The package is a panel adaptation of the gets package see here.
This code is being developed by Felix Pretis and Moritz Schwarz. The associated working paper is published under “Panel Break Detection: Detecting Unknown Treatment, Stability, Heterogeneity, and Outliers” by Pretis and Schwarz, which is available at SSRN here and was applied to a study by Nico Koch and colleagues on EU Road CO2 emissions, which was published in Nature Energy in 2022.
You can install the released version of getspanel from CRAN with:
install.packages("getspanel")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("moritzpschwarz/getspanel")
library(getspanel)
Warning: package 'getspanel' was built under R version 4.5.3
data("EU_emissions_road")
# let's subset a few countries to make this faster
subset <- c("Austria", "Belgium", "Germany", "Denmark", "Spain",
"France", "Greece", "Ireland", "Italy", "Netherlands", "Sweden", "United Kingdom")
EU_emissions_road <- EU_emissions_road[EU_emissions_road$country %in% subset, ]
is1 <- isatpanel(data = EU_emissions_road,
formula = ltransport.emissions ~ lgdp + lpop,
index = c("country","year"),
effect = "twoways",
fesis = TRUE,
print.searchinfo = FALSE # to save space we suppress the status information in the estimation
)
is1
Date: Wed Oct 7 10:14:17 2026
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 576
Sample: 1 to 576
SPECIFIC mean equation:
coef std.error t-stat p-value
lgdp 0.191984 0.048508 3.9578 8.665e-05 ***
lpop 1.661090 0.146926 11.3056 < 2.2e-16 ***
idBelgium -0.150522 0.032887 -4.5769 5.966e-06 ***
idDenmark 0.280623 0.057795 4.8555 1.610e-06 ***
idFrance -1.630424 0.234931 -6.9400 1.226e-11 ***
idGermany -1.976952 0.278236 -7.1053 4.178e-12 ***
idGreece -0.925506 0.039916 -23.1863 < 2.2e-16 ***
idIreland 0.652538 0.094434 6.9100 1.488e-11 ***
idItaly -1.952354 0.240815 -8.1073 4.048e-15 ***
idNetherlands -0.559940 0.071258 -7.8579 2.429e-14 ***
idSpain -1.817624 0.198185 -9.1714 < 2.2e-16 ***
idSweden 0.163796 0.017434 9.3951 < 2.2e-16 ***
idUnitedKingdom -1.694515 0.241823 -7.0073 7.932e-12 ***
time1971 -22.266234 1.712374 -13.0031 < 2.2e-16 ***
time1972 -22.209745 1.712719 -12.9675 < 2.2e-16 ***
time1973 -22.166745 1.712937 -12.9408 < 2.2e-16 ***
time1974 -22.234614 1.713418 -12.9768 < 2.2e-16 ***
time1975 -22.182121 1.714008 -12.9417 < 2.2e-16 ***
time1976 -22.114917 1.714213 -12.9009 < 2.2e-16 ***
time1977 -22.067555 1.714529 -12.8709 < 2.2e-16 ***
time1978 -22.008915 1.714788 -12.8348 < 2.2e-16 ***
time1979 -22.000682 1.715010 -12.8283 < 2.2e-16 ***
time1980 -22.019270 1.715388 -12.8363 < 2.2e-16 ***
time1981 -22.033913 1.715851 -12.8414 < 2.2e-16 ***
time1982 -22.031766 1.715724 -12.8411 < 2.2e-16 ***
time1983 -22.044801 1.714897 -12.8549 < 2.2e-16 ***
time1984 -22.026822 1.714883 -12.8445 < 2.2e-16 ***
time1985 -21.986600 1.713680 -12.8300 < 2.2e-16 ***
time1986 -21.937925 1.713782 -12.8009 < 2.2e-16 ***
time1987 -21.918101 1.713835 -12.7889 < 2.2e-16 ***
time1988 -21.886703 1.713439 -12.7736 < 2.2e-16 ***
time1989 -21.857159 1.713514 -12.7558 < 2.2e-16 ***
time1990 -21.825409 1.713532 -12.7371 < 2.2e-16 ***
time1991 -21.806648 1.714060 -12.7222 < 2.2e-16 ***
time1992 -21.783408 1.714627 -12.7045 < 2.2e-16 ***
time1993 -21.779781 1.715239 -12.6978 < 2.2e-16 ***
time1994 -21.785776 1.715018 -12.7029 < 2.2e-16 ***
time1995 -21.776892 1.715203 -12.6964 < 2.2e-16 ***
time1996 -21.771196 1.714708 -12.6967 < 2.2e-16 ***
time1997 -21.773890 1.714776 -12.6978 < 2.2e-16 ***
time1998 -21.736594 1.714849 -12.6755 < 2.2e-16 ***
time1999 -21.726690 1.714930 -12.6691 < 2.2e-16 ***
time2000 -21.714010 1.715121 -12.6603 < 2.2e-16 ***
time2001 -21.691526 1.715842 -12.6419 < 2.2e-16 ***
time2002 -21.701688 1.716518 -12.6429 < 2.2e-16 ***
time2003 -21.698606 1.717274 -12.6355 < 2.2e-16 ***
time2004 -21.694719 1.717785 -12.6295 < 2.2e-16 ***
time2005 -21.702992 1.718411 -12.6297 < 2.2e-16 ***
time2006 -21.708115 1.718955 -12.6287 < 2.2e-16 ***
time2007 -21.713375 1.719622 -12.6268 < 2.2e-16 ***
time2008 -21.755859 1.720512 -12.6450 < 2.2e-16 ***
time2009 -21.787691 1.721588 -12.6556 < 2.2e-16 ***
time2010 -21.815245 1.722005 -12.6685 < 2.2e-16 ***
time2011 -21.864707 1.722959 -12.6902 < 2.2e-16 ***
time2012 -21.925951 1.723473 -12.7220 < 2.2e-16 ***
time2013 -21.929240 1.723956 -12.7203 < 2.2e-16 ***
time2014 -21.933833 1.724324 -12.7202 < 2.2e-16 ***
time2015 -21.933998 1.724608 -12.7183 < 2.2e-16 ***
time2016 -21.929212 1.725103 -12.7118 < 2.2e-16 ***
time2017 -21.938441 1.725461 -12.7145 < 2.2e-16 ***
time2018 -21.951538 1.725811 -12.7195 < 2.2e-16 ***
fesisAustria.2002 0.217753 0.016521 13.1802 < 2.2e-16 ***
fesisGermany.2001 -0.182226 0.019828 -9.1902 < 2.2e-16 ***
fesisGermany.2011 0.192494 0.026512 7.2606 1.492e-12 ***
fesisSpain.1988 0.228834 0.025115 9.1115 < 2.2e-16 ***
fesisSpain.1994 0.109825 0.024273 4.5246 7.575e-06 ***
fesisFrance.1985 -0.099240 0.020099 -4.9377 1.081e-06 ***
fesisFrance.2003 -0.115748 0.018391 -6.2938 6.801e-10 ***
fesisUnitedKingdom.2000 -0.144022 0.016374 -8.7958 < 2.2e-16 ***
fesisGreece.1983 0.336377 0.024504 13.7273 < 2.2e-16 ***
fesisGreece.2003 0.161829 0.018121 8.9305 < 2.2e-16 ***
fesisIreland.1982 -0.129324 0.031035 -4.1671 3.637e-05 ***
fesisIreland.1986 -0.106019 0.030918 -3.4290 0.0006560 ***
fesisIreland.1996 0.285148 0.028979 9.8397 < 2.2e-16 ***
fesisItaly.1982 0.159975 0.018763 8.5259 < 2.2e-16 ***
fesisNetherlands.1985 -0.131479 0.019114 -6.8785 1.821e-11 ***
fesisSweden.1990 -0.172648 0.026939 -6.4088 3.404e-10 ***
fesisSweden.1995 -0.090674 0.025941 -3.4954 0.0005157 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 255.88 1 < 2.2e-16 ***
Ljung-Box ARCH(1) 124.08 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
SE of regression 0.04926
R-squared 0.99815
Log-lik.(n=576) 955.86760
plot(is1)
Let’s explore the other plots that we can use:
plot_grid(is1)
We can plot the counterfactuals as well:
plot_counterfactual(is1, plus_t = 5)
We can plot the residuals against an OLS model:
plot_residuals(is1)
An example using coefficient step indicator saturation and impulse indicator saturation:
is2 <- isatpanel(data = EU_emissions_road,
formula = ltransport.emissions ~ lgdp + lpop,
index = c("country","year"),
effect = "twoways",
csis = TRUE,
iis = TRUE,
print.searchinfo = FALSE # to save space we suppress the status information in the estimation
)
is2
Date: Wed Oct 7 10:16:38 2026
Dependent var.: y
Method: Ordinary Least Squares (OLS)
Variance-Covariance: Ordinary
No. of observations (mean eq.): 576
Sample: 1 to 576
SPECIFIC mean equation:
coef std.error t-stat p-value
lgdp 0.480076 0.057505 8.3484 6.688e-16 ***
lpop 1.102095 0.201437 5.4712 7.048e-08 ***
idBelgium -0.145658 0.047862 -3.0433 0.0024618 **
idDenmark 0.010816 0.079617 0.1359 0.8919901
idFrance -1.249756 0.328865 -3.8002 0.0001622 ***
idGermany -1.443445 0.376986 -3.8289 0.0001449 ***
idGreece -0.371161 0.071474 -5.1929 3.007e-07 ***
idIreland 0.386181 0.116254 3.3219 0.0009589 ***
idItaly -1.321369 0.326088 -4.0522 5.874e-05 ***
idNetherlands -0.578138 0.105033 -5.5044 5.903e-08 ***
idSpain -1.128038 0.279220 -4.0400 6.178e-05 ***
idSweden -0.070651 0.024952 -2.8314 0.0048189 **
idUnitedKingdom -1.238038 0.330535 -3.7456 0.0002007 ***
time1971 -20.768078 2.256368 -9.2042 < 2.2e-16 ***
time1972 -20.728114 2.256497 -9.1860 < 2.2e-16 ***
time1973 -20.693515 2.256352 -9.1712 < 2.2e-16 ***
time1974 -20.758251 2.256698 -9.1985 < 2.2e-16 ***
time1975 -20.700178 2.257571 -9.1692 < 2.2e-16 ***
time1976 -20.657978 2.257342 -9.1515 < 2.2e-16 ***
time1977 -20.620444 2.257401 -9.1346 < 2.2e-16 ***
time1978 -20.571088 2.257415 -9.1127 < 2.2e-16 ***
time1979 -20.568553 2.257282 -9.1121 < 2.2e-16 ***
time1980 -20.592616 2.257574 -9.1216 < 2.2e-16 ***
time1981 -20.637430 2.257528 -9.1416 < 2.2e-16 ***
time1982 -20.634405 2.257739 -9.1394 < 2.2e-16 ***
time1983 -20.622710 2.257725 -9.1343 < 2.2e-16 ***
time1984 -20.611186 2.257451 -9.1303 < 2.2e-16 ***
time1985 -20.596647 2.257167 -9.1250 < 2.2e-16 ***
time1986 -20.562357 2.256992 -9.1105 < 2.2e-16 ***
time1987 -20.548213 2.256834 -9.1049 < 2.2e-16 ***
time1988 -20.507389 2.256370 -9.0887 < 2.2e-16 ***
time1989 -20.486237 2.256133 -9.0802 < 2.2e-16 ***
time1990 -20.474918 2.256273 -9.0747 < 2.2e-16 ***
time1991 -20.458219 2.256869 -9.0649 < 2.2e-16 ***
time1992 -20.435620 2.257574 -9.0520 < 2.2e-16 ***
time1993 -20.428974 2.258481 -9.0454 < 2.2e-16 ***
time1994 -20.432759 2.258439 -9.0473 < 2.2e-16 ***
time1995 -20.438540 2.258330 -9.0503 < 2.2e-16 ***
time1996 -20.414389 2.258354 -9.0395 < 2.2e-16 ***
time1997 -20.425825 2.258092 -9.0456 < 2.2e-16 ***
time1998 -20.397145 2.257843 -9.0339 < 2.2e-16 ***
time1999 -20.396400 2.257580 -9.0346 < 2.2e-16 ***
time2000 -20.405635 2.257344 -9.0397 < 2.2e-16 ***
time2001 -20.402260 2.257728 -9.0366 < 2.2e-16 ***
time2002 -20.396216 2.258378 -9.0314 < 2.2e-16 ***
time2003 -20.390618 2.259102 -9.0260 < 2.2e-16 ***
time2004 -20.392060 2.259545 -9.0249 < 2.2e-16 ***
time2005 -20.403177 2.260237 -9.0270 < 2.2e-16 ***
time2006 -20.414627 2.260682 -9.0303 < 2.2e-16 ***
time2007 -20.424333 2.261361 -9.0319 < 2.2e-16 ***
time2008 -20.462583 2.262676 -9.0435 < 2.2e-16 ***
time2009 -20.479077 2.264676 -9.0428 < 2.2e-16 ***
time2010 -20.508758 2.265126 -9.0541 < 2.2e-16 ***
time2011 -20.542655 2.265483 -9.0677 < 2.2e-16 ***
time2012 -20.599286 2.266329 -9.0893 < 2.2e-16 ***
time2013 -20.600458 2.267034 -9.0870 < 2.2e-16 ***
time2014 -20.608280 2.267379 -9.0890 < 2.2e-16 ***
time2015 -20.616047 2.267439 -9.0922 < 2.2e-16 ***
time2016 -20.613900 2.267971 -9.0891 < 2.2e-16 ***
time2017 -20.628101 2.268232 -9.0944 < 2.2e-16 ***
time2018 -20.645403 2.268513 -9.1009 < 2.2e-16 ***
iis337 -0.571395 0.110397 -5.1758 3.280e-07 ***
iis338 -0.502695 0.110864 -4.5343 7.225e-06 ***
iis339 -0.555516 0.111172 -4.9969 8.043e-07 ***
iis340 -0.627122 0.110357 -5.6827 2.244e-08 ***
iis341 -0.675722 0.110746 -6.1016 2.093e-09 ***
iis342 -0.489231 0.110602 -4.4234 1.191e-05 ***
iis343 -0.435945 0.110332 -3.9512 8.882e-05 ***
iis344 -0.404908 0.110429 -3.6667 0.0002718 ***
iis345 -0.422950 0.110172 -3.8390 0.0001392 ***
iis346 -0.382619 0.109944 -3.4801 0.0005446 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Diagnostics and fit:
Chi-sq df p-value
Ljung-Box AR(1) 475.51 1 < 2.2e-16 ***
Ljung-Box ARCH(1) 336.05 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
SE of regression 0.10295
R-squared 0.99181
Log-lik.(n=576) 527.71075
plot(is2)
plot_grid(is2)
and an example of Coefficient Fixed-Effect Step indicator saturation:
is3 <- isatpanel(data = EU_emissions_road,
formula = ltransport.emissions ~ lgdp + lpop,
index = c("country","year"),
effect = "twoways",
cfesis = TRUE,
print.searchinfo = FALSE # to save space we suppress the status information in the estimation
)
is3
plot(is3)
We can also use e.g. the fixest package to estimate our models:
is4 <- isatpanel(data = EU_emissions_road,
formula = ltransport.emissions ~ lgdp + lpop,
index = c("country","year"),
effect = "twoways",
engine = "fixest",
fesis = TRUE,
print.searchinfo = FALSE # to save space we suppress the status information in the estimation
)
plot(is4)
