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Indicator Saturation for Panel Data

Source: R/isatpanel.R
isatpanel.Rd

This function is essentially a wrapper function around the gets::isat() function from the gets package. This function allows the running of various different indicator saturation techniques that can, for example, be used to answer reverse causal questions. Indicator Saturation techniques fully saturate a model with indicators (for example dummy-indicators or step-indicators) and then use an automated block-search algorithm to retain only relevant indicators that improve the model (based on a chosen information criterion).

Usage

isatpanel(
  data = NULL,
  formula = NULL,
  index = NULL,
  effect = c("twoways"),
  na.remove = TRUE,
  engine = NULL,
  user.estimator = NULL,
  cluster = "none",
  ar = 0,
  iis = FALSE,
  jiis = FALSE,
  jsis = FALSE,
  fesis = FALSE,
  tis = FALSE,
  csis = FALSE,
  cfesis = FALSE,
  fesis_id = NULL,
  fesis_time = NULL,
  tis_id = NULL,
  tis_time = NULL,
  csis_var = NULL,
  csis_time = NULL,
  cfesis_var = NULL,
  cfesis_id = NULL,
  cfesis_time = NULL,
  uis = NULL,
  t.pval = 0.001,
  plot = TRUE,
  print.searchinfo = TRUE,
  plm_model = "within",
  y = NULL,
  id = NULL,
  time = NULL,
  mxreg = NULL,
  ...
)

Arguments

data

The input data.frame object.

formula

Formula argument. The dependent variable will be the left-most element, separated by a ~ symbol from the remaining regressors (e.g. y ~ x + z). Note the intercept will always be removed unless the effect is "none" - this means that if any fixed effects are specified, the intercept will always be removed.

index

Specify the name of the group and time column in the format c("id", "time").

effect

Fixed Effect specification. Possible arguments: "twoways" (Default), "individual", "time", or "none".

na.remove

remove NAs

engine

Estimation function to use. Default is NULL, which uses the default estimation procedure of the gets package. Alternatives are "fixest", "plm", or "felm".

user.estimator

Use a user.estimator

cluster

cluster Standard Errors at this level. Default is "none". Possible values are: "individual", "time", or "twoways".

ar

Autoregressive Term to be included. default is 0.

iis

Logical. Use Impulse Indicator Saturation.

jiis

Logical. Use Joint Impulse Indicator Saturation (Outliers are common across all units). This is essentially just a time fixed effect, but this allows selection of FE.

jsis

Logical. Use Join Step Indicator Saturation (steps are common across all units). Will only be retained if time fixed effects are not included (i.e. effect = 'none' or 'individual'), as they are collinear otherwise.

fesis

Logical. Use Fixed Effect Step Indicator Saturation. Constructed by multiplying a constant (1) with group Fixed Effects. Default is FALSE.

tis

Logical. Use Trend Indicator Saturation. Constructed by fitting a trend for each unit from every observation. Default is FALSE.

csis

Logical. Use Coefficient Step Indicator Saturation. Constructed by Default is FALSE.

cfesis

Logical. Use Coefficient-Fixed Effect Indicator Saturation. Default is FALSE.

fesis_id

The FESIS method can be conducted for all (default) individuals/units (i.e. looking for breaks in individual countries) or just a subset of them. If you want to use a subset, specify the individuals/units for which you want to test the stability of the fixed effect in a character vector.

fesis_time

The FESIS method can be conducted for all (default) time periods (i.e. looking for Fixed Effect Step-shifts at every time period) or just a subset of them. If you want to use a subset, specify the time periods as a numeric vector (for all id's the same like 1:10) or as a list with an equal number of elements as there are id's e.g. list(A = 1:10, B = NULL, C = 5:10).

tis_id

The TIS method can be conducted for all (default) individuals/units (i.e. looking for trends in individual countries) or just a subset of them. If you want to use a subset, specify the individuals/units for which you want to test the trend in a character vector.

tis_time

The TIS method can be conducted for all (default) time periods (i.e. looking for trends at every time period) or just a subset of them. If you want to use a subset, specify the time periods as a numeric vector (for all id's the same like 1:10) or as a list with an equal number of elements as there are id's e.g. list(A = 1:10, B = NULL, C = 5:10).

csis_var

The CSIS method can be conducted for all (default) variables or just a subset of them. If you want to use a subset, please specify the column names of the variable in a character vector.

csis_time

The CSIS method can be conducted for all (default) time periods (i.e. looking for Coefficient Step Shifts across all units at every time period) or just a subset of them. If you want to use a subset, specify the time periods as a numeric vector (e.g. 1:10).'

cfesis_var

The CFESIS method can be conducted for all variables (default) or just a subset of them. If you want to use a subset, please specify the column names of the variable in a character vector.

cfesis_id

The CFESIS method can be conducted for all individuals/units (default) or just a subset of them. If you want to use a subset, please specify the individuals/units to be tested in a character vector.

cfesis_time

The CFESIS method can be conducted for all (default) time periods (i.e. looking for Coefficient Step Shifts per unit at every time period) or just a subset of them. If you want to use a subset, specify the time periods as a numeric vector (for all id's the same like 1:10) or as a list with an equal number of elements as there are id's e.g. list(A = 1:10, B = NULL, C = 5:10).

uis

Matrix or List. This can be used to include a set of UIS (User Specified Indicators). Must be equal to the sample size (so it is recommended to use this only with datasets without NA values. Default is NULL. See the reference by Genaro Sucarrat (2020) below for an explanation of the UIS system.

t.pval

numeric value between 0 and 1. The significance level used for the two-sided regressor significance t-tests

plot

Logical. Should the final object be plotted? Default is TRUE. The output is a combination of plot() and plot_grid() using the cowplot package.

print.searchinfo

logical. If TRUE (default), then detailed information is printed.

plm_model

Type of PLM model (only if engine = "PLM")

y

Deprecated. The dependent variable. Can be used when data, index, and formula are not specified.

id

Deprecated. Can be used when data, index, and formula are not specified. Must be a vector of the grouping variable as a character or factor

time

Deprecated. Can be used when data, index, and formula are not specified. Must be a vector of the time variable as an integer or numeric.

mxreg

Deprecated.The co-variates matrix. Superseded by the formula argument.

...

Further arguments to gets::isat()

Value

A list with class 'isatpanel'.

References

Felix Pretis and Moritz Schwarz (2022). Discovering What Mattered: Answering Reverse Causal Questions by Detecting Unknown Treatment Assignment and Timing as Breaks in Panel Models. January 31, 2022. Available at SSRN: https://ssrn.com/abstract=4022745 or http://dx.doi.org/10.2139/ssrn.4022745

Genaro Sucarrat. User-Specified General-to-Specific and Indicator Saturation Methods, The R Journal (2020) 12:2, pages 388-401. Available at: https://journal.r-project.org/archive/2021/RJ-2021-024/index.html

See also

gets::isat()

Examples

# \donttest{
data(EU_emissions_road)

# Group specification
EU15 <- c("Austria", "Germany", "Denmark", "Spain", "Finland", "Belgium",
         "France", "United Kingdom", "Ireland", "Italy", "Luxembourg",
         "Netherlands", "Greece", "Portugal", "Sweden")

# Prepare sample and data
EU_emissions_road_short <- EU_emissions_road[
EU_emissions_road$country %in% EU15 &
EU_emissions_road$year >= 2000,
]

# Run
result <- isatpanel(
  data = EU_emissions_road_short,
  formula = ltransport.emissions ~ lgdp + I(lgdp^2) + lpop,
  index = c("country", "year"),
  effect = "twoways",
  fesis = TRUE,
  plot = FALSE,
  t.pval = 0.01
)
#> 
#> UIS1.fesis block 1 of 9:
#> 30 path(s) to search
#> Searching: 
#> 1 
#> 2 
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#> 
#> UIS1.fesis block 2 of 9:
#> 30 path(s) to search
#> Searching: 
#> 1 
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#> 
#> UIS1.fesis block 3 of 9:
#> 30 path(s) to search
#> Searching: 
#> 1 
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#> 
#> UIS1.fesis block 4 of 9:
#> 30 path(s) to search
#> Searching: 
#> 1 
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#> 
#> UIS1.fesis block 5 of 9:
#> 30 path(s) to search
#> Searching: 
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#> 
#> UIS1.fesis block 6 of 9:
#> 30 path(s) to search
#> Searching: 
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#> 
#> UIS1.fesis block 7 of 9:
#> 30 path(s) to search
#> Searching: 
#> 1 
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#> 
#> UIS1.fesis block 8 of 9:
#> 30 path(s) to search
#> Searching: 
#> 1 
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#> 
#> UIS1.fesis block 9 of 9:
#> 30 path(s) to search
#> Searching: 
#> 1 
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#> 
#> GETS of union of retained UIS1.fesis variables... 
#> 1 path(s) to search
#> Searching: 
#> 1 
#> 
#> GETS of union of ALL retained variables...
#> 1 path(s) to search
#> Searching: 
#> 1 
plot(result)

plot_grid(result)


# print the retained indicators
get_indicators(result)
#> $fesis
#>                                  id time                  name
#> 1.fesisAustria.2002         Austria 2002     fesisAustria.2002
#> 1.fesisAustria.2013         Austria 2013     fesisAustria.2013
#> 1.fesisGermany.2003         Germany 2003     fesisGermany.2003
#> 1.fesisGermany.2012         Germany 2012     fesisGermany.2012
#> 1.fesisGreece.2009           Greece 2009      fesisGreece.2009
#> 1.fesisIreland.2006         Ireland 2006     fesisIreland.2006
#> 1.fesisIreland.2010         Ireland 2010     fesisIreland.2010
#> 1.fesisIreland.2015         Ireland 2015     fesisIreland.2015
#> 1.fesisLuxembourg.2004   Luxembourg 2004  fesisLuxembourg.2004
#> 1.fesisLuxembourg.2015   Luxembourg 2015  fesisLuxembourg.2015
#> 1.fesisNetherlands.2014 Netherlands 2014 fesisNetherlands.2014
#> 1.fesisPortugal.2011       Portugal 2011    fesisPortugal.2011
#> 
# }