R: UK Phillips Curve Equation Data

PhillipsCurve {strucchange} | R Documentation

## UK Phillips Curve Equation Data

### Description

Macroeconomic time series from the United Kingdom with variables for estimating the Phillips curve equation.

### Usage

    data("PhillipsCurve")

### Format

A multivariate annual time series from 1857 to 1987 with the columns

p

  Logarithm of the consumer price index,

w

  Logarithm of nominal wages,

u

  Unemployment rate,

dp

  First differences of `p`,

dw

  First differences of `w`,

du

  First differences of `u`

u1

  Lag 1 of `u`,

dp1

  Lag 1 of `dp`.

### Source

The data is available online in the data archive of the Journal of Applied Econometrics http://qed.econ.queensu.ca/jae/2003-v18.1/bai-perron/.

### References

Alogoskoufis G.S., Smith R. (1991), The Phillips Curve, the Persistence of Inflation, and the Lucas Critique: Evidence from Exchange Rate Regimes, American Economic Review, 81, 1254-1275.

Bai J., Perron P. (2003), Computation and Analysis of Multiple Structural Change Models, Journal of Applied Econometrics, 18, 1-22.

### Examples

    ## load and plot data
    data("PhillipsCurve")
    uk <- window(PhillipsCurve, start = 1948)
    plot(uk[, "dp"])

    ## AR(1) inflation model
    ## estimate breakpoints
    bp.inf <- breakpoints(dp ~ dp1, data = uk, h = 8)
    plot(bp.inf)
    summary(bp.inf)

    ## fit segmented model with three breaks
    fac.inf <- breakfactor(bp.inf, breaks = 2, label = "seg")
    fm.inf <- lm(dp ~ 0 + fac.inf/dp1, data = uk)
    summary(fm.inf)

    ## Results from Table 2 in Bai & Perron (2003):
    ## coefficient estimates
    coef(bp.inf, breaks = 2)
    ## corresponding standard errors
    sqrt(sapply(vcov(bp.inf, breaks = 2), diag))
    ## breakpoints and confidence intervals
    confint(bp.inf, breaks = 2)

    ## Phillips curve equation
    ## estimate breakpoints
    bp.pc <- breakpoints(dw ~ dp1 + du + u1, data = uk, h = 5, breaks = 5)
    ## look at RSS and BIC
    plot(bp.pc)
    summary(bp.pc)

    ## fit segmented model with three breaks
    fac.pc <- breakfactor(bp.pc, breaks = 2, label = "seg")
    fm.pc <- lm(dw ~ 0 + fac.pc/dp1 + du + u1, data = uk)
    summary(fm.pc)

    ## Results from Table 3 in Bai & Perron (2003):
    ## coefficient estimates
    coef(fm.pc)
    ## corresponding standard errors
    sqrt(diag(vcov(fm.pc)))
    ## breakpoints and confidence intervals
    confint(bp.pc, breaks = 2, het.err = FALSE)

[Package strucchange version 1.5-2 Index]