Running 'strucchange-tests.R' [164s]
Running the tests in 'tests/strucchange-tests.R' failed.
Complete output:
  > library("strucchange")
  Loading required package: zoo
  
  Attaching package: 'zoo'
  
  The following objects are masked from 'package:base':
  
      as.Date, as.Date.numeric
  
  Loading required package: sandwich
  > 
  > ## Nile data
  > data("Nile")
  > 
  > Nile.rcus <- efp(Nile ~ 1, type = "Rec-CUSUM")
  > Nile.ocus <- efp(Nile ~ 1, type = "OLS-CUSUM")
  > Nile.re <- efp(Nile ~ 1, type = "RE")
  > Nile.rmos <- efp(Nile ~ 1, type = "Rec-MOSUM", h = 0.5)
  > Nile.omos <- efp(Nile ~ 1, type = "OLS-MOSUM", h = 0.5)
  > Nile.me <- efp(Nile ~ 1, type = "ME", h = 0.5)
  > sctest(Nile.rcus)
  
  	Recursive CUSUM test
  
  data:  Nile.rcus
  S = 2.0669, p-value = 7.487e-08
  
  > sctest(Nile.ocus)
  
  	OLS-based CUSUM test
  
  data:  Nile.ocus
  S0 = 2.9518, p-value = 5.409e-08
  
  > sctest(Nile.ocus, alt = TRUE)
  
  	OLS-based CUSUM test with alternative boundaries
  
  data:  Nile.ocus
  S0 = 6.5741, p-value = 1e-04
  
  > sctest(Nile.re)
  
  	RE test (recursive estimates test)
  
  data:  Nile.re
  RE = 2.9518, p-value = 5.409e-08
  
  > sctest(Nile.re, alt = TRUE)
  
  	RE test (recursive estimates test) with alternative boundaries
  
  data:  Nile.re
  RE = 6.5741, p-value = 1e-04
  
  > sctest(Nile.re, fun = "range")
  
  	RE test (recursive estimates test) with range norm
  
  data:  Nile.re
  RE = 2.9518, p-value = 1.831e-06
  
  > sctest(Nile.rmos)
  
  	Recursive MOSUM test
  
  data:  Nile.rmos
  M = 5.097, p-value = 0.01
  
  > sctest(Nile.omos)
  
  	OLS-based MOSUM test
  
  data:  Nile.omos
  M0 = 2.4237, p-value = 0.01
  
  > sctest(Nile.omos, fun = "range")
  
  	OLS-based MOSUM test with range norm
  
  data:  Nile.omos
  M0 = 4.3433, p-value = 5.615e-05
  
  > sctest(Nile.me)
  
  	ME test (moving estimates test)
  
  data:  Nile.me
  ME = 2.4237, p-value = 0.01
  
  > sctest(Nile.me, fun = "range")
  
  	ME test (moving estimates test) with range norm
  
  data:  Nile.me
  ME = 4.3433, p-value = 5.615e-05
  
  > 
  > Nile.fs <- Fstats(Nile ~ 1)
  > sctest(Nile.fs, type = "supF")
  
  	supF test
  
  data:  Nile.fs
  sup.F = 75.93, p-value = 2.22e-16
  
  > sctest(Nile.fs, type = "aveF")
  
  	aveF test
  
  data:  Nile.fs
  ave.F = 21.215, p-value < 2.2e-16
  
  > sctest(Nile.fs, type = "expF")
  
  	expF test
  
  data:  Nile.fs
  exp.F = 33.759, p-value < 2.2e-16
  
  > breakpoints(Nile.fs)
  
  	 Optimal 2-segment partition: 
  
  Call:
  breakpoints.Fstats(obj = Nile.fs)
  
  Breakpoints at observation number:
  28 
  
  Corresponding to breakdates:
  1898 
  > 
  > ## Seatbelt data
  > data("UKDriverDeaths")
  > seatbelt <- log10(UKDriverDeaths)
  > seatbelt <- cbind(seatbelt, lag(seatbelt, k = -1), lag(seatbelt, k = -12))
  > colnames(seatbelt) <- c("y", "ylag1", "ylag12")
  > seatbelt <- window(seatbelt, start = c(1970, 1), end = c(1984,12))
  > 
  > seat.rcus <- efp(y ~ ylag1 + ylag12, data = seatbelt, type = "Rec-CUSUM")
  > seat.ocus <- efp(y ~ ylag1 + ylag12, data = seatbelt, type = "OLS-CUSUM")
  > seat.re <- efp(y ~ ylag1 + ylag12, data = seatbelt, type = "RE")
  > seat.rmos <- efp(y ~ ylag1 + ylag12, data = seatbelt, type = "Rec-MOSUM", h = 0.5)
  > seat.omos <- efp(y ~ ylag1 + ylag12, data = seatbelt, type = "OLS-MOSUM", h = 0.5)
  > seat.me <- efp(y ~ ylag1 + ylag12, data = seatbelt, type = "ME", h = 0.5)
  > sctest(seat.rcus)
  
  	Recursive CUSUM test
  
  data:  seat.rcus
  S = 1.1599, p-value = 0.008572
  
  > sctest(seat.ocus)
  
  	OLS-based CUSUM test
  
  data:  seat.ocus
  S0 = 1.4866, p-value = 0.02407
  
  > sctest(seat.ocus, alt = TRUE)
  
  	OLS-based CUSUM test with alternative boundaries
  
  data:  seat.ocus
  S0 = 3.4082, p-value = 0.04502
  
  > sctest(seat.re)
  
  	RE test (recursive estimates test)
  
  data:  seat.re
  RE = 1.6311, p-value = 0.02904
  
  > sctest(seat.re, alt = TRUE)
  
  	RE test (recursive estimates test) with alternative boundaries
  
  data:  seat.re
  RE = 4.7365, p-value = 3e-04
  
  > sctest(seat.re, fun = "range")
  
  	RE test (recursive estimates test) with range norm
  
  data:  seat.re
  RE = 1.9303, p-value = 0.04762
  
  > sctest(seat.rmos)
  
  	Recursive MOSUM test
  
  data:  seat.rmos
  M = 2.0539, p-value = 0.04259
  
  > sctest(seat.omos)
  
  	OLS-based MOSUM test
  
  data:  seat.omos
  M0 = 0.91616, p-value = 0.4004
  
  > sctest(seat.omos, fun = "range")
  
  	OLS-based MOSUM test with range norm
  
  data:  seat.omos
  M0 = 1.4949, p-value = 0.5175
  
  > sctest(seat.me)
  
  	ME test (moving estimates test)
  
  data:  seat.me
  ME = 1.7107, p-value = 0.04712
  
  > sctest(seat.me, fun = "range")
  
  	ME test (moving estimates test) with range norm
  
  data:  seat.me
  ME = 2.1956, p-value = 0.3007
  
  > 
  > seat.fs <- Fstats(y ~ ylag1 + ylag12, data = seatbelt, from = 0.1)
  > sctest(seat.fs, type = "supF")
  
  	supF test
  
  data:  seat.fs
  sup.F = 19.333, p-value = 0.006721
  
  > sctest(seat.fs, type = "aveF")
  
  	aveF test
  
  data:  seat.fs
  ave.F = 7.458, p-value = 0.01461
  
  > sctest(seat.fs, type = "expF")
  
  	expF test
  
  data:  seat.fs
  exp.F = 6.4247, p-value = 0.008093
  
  > breakpoints(seat.fs)
  
  	 Optimal 2-segment partition: 
  
  Call:
  breakpoints.Fstats(obj = seat.fs)
  
  Breakpoints at observation number:
  46 
  
  Corresponding to breakdates:
  1973(10) 
  > 
  > ## German M1 data
  > data("GermanM1")
  > LTW.model <- dm ~ dy2 + dR + dR1 + dp + m1 + y1 + R1 + season
  > M1.model <- dm ~ dy2 + dR + dR1 + dp + ecm.res + season
  > 
  > M1.ocus <- efp(LTW.model, data = GermanM1, type = "OLS-CUSUM")
  > M1.re <- efp(LTW.model, data = GermanM1, type = "RE")
  > M1.fs <- Fstats(LTW.model, data = GermanM1, from = 0.1)
  > sctest(M1.ocus)
  
  	OLS-based CUSUM test
  
  data:  M1.ocus
  S0 = 0.50881, p-value = 0.958
  
  > sctest(M1.ocus, alt = TRUE)
  
  	OLS-based CUSUM test with alternative boundaries
  
  data:  M1.ocus
  S0 = 1.6288, p-value = 0.9704
  
  > sctest(M1.re)
  
  	RE test (recursive estimates test)
  
  data:  M1.re
  RE = 1.9821, p-value = 0.008475
  
  > sctest(M1.re, fun = "range")
  
  	RE test (recursive estimates test) with range norm
  
  data:  M1.re
  RE = 2.2386, p-value = 0.01844
  
  > sctest(M1.re, alt = TRUE)
  
  	RE test (recursive estimates test) with alternative boundaries
  
  data:  M1.re
  RE = 5.4937, p-value = 0.001099
  
  > sctest(M1.fs, type = "supF")
  
  	supF test
  
  data:  M1.fs
  sup.F = 108.83, p-value = 4.441e-16
  
  > sctest(M1.fs, type = "aveF")
  
  	aveF test
  
  data:  M1.fs
  ave.F = 35.551, p-value = 2.007e-07
  
  > sctest(M1.fs, type = "expF")
  
  	expF test
  
  data:  M1.fs
  exp.F = 49.74, p-value < 2.2e-16
  
  > 
  > M1 <- historyM1
  > ols.efp <- efp(M1.model, type = "OLS-CUSUM", data = M1)
  > newborder <- function(k) 1.5778*k/118
  > ols.mefp <- mefp(ols.efp, period = 2)
  > ols.mefp2 <- mefp(ols.efp, border = newborder)
  > M1 <- GermanM1
  > ols.mon <- monitor(ols.mefp)
  Break detected at observation # 128 
  > ols.mon2 <- monitor(ols.mefp2)
  Break detected at observation # 135 
  > ols.mon
  Monitoring with OLS-based CUSUM test 
  
  Initial call:
    mefp.efp(obj = ols.efp, period = 2) 
  
  Last call:
    monitor(obj = ols.mefp) 
  
  Significance level   :  0.05 
  Critical value       :  2.795483 
  History size         :  118 
  Last point evaluated :  140 
  Structural break at  :  128 
  
  Parameter estimate on history :
  (Intercept)         dy2          dR         dR1          dp     ecm.res 
  -0.05025441 -0.29729418 -0.67278600 -0.99950033 -0.52786586  1.00000000 
     seasonQ1    seasonQ2    seasonQ3 
  -0.13330148 -0.01559447 -0.10906796 
  > ols.mon2
  Monitoring with OLS-based CUSUM test 
  
  Initial call:
    mefp.efp(obj = ols.efp, border = newborder) 
  
  Last call:
    monitor(obj = ols.mefp2) 
  
  Significance level   :  0.05 
  Critical value       :  2.795483 
  History size         :  118 
  Last point evaluated :  140 
  Structural break at  :  135 
  
  Parameter estimate on history :
  (Intercept)         dy2          dR         dR1          dp     ecm.res 
  -0.05025441 -0.29729418 -0.67278600 -0.99950033 -0.52786586  1.00000000 
     seasonQ1    seasonQ2    seasonQ3 
  -0.13330148 -0.01559447 -0.10906796 
  > 
  > ## Grossarl data
  > data("Grossarl")
  > Grossarl.bp <- breakpoints(fraction ~ 1, data = Grossarl, h = 0.1)
  > summary(Grossarl.bp)
  
  	 Optimal (m+1)-segment partition: 
  
  Call:
  breakpoints.formula(formula = fraction ~ 1, h = 0.1, data = Grossarl)
  
  Breakpoints at observation number:
                                      
  m = 1                127            
  m = 2      55        122            
  m = 3      55        124         180
  m = 4      55        122     157 179
  m = 5      54 86     122     157 179
  m = 6   35 55 86     122     157 179
  m = 7   35 55 80 101 122     157 179
  m = 8   35 55 79 99  119 139 159 179
  
  Corresponding to breakdates:
                                                 
  m = 1                       1826               
  m = 2        1754           1821               
  m = 3        1754           1823           1879
  m = 4        1754           1821      1856 1878
  m = 5        1753 1785      1821      1856 1878
  m = 6   1734 1754 1785      1821      1856 1878
  m = 7   1734 1754 1779 1800 1821      1856 1878
  m = 8   1734 1754 1778 1798 1818 1838 1858 1878
  
  Fit:
                                                                           
  m   0         1         2         3         4         5         6        
  RSS    1.1088    0.8756    0.6854    0.6587    0.6279    0.6019    0.5917
  BIC -460.8402 -497.4625 -535.8459 -533.1857 -532.1789 -530.0501 -522.8510
                         
  m   7         8        
  RSS    0.5934    0.6084
  BIC -511.7017 -496.0924
  > confint(Grossarl.bp)
  
  	 Confidence intervals for breakpoints
  	 of optimal 3-segment partition: 
  
  Call:
  confint.breakpointsfull(object = Grossarl.bp)
  
  Breakpoints at observation number:
    2.5 % breakpoints 97.5 %
  1    52          55     61
  2   116         122    124
  
  Corresponding to breakdates:
    2.5 % breakpoints 97.5 %
  1  1751        1754   1760
  2  1815        1821   1823
  > 
  > 
  > proc.time()
     user  system elapsed 
     1.03    0.09    1.10