Package 'getspanel' reference manual

Title:	General-to-Specific Modelling of Panel Data
Description:	Uses several types of indicator saturation and automated General-to-Specific (GETS) modelling from the 'gets' package and applies it to panel data. This allows the detection of structural breaks in panel data, operationalising a reverse causal approach of causal inference, see Pretis and Schwarz (2022) <doi:10.2139/ssrn.4022745>.
Authors:	Felix Pretis [aut], Moritz Schwarz [aut, cre]
Maintainer:	Moritz Schwarz <[email protected]>
License:	MIT + file LICENSE
Version:	0.2.0
Built:	2024-11-14 05:01:42 UTC
Source:	https://github.com/moritzpschwarz/getspanel

Estimate Breakdate Uncertainty

Description

Estimate Breakdate Uncertainty

Usage

break_uncertainty(x, m = 15, interval = 0.99)
break_uncertainty(x, m = 15, interval = 0.99)

Arguments

`x`	An object produced by the isatpanel function
`m`	Maximum range of interval (default is 15 time periods).
`interval`	Approximate level of interval. CI level will be at least > interval. Default 0.99 is a 99% CI, so the time interval will always be the integer that results in at least > 99% coverage.

Value

A data.frame that indicates the uncertainty for each FESIS break. The time interval is given by the estimated date in the 'time' column with a confidence interval of +/- the interval in the tci column.

Examples


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
)

break_uncertainty(result)

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
)

break_uncertainty(result)

Internal function to check vectors that subset the indicator selection using the time dimension

Description

Internal function to check vectors that subset the indicator selection using the time dimension

Usage

check.time.subset.vectors(time.vector, vector.name, time, id)
check.time.subset.vectors(time.vector, vector.name, time, id)

Arguments

`time.vector`	A vector containing the user input in e.g. `tis_time` or `fesis_time`
`vector.name`	The name of argument that the user inputted this vector in. This is just to make error messages more elaborate.
`time`	The time dimension of isatpanel.
`id`	The id dimension of isatpanel.

Value

Does not return any value but will throw error if something is not correct.

CO2 Data for EU Road Emissions

Description

CO2 Data for EU Road Emissions

Usage

EU_emissions_road
EU_emissions_road

Format

A data frame with 1550 rows and 13 variables:

X: Index
country: Country
year: Year
gdp: Gross Domestic Product
pop: Population
transport.emissions: Transport CO2 Emissions
lgdp: Log GDP
lpop: Log Population
ltransport.emissions: Log Transport CO2 Emissions
const: Constant
L1.ltransport.emissions: Lag 1 Log Transport CO2 Emissions
L1.lgdp: Lag 1 Log GDP
L1.lpop: Lag 1 Log Population

Source

EDGAR

CO2 Data for the EU Residential Sector

Description

CO2 Data for the EU Residential Sector

Usage

EUCO2residential
EUCO2residential

Format

A data frame with 1550 rows and 9 variables:

country: Country
year: Year
lgdp: Log Gross Domestic Product
lhdd: Log Heating Degree Days
lcdd: Log Cooling Degree Days
urban: Urban Share
av.rate: EU Interest Rate
pop: Population
agg.directem: Aggregated Direct Emissions

Source

IEA

Internal lfe/felm Estimation Method

Description

Internal lfe/felm Estimation Method

Usage

felmFun(y, x, effect, time, id, cluster = "individual", ...)
felmFun(y, x, effect, time, id, cluster = "individual", ...)

Arguments

`y`	dependent variable
`x`	matrix of regressors
`effect`	Fixed Effect specification
`time`	Character vector of name of the time variable
`id`	Character vector of the name of the group variable
`cluster`	Character vector of the variable(s) to cluster Standard Errors at
`...`	Further arguments to pass to gets::isat

Value

List to be used by gets::isat

Internal fixest/feols Estimation Method

Description

Internal fixest/feols Estimation Method

Usage

fixestFun(y, x, effect, time, id, cluster = "individual", ...)
fixestFun(y, x, effect, time, id, cluster = "individual", ...)

Arguments

`y`	dependent variable
`x`	matrix of regressors
`effect`	Fixed Effect specification
`time`	Character vector of name of the time variable
`id`	Character vector of the name of the group variable
`cluster`	Character vector of the variable(s) to cluster Standard Errors at
`...`	Further arguments to pass to gets::isat

Value

List to be used by gets::isat

Extract the retained indicators from an `isatpanel` object

Description

Extract the retained indicators from an isatpanel object

Usage

get_indicators(object, uis_breaks = NULL)
get_indicators(object, uis_breaks = NULL)

Arguments

`object`	An object produced by the isatpanel function.
`uis_breaks`	A string with the names of user-specified indicators.

Value

A list of indicators.

Examples


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
)
plot(result)
plot_grid(result)

# print the retained indicators
get_indicators(result)

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
)
plot(result)
plot_grid(result)

# print the retained indicators
get_indicators(result)

Internal function to identify the timing of selected indicators

Description

Internal function to identify the timing of selected indicators

Usage

identify_indicator_timings(object, uis_breaks = NULL)
identify_indicator_timings(object, uis_breaks = NULL)

Arguments

`object`	data.frame
`uis_breaks`	A character vector with the names of the UIS breaks if the `uis` argument was used in isatpanel.

Value

A list of data.frames

Indicator Saturation for Panel Data

Description

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,
  ...
)
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

Examples


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
)
plot(result)
plot_grid(result)

# print the retained indicators
get_indicators(result)

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
)
plot(result)
plot_grid(result)

# print the retained indicators
get_indicators(result)

Log-Likelihood Function for a plm object

Description

Log-Likelihood Function for a plm object

Usage

## S3 method for class 'plm'
logLik(object, ...)
## S3 method for class 'plm'
logLik(object, ...)

Arguments

`object`	A plm object
`...`	Further Arguments

Value

The Log-Likelihood

Simulated Panel Data

Description

Simulated Panel Data

Usage

pandata_simulated
pandata_simulated

Format

A data frame with 400 rows and 9 variables:

country: A random country
year: Year
gdp: A simulated Gross Domestic Product
temp: A simulated variable standing for temperature
const: The constant
country_1: A dummy for country 1
country_2: A dummy for country 2
country_3: A dummy for country 3
country_4: A dummy for country 4

...

Source

https://github.com/moritzpschwarz/getspanel/

plm Function to estimate isatpanel

Description

plm Function to estimate isatpanel

Usage

plmFun(y, x, time, id, cluster, effect, model = "pooling", ...)
plmFun(y, x, time, id, cluster, effect, model = "pooling", ...)

Arguments

`y`	Dependent Variable
`x`	matrix or data.frame of regressors
`time`	Vector of time variable
`id`	Vector of group variable
`cluster`	cluster specification
`effect`	effect specification
`model`	model specification
`...`	Further arguments passed to plm

Value

A list to be used by gets::isat

Plot the Counterfactual Path

Description

Plot the Counterfactual Path

Usage

plot_counterfactual(
  x,
  plus_t = 5,
  facet.scales = "free",
  title = NULL,
  zero_line = FALSE
)
plot_counterfactual(
  x,
  plus_t = 5,
  facet.scales = "free",
  title = NULL,
  zero_line = FALSE
)

Arguments

`x`	An object produced by the isatpanel function
`plus_t`	Number of time periods for the counterfactual to be displayed (default = 5).
`facet.scales`	To be passed to ggplot2::facet_wrap. Default is "free" (i.e. a separate y axis for each panel group/id). Alternatives are: "fixed", "fixed_y", and "fixed_x".
`title`	Plot title. Must be a character vector.
`zero_line`	Plot a horizontal line at y = 0. Default is FALSE.

Value

A ggplot2 plot that plots an 'isatpanel' object and shows the counterfactuals for each break.

Examples


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
)
plot(result)
plot_grid(result)
plot_counterfactual(result)

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
)
plot(result)
plot_grid(result)
plot_counterfactual(result)

Plotting an isatpanel object

Description

Plotting an isatpanel object

Usage

plot_grid(x, title = NULL, regex_exclude_indicators = NULL, ...)
plot_grid(x, title = NULL, regex_exclude_indicators = NULL, ...)

Arguments

`x`	An object produced by the isatpanel function
`title`	Plot title. Must be a character vector.
`regex_exclude_indicators`	A regex character vector to exclude the inclusion of certain indicators in the plot. Default = NULL. Use with care, experimental.
`...`	Further arguments to be passed to ggplot2.

Value

A ggplot2 plot that plots an 'isatpanel' object and shows all indicators as a grid to give a good and quick overview.

Examples


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
)
plot(result)
plot_grid(result)

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
)
plot(result)
plot_grid(result)

Plot Residuals from 'isatpanel' against OLS

Description

Plot Residuals from 'isatpanel' against OLS

Usage

plot_residuals(isatpanelobject)
plot_residuals(isatpanelobject)

Arguments

isatpanelobject

An output from the 'isatpanel' function

Value

A ggplot2 plot that plots an 'isatpanel' object and shows the residuals over time in comparison to an OLS model.

Examples


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
)
plot(result)
plot_residuals(result)

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
)
plot(result)
plot_residuals(result)

Plotting an isatpanel object

Description

Plotting an isatpanel object

Usage

## S3 method for class 'isatpanel'
plot(
  x,
  max.id.facet = 16,
  facet.scales = "free",
  title = NULL,
  zero_line = FALSE,
  ...
)
## S3 method for class 'isatpanel'
plot(
  x,
  max.id.facet = 16,
  facet.scales = "free",
  title = NULL,
  zero_line = FALSE,
  ...
)

Arguments

`x`	An object produced by the isatpanel function
`max.id.facet`	The resulting plot will be faceted for each individual in the panel. Beyond a certain number, this might result in unreadable figures. Default set at 16.
`facet.scales`	To be passed to ggplot2::facet_wrap. Default is "free" (i.e. a separate y axis for each panel group/id). Alternatives are: "fixed", "fixed_y", and "fixed_x".
`title`	Plot title. Must be a character vector.
`zero_line`	Plot a horizontal line at y = 0. Default is FALSE.
`...`	Further arguments to be passed to ggplot2.

Value

A ggplot2 plot that plots an 'isatpanel' object and shows observed data, the fitted values, and all identified breaks and impulses.

Printing isatpanel results

Description

Printing isatpanel results

Usage

## S3 method for class 'isatpanel'
print(x, ...)
## S3 method for class 'isatpanel'
print(x, ...)

Arguments

`x`	An isatpanel object.
`...`	Further arguments passed to print

Value

Print output of the 'isatpanel.result' list element of the 'isatpanel' object.

Get robust Standard Errors for the isatpanel result

Description

Get robust Standard Errors for the isatpanel result

Usage

robust_isatpanel(
  object,
  robust = TRUE,
  HAC = FALSE,
  lag = NULL,
  type = "HC0",
  cluster = "group"
)
robust_isatpanel(
  object,
  robust = TRUE,
  HAC = FALSE,
  lag = NULL,
  type = "HC0",
  cluster = "group"
)

Arguments

`object`	An isatpanel object
`robust`	Logical (TRUE or FALSE). Should the Standard Errors be robustified for Heterogeneity? This uses plm::vcovHC with the specified type (default is "HC0").
`HAC`	Should Heteroscedasticity and Autocorrelation Robust Standard Errors be used? This uses plm::vcovNW, which uses the Newey-West estimator.
`lag`	Maximum Number of Lags to be used with plm::vcovNW using the Newey-West estimator. Cannot be specified when HAC = FALSE. Default is `NULL`.
`type`	Character string. Type of Robust procedure e.g. 'HC0' for White SE or 'HC3' for Lang.
`cluster`	Should an object with clustered S.E. be included? Choose between 'group' or 'time' or FALSE. Uses plm::vcovHC with the cluster argument.

Value

A list with robust estimates

Examples


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
)
robust_isatpanel(result)


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
)
robust_isatpanel(result)

Use the within transformation from the plm package

Description

Use the within transformation from the plm package

Usage

Within_plm(df, effect = "twoways")
Within_plm(df, effect = "twoways")

Arguments

`df`	A data.frame object
`effect`	The fixed effect specification. Values possible: "twoways" (default), "individual", "time", "nested"

Value

A data.frame object with the transformation complete

Package 'getspanel'

Help Index

Estimate Breakdate Uncertainty

Description

Usage

Arguments

Value

Examples

Internal function to check vectors that subset the indicator selection using the time dimension

Description

Usage

Arguments

Value

CO2 Data for EU Road Emissions

Description

Usage

Format

Source

CO2 Data for the EU Residential Sector

Description

Usage

Format

Source

Internal lfe/felm Estimation Method

Description

Usage

Arguments

Value

Internal fixest/feols Estimation Method

Description

Usage

Arguments

Value

Extract the retained indicators from an isatpanel object

Description

Usage

Arguments

Value

Examples

Internal function to identify the timing of selected indicators

Description

Usage

Arguments

Value

Indicator Saturation for Panel Data

Description

Usage

Arguments

Value

References

See Also

Examples

Log-Likelihood Function for a plm object

Description

Usage

Arguments

Value

Simulated Panel Data

Description

Usage

Format

Source

plm Function to estimate isatpanel

Description

Usage

Arguments

Value

Plot the Counterfactual Path

Description

Usage

Arguments

Value

Examples

Plotting an isatpanel object

Description

Usage

Arguments

Value

Examples

Plot Residuals from 'isatpanel' against OLS

Extract the retained indicators from an `isatpanel` object