PhillipsCurve          package:strucchange          R Documentation

_U_K _P_h_i_l_l_i_p_s _C_u_r_v_e _E_q_u_a_t_i_o_n _D_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     data(PhillipsCurve)

_F_o_r_m_a_t:

     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,

     _d_p First differences of 'p',

     _d_w First differences of 'w',

     _d_u First differences of 'u'

     _u_1 Lag 1 of 'u',

     _d_p_1 Lag 1 of 'dp'.

_S_o_u_r_c_e:

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

_R_e_f_e_r_e_n_c_e_s:

     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.

_E_x_a_m_p_l_e_s:

     ## 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)

