees of freedom, p= 0.0023 
#> Number of Newton-Raphson Iterations: 6 
#> n= 62 
#>