Package 'alpaca' reference manual

Title:	Fit GLM's with High-Dimensional k-Way Fixed Effects
Description:	Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <arXiv:1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <arXiv:2004.12655>.
Authors:	Amrei Stammann [aut, cre], Daniel Czarnowske [aut]
Maintainer:	Amrei Stammann <[email protected]>
License:	GPL-3
Version:	0.3.4
Built:	2024-11-21 05:21:44 UTC
Source:	https://github.com/amrei-stammann/alpaca

alpaca: A package for fitting glm's with high-dimensional $k$ -way fixed effects

Description

Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernández-Val and Weidner (2016) and Hinz, Stammann, and Wanner (2020).

Note: Linear models are also beyond the scope of this package since there is already a comprehensive procedure available felm.

alpaca: A package for fitting glm's with high-dimensional $k$ -way fixed effects

Description

Concentrates out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is restricted to glm's that are based on maximum likelihood estimation. This excludes all quasi-variants of glm. The package also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides an analytical bias-correction for binary choice models (logit and probit) derived by Fernandez-Val and Weidner (2016).

Note: Linear models are also beyond the scope of this package since there is already a comprehensive procedure available felm.

Asymptotic bias correction after fitting binary choice models with a one-/two-/three-way error component

Description

biasCorr is a post-estimation routine that can be used to substantially reduce the incidental parameter bias problem (Neyman and Scott (1948)) present in nonlinear fixed effects models (see Fernández-Val and Weidner (2018) for an overview). The command applies the analytical bias correction derived by Fernández-Val and Weidner (2016) and Hinz, Stammann, and Wanner (2020) to obtain bias-corrected estimates of the structural parameters and is currently restricted to binomial with one-, two-, and three-way fixed effects.

Usage

biasCorr(object = NULL, L = 0L, panel.structure = c("classic", "network"))
biasCorr(object = NULL, L = 0L, panel.structure = c("classic", "network"))

Arguments

`object`	an object of class `"feglm"`; currently restricted to `binomial`.
`L`	unsigned integer indicating a bandwidth for the estimation of spectral densities proposed by Hahn and Kuersteiner (2011). Default is zero, which should be used if all regressors are assumed to be strictly exogenous with respect to the idiosyncratic error term. In the presence of weakly exogenous regressors, e.g. lagged outcome variables, Fernández-Val and Weidner (2016, 2018) suggest to choose a bandwidth between one and four. Note that the order of factors to be partialed out is important for bandwidths larger than zero (see vignette for details).
`panel.structure`	a string equal to `"classic"` or `"network"` which determines the structure of the panel used. `"classic"` denotes panel structures where for example the same cross-sectional units are observed several times (this includes pseudo panels). `"network"` denotes panel structures where for example bilateral trade flows are observed for several time periods. Default is `"classic"`.

Value

The function biasCorr returns a named list of classes "biasCorr" and "feglm".

References

Czarnowske, D. and A. Stammann (2020). "Fixed Effects Binary Choice Models: Estimation and Inference with Long Panels". ArXiv e-prints.

Fernández-Val, I. and M. Weidner (2016). "Individual and time effects in nonlinear panel models with large N, T". Journal of Econometrics, 192(1), 291-312.

Fernández-Val, I. and M. Weidner (2018). "Fixed effects estimation of large-t panel data models". Annual Review of Economics, 10, 109-138.

Hahn, J. and G. Kuersteiner (2011). "Bias reduction for dynamic nonlinear panel models with fixed effects". Econometric Theory, 27(6), 1152-1191.

Hinz, J., A. Stammann, and J. Wanner (2020). "State Dependence and Unobserved Heterogeneity in the Extensive Margin of Trade". ArXiv e-prints.

Neyman, J. and E. L. Scott (1948). "Consistent estimates based on partially consistent observations". Econometrica, 16(1), 1-32.

Examples


# Generate an artificial data set for logit models
library(alpaca)
data <- simGLM(1000L, 20L, 1805L, model = "logit")

# Fit 'feglm()'
mod <- feglm(y ~ x1 + x2 + x3 | i + t, data)

# Apply analytical bias correction
mod.bc <- biasCorr(mod)
summary(mod.bc)

# Generate an artificial data set for logit models
library(alpaca)
data <- simGLM(1000L, 20L, 1805L, model = "logit")

# Fit 'feglm()'
mod <- feglm(y ~ x1 + x2 + x3 | i + t, data)

# Apply analytical bias correction
mod.bc <- biasCorr(mod)
summary(mod.bc)

Extract estimates of average partial effects

Description

coef.APEs is a generic function which extracts estimates of the average partial effects from objects returned by getAPEs.

Usage

## S3 method for class 'APEs'
coef(object, ...)
## S3 method for class 'APEs'
coef(object, ...)

Arguments

`object`	an object of class `"APEs"`.
`...`	other arguments.

Value

The function coef.APEs returns a named vector of estimates of the average partial effects.

Extract estimates of structural parameters

Description

coef.feglm is a generic function which extracts estimates of the structural parameters from objects returned by feglm.

Usage

## S3 method for class 'feglm'
coef(object, ...)
## S3 method for class 'feglm'
coef(object, ...)

Arguments

`object`	an object of class `"feglm"`.
`...`	other arguments.

Value

The function coef.feglm returns a named vector of estimates of the structural parameters.

Extract coefficient matrix for average partial effects

Description

coef.summary.APEs is a generic function which extracts a coefficient matrix for average partial effects from objects returned by getAPEs.

Usage

## S3 method for class 'summary.APEs'
coef(object, ...)
## S3 method for class 'summary.APEs'
coef(object, ...)

Arguments

`object`	an object of class `"summary.APEs"`.
`...`	other arguments.

Value

The function coef.summary.APEs returns a named matrix of estimates related to the average partial effects.

Extract coefficient matrix for structural parameters

Description

coef.summary.feglm is a generic function which extracts a coefficient matrix for structural parameters from objects returned by feglm.

Usage

## S3 method for class 'summary.feglm'
coef(object, ...)
## S3 method for class 'summary.feglm'
coef(object, ...)

Arguments

`object`	an object of class `"summary.feglm"`.
`...`	other arguments.

Value

The function coef.summary.feglm returns a named matrix of estimates related to the structural parameters.

Efficiently fit glm's with high-dimensional $k$ -way fixed effects

Description

feglm can be used to fit generalized linear models with many high-dimensional fixed effects. The estimation procedure is based on unconditional maximum likelihood and can be interpreted as a “weighted demeaning” approach that combines the work of Gaure (2013) and Stammann et. al. (2016). For technical details see Stammann (2018). The routine is well suited for large data sets that would be otherwise infeasible to use due to memory limitations.

Remark: The term fixed effect is used in econometrician's sense of having intercepts for each level in each category.

Usage

feglm(
  formula = NULL,
  data = NULL,
  family = binomial(),
  weights = NULL,
  beta.start = NULL,
  eta.start = NULL,
  control = NULL
)
feglm(
  formula = NULL,
  data = NULL,
  family = binomial(),
  weights = NULL,
  beta.start = NULL,
  eta.start = NULL,
  control = NULL
)

Arguments

`formula`	an object of class `"formula"`: a symbolic description of the model to be fitted. `formula` must be of type `y ~ x \| k`, where the second part of the formula refers to factors to be concentrated out. It is also possible to pass additional variables to `feglm` (e.g. to cluster standard errors). This can be done by specifying the third part of the formula: `y ~ x \| k \| add`.
`data`	an object of class `"data.frame"` containing the variables in the model.
`family`	a description of the error distribution and link function to be used in the model. Similar to `glm.fit` this has to be the result of a call to a family function. Default is `binomial()`. See `family` for details of family functions.
`weights`	an optional string with the name of the 'prior weights' variable in `data`.
`beta.start`	an optional vector of starting values for the structural parameters in the linear predictor. Default is $\boldsymbol{\beta} = \mathbf{0}$ .
`eta.start`	an optional vector of starting values for the linear predictor.
`control`	a named list of parameters for controlling the fitting process. See `feglmControl` for details.

Details

If feglm does not converge this is often a sign of linear dependence between one or more regressors and a fixed effects category. In this case, you should carefully inspect your model specification.

Value

The function feglm returns a named list of class "feglm".

References

Gaure, S. (2013). "OLS with Multiple High Dimensional Category Variables". Computational Statistics and Data Analysis, 66.

Marschner, I. (2011). "glm2: Fitting generalized linear models with convergence problems". The R Journal, 3(2).

Stammann, A., F. Heiss, and D. McFadden (2016). "Estimating Fixed Effects Logit Models with Large Panel Data". Working paper.

Stammann, A. (2018). "Fast and Feasible Estimation of Generalized Linear Models with High-Dimensional k-Way Fixed Effects". ArXiv e-prints.

Examples


# Generate an artificial data set for logit models
library(alpaca)
data <- simGLM(1000L, 20L, 1805L, model = "logit")

# Fit 'feglm()'
mod <- feglm(y ~ x1 + x2 + x3 | i + t, data)
summary(mod)

# Generate an artificial data set for logit models
library(alpaca)
data <- simGLM(1000L, 20L, 1805L, model = "logit")

# Fit 'feglm()'
mod <- feglm(y ~ x1 + x2 + x3 | i + t, data)
summary(mod)

Set `feglm` Control Parameters

Description

Set and change parameters used for fitting feglm.

Usage

feglm.control(dev.tol = 1e-08, step.tol = 1e-08, pseudo.tol = 1e-05,
  rho.tol = 1e-04, iter.max = 100L, trace = 0L, drop.pc = TRUE)
feglm.control(dev.tol = 1e-08, step.tol = 1e-08, pseudo.tol = 1e-05,
  rho.tol = 1e-04, iter.max = 100L, trace = 0L, drop.pc = TRUE)

Arguments

`dev.tol`	tolerance level for the first stopping condition of the maximization routine. The stopping condition is based on the relative change of the deviance in iteration $r$ and can be expressed as follows: $(\text{dev}_{r - 1} \text{dev}_{r}) / \text{dev}_{r} < \text{tol}$ . Default is `1.0e-08`.
`step.tol`	tolerance level for the second stopping condition of the maximization routine. The stopping condition is based on the euclidean norm of the step size in iteration $r$ and can be expressed as follows: $\lvert\boldsymbol{\beta}_{r} - \boldsymbol{\beta}_{r - 1}\rvert_{2} < \text{tol}$ . Default is `1.0e-08`.
`pseudo.tol`	tolerance level for the stopping condition of the “pseudo demeaning” algorithm. The stopping condition is based on the relative change of euclidean norm in iteration $i$ and can be expressed as follows: $\lvert\mathbf{v}_{i} - \mathbf{v}_{i - 1}\rvert_{2} < \text{tol} \lvert\mathbf{v}_{i - 1}\rvert$ . Default is `1.0e-05`.
`rho.tol`	tolerance level for the stephalving in the maximization routine. Stephalving only takes place if the deviance in iteration $r$ is larger than the one of the previous iteration. If this is the case, $\lvert\boldsymbol{\beta}_{r} - \boldsymbol{\beta}_{r - 1}\rvert_{2}$ is halfed until the deviance is less or equal compared to the deviance of the previous iteration. Stephalving fails if the the following condition holds: $\rho < \text{tol}$ , where $\rho$ is the stepcorrection factor. If stephalving fails the maximization routine is canceled. Default is `1.0e-04`.
`iter.max`	unsigned integer indicating the maximum number of iterations in the maximization routine.
`trace`	unsigned integer indicating if output should be produced in each iteration. Default is `0`.
`drop.pc`	logical indicating to drop observations that are perfectly classified and hence do not contribute to the log-likelihood. This option is useful to reduce the computational costs of the maximization problem, since it reduces the number of observations and does not change the estimates. Default is `TRUE`.

Value

The function feglm.control returns a named list of control parameters.

Efficiently fit negative binomial glm's with high-dimensional $k$ -way fixed effects

Description

feglm.nb can be used to fit negative binomial generalized linear models with many high-dimensional fixed effects (see feglm).

Usage

feglm.nb(
  formula = NULL,
  data = NULL,
  weights = NULL,
  beta.start = NULL,
  eta.start = NULL,
  init.theta = NULL,
  link = c("log", "identity", "sqrt"),
  control = NULL
)
feglm.nb(
  formula = NULL,
  data = NULL,
  weights = NULL,
  beta.start = NULL,
  eta.start = NULL,
  init.theta = NULL,
  link = c("log", "identity", "sqrt"),
  control = NULL
)

Arguments

`formula`, `data`, `weights`, `beta.start`, `eta.start`, `control`	see `feglm`.
`init.theta`	an optional initial value for the theta parameter (see `glm.nb`).
`link`	the link function. Must be one of `"log"`, `"sqrt"`, or `"identity"`.

Details

If feglm.nb does not converge this is usually a sign of linear dependence between one or more regressors and a fixed effects category. In this case, you should carefully inspect your model specification.

Value

The function feglm.nb returns a named list of class "feglm".

References

Gaure, S. (2013). "OLS with Multiple High Dimensional Category Variables". Computational Statistics and Data Analysis. 66.

Marschner, I. (2011). "glm2: Fitting generalized linear models with convergence problems". The R Journal, 3(2).

Stammann, A., F. Heiss, and D. McFadden (2016). "Estimating Fixed Effects Logit Models with Large Panel Data". Working paper.

Stammann, A. (2018). "Fast and Feasible Estimation of Generalized Linear Models with High-Dimensional k-Way Fixed Effects". ArXiv e-prints.

Set `feglm` Control Parameters

Description

Set and change parameters used for fitting feglm.

Note: feglm.control is deprecated and will be removed soon.

Usage

feglmControl(
  dev.tol = 1e-08,
  center.tol = 1e-08,
  iter.max = 25L,
  limit = 10L,
  trace = FALSE,
  drop.pc = TRUE,
  keep.mx = TRUE,
  conv.tol = NULL,
  rho.tol = NULL,
  pseudo.tol = NULL,
  step.tol = NULL
)

feglm.control(...)
feglmControl(
  dev.tol = 1e-08,
  center.tol = 1e-08,
  iter.max = 25L,
  limit = 10L,
  trace = FALSE,
  drop.pc = TRUE,
  keep.mx = TRUE,
  conv.tol = NULL,
  rho.tol = NULL,
  pseudo.tol = NULL,
  step.tol = NULL
)

feglm.control(...)

Arguments

`dev.tol`	tolerance level for the first stopping condition of the maximization routine. The stopping condition is based on the relative change of the deviance in iteration $r$ and can be expressed as follows: $\|dev_{r} - dev_{r - 1}\| / (0.1 + \|dev_{r}\|) < tol$ . Default is `1.0e-08`.
`center.tol`	tolerance level for the stopping condition of the centering algorithm. The stopping condition is based on the relative change of the centered variable similar to felm. Default is `1.0e-08`.
`iter.max`	unsigned integer indicating the maximum number of iterations in the maximization routine. Default is `25L`.
`limit`	unsigned integer indicating the maximum number of iterations of `theta.ml`. Default is `10L`.
`trace`	logical indicating if output should be produced in each iteration. Default is `FALSE`.
`drop.pc`	logical indicating to drop observations that are perfectly classified/separated and hence do not contribute to the log-likelihood. This option is useful to reduce the computational costs of the maximization problem and improves the numerical stability of the algorithm. Note that dropping perfectly separated observations does not affect the estimates. Default is `TRUE`.
`keep.mx`	logical indicating if the centered regressor matrix should be stored. The centered regressor matrix is required for some covariance estimators, bias corrections, and average partial effects. This option saves some computation time at the cost of memory. Default is `TRUE`.
`conv.tol`, `rho.tol`	deprecated; step-halving is now similar to `glm.fit2`.
`pseudo.tol`	deprecated; use `center.tol` instead.
`step.tol`	deprecated; termination conditions is now similar to `glm`.
`...`	arguments passed to the deprecated function `feglm.control`.

Value

The function feglmControl returns a named list of control parameters.

Extract `feglm` fitted values

Description

fitted.feglm is a generic function which extracts fitted values from an object returned by feglm.

Usage

## S3 method for class 'feglm'
fitted(object, ...)
## S3 method for class 'feglm'
fitted(object, ...)

Arguments

`object`	an object of class `"feglm"`.
`...`	other arguments.

Value

The function fitted.feglm returns a vector of fitted values.

Compute average partial effects after fitting binary choice models with a one-/two-/three-way error component

Description

getAPEs is a post-estimation routine that can be used to estimate average partial effects with respect to all covariates in the model and the corresponding covariance matrix. The estimation of the covariance is based on a linear approximation (delta method) plus an optional finite population correction. Note that the command automatically determines which of the regressors are binary or non-binary.

Remark: The routine currently does not allow to compute average partial effects based on functional forms like interactions and polynomials.

Usage

getAPEs(
  object = NULL,
  n.pop = NULL,
  panel.structure = c("classic", "network"),
  sampling.fe = c("independence", "unrestricted"),
  weak.exo = FALSE
)
getAPEs(
  object = NULL,
  n.pop = NULL,
  panel.structure = c("classic", "network"),
  sampling.fe = c("independence", "unrestricted"),
  weak.exo = FALSE
)

Arguments

`object`	an object of class `"biasCorr"` or `"feglm"`; currently restricted to `binomial`.
`n.pop`	unsigned integer indicating a finite population correction for the estimation of the covariance matrix of the average partial effects proposed by Cruz-Gonzalez, Fernández-Val, and Weidner (2017). The correction factor is computed as follows: $(n^{\ast} - n) / (n^{\ast} - 1)$ , where $n^{\ast}$ and $n$ are the sizes of the entire population and the full sample size. Default is `NULL`, which refers to a factor of zero and a covariance obtained by the delta method.
`panel.structure`	a string equal to `"classic"` or `"network"` which determines the structure of the panel used. `"classic"` denotes panel structures where for example the same cross-sectional units are observed several times (this includes pseudo panels). `"network"` denotes panel structures where for example bilateral trade flows are observed for several time periods. Default is `"classic"`.
`sampling.fe`	a string equal to `"independence"` or `"unrestricted"` which imposes sampling assumptions about the unobserved effects. `"independence"` imposes that all unobserved effects are independent sequences. `"unrestricted"` does not impose any sampling assumptions. Note that this option only affects the optional finite population correction. Default is `"independence"`.
`weak.exo`	logical indicating if some of the regressors are assumed to be weakly exogenous (e. g. predetermined). If object is of class `"biasCorr"`, the option will be automatically set to `TRUE` if the chosen bandwidth parameter is larger than zero. Note that this option only affects the estimation of the covariance matrix. Default is `FALSE`, which assumes that all regressors are strictly exogenous.

Value

The function getAPEs returns a named list of class "APEs".

References

Cruz-Gonzalez, M., I. Fernández-Val, and M. Weidner (2017). "Bias corrections for probit and logit models with two-way fixed effects". The Stata Journal, 17(3), 517-545.

Czarnowske, D. and A. Stammann (2020). "Fixed Effects Binary Choice Models: Estimation and Inference with Long Panels". ArXiv e-prints.

Fernández-Val, I. and M. Weidner (2016). "Individual and time effects in nonlinear panel models with large N, T". Journal of Econometrics, 192(1), 291-312.

Fernández-Val, I. and M. Weidner (2018). "Fixed effects estimation of large-t panel data models". Annual Review of Economics, 10, 109-138.

Hinz, J., A. Stammann, and J. Wanner (2020). "State Dependence and Unobserved Heterogeneity in the Extensive Margin of Trade". ArXiv e-prints.

Neyman, J. and E. L. Scott (1948). "Consistent estimates based on partially consistent observations". Econometrica, 16(1), 1-32.

Examples


# Generate an artificial data set for logit models
library(alpaca)
data <- simGLM(1000L, 20L, 1805L, model = "logit")

# Fit 'feglm()'
mod <- feglm(y ~ x1 + x2 + x3 | i + t, data)

# Compute average partial effects
mod.ape <- getAPEs(mod)
summary(mod.ape)

# Apply analytical bias correction
mod.bc <- biasCorr(mod)
summary(mod.bc)

# Compute bias-corrected average partial effects
mod.ape.bc <- getAPEs(mod.bc)
summary(mod.ape.bc)

# Generate an artificial data set for logit models
library(alpaca)
data <- simGLM(1000L, 20L, 1805L, model = "logit")

# Fit 'feglm()'
mod <- feglm(y ~ x1 + x2 + x3 | i + t, data)

# Compute average partial effects
mod.ape <- getAPEs(mod)
summary(mod.ape)

# Apply analytical bias correction
mod.bc <- biasCorr(mod)
summary(mod.bc)

# Compute bias-corrected average partial effects
mod.ape.bc <- getAPEs(mod.bc)
summary(mod.ape.bc)

Efficiently recover estimates of the fixed effects after fitting `feglm`

Description

Recover estimates of the fixed effects by alternating between the normal equations of the fixed effects as shown by Stammann (2018).

Remark: The system might not have a unique solution since we do not take collinearity into account. If the solution is not unique, an estimable function has to be applied to our solution to get meaningful estimates of the fixed effects. See Gaure (n. d.) for an extensive treatment of this issue.

Usage

getFEs(object = NULL, alpha.tol = 1e-08)
getFEs(object = NULL, alpha.tol = 1e-08)

Arguments

`object`	an object of class `"feglm"`.
`alpha.tol`	tolerance level for the stopping condition. The algorithm is stopped in iteration $i$ if $\|\|\boldsymbol{\alpha}_{i} - \boldsymbol{\alpha}_{i - 1}\|\|_{2} < tol \|\|\boldsymbol{\alpha}_{i - 1}\|\|_{2}$ . Default is `1.0e-08`.

Value

The function getFEs returns a named list containing named vectors of estimated fixed effects.

References

Gaure, S. (n. d.). "Multicollinearity, identification, and estimable functions". Unpublished.

Stammann, A. (2018). "Fast and Feasible Estimation of Generalized Linear Models with High-Dimensional k-way Fixed Effects". ArXiv e-prints.

Predict method for `feglm` fits

Description

predict.feglm is a generic function which obtains predictions from an object returned by feglm.

Usage

## S3 method for class 'feglm'
predict(object, type = c("link", "response"), ...)
## S3 method for class 'feglm'
predict(object, type = c("link", "response"), ...)

Arguments

`object`	an object of class `"feglm"`.
`type`	the type of prediction required. `"link"` is on the scale of the linear predictor whereas `"response"` is on the scale of the response variable. Default is `"link"`.
`...`	other arguments.

Value

The function predict.feglm returns a vector of predictions.

Print `APEs`

Description

print.APEs is a generic function which displays some minimal information from objects returned by getAPEs.

Usage

## S3 method for class 'APEs'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'APEs'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

`x`	an object of class `"APEs"`.
`digits`	unsigned integer indicating the number of decimal places. Default is `max(3L, getOption("digits") - 3L)`.
`...`	other arguments.

Print `feglm`

Description

print.feglm is a generic function which displays some minimal information from objects returned by feglm.

Usage

## S3 method for class 'feglm'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'feglm'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

`x`	an object of class `"feglm"`.
`digits`	unsigned integer indicating the number of decimal places. Default is `max(3L, getOption("digits") - 3L)`.
`...`	other arguments.

Print `summary.APEs`

Description

print.summary.APEs is a generic function which displays summary statistics from objects returned by summary.APEs.

Usage

## S3 method for class 'summary.APEs'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'summary.APEs'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

`x`	an object of class `"summary.APEs"`.
`digits`	unsigned integer indicating the number of decimal places. Default is `max(3L, getOption("digits") - 3L)`.
`...`	other arguments.

Print `summary.feglm`

Description

print.summary.feglm is a generic function which displays summary statistics from objects returned by summary.feglm.

Usage

## S3 method for class 'summary.feglm'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'summary.feglm'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

`x`	an object of class `"summary.feglm"`.
`digits`	unsigned integer indicating the number of decimal places. Default is `max(3L, getOption("digits") - 3L)`.
`...`	other arguments.

Generate an artificial data set for some GLM's with two-way fixed effects

Description

Constructs an artificial data set with $n$ cross-sectional units observed for $t$ time periods for logit, poisson, or gamma models. The “true” linear predictor ( $\boldsymbol{\eta}$ ) is generated as follows:

$\eta_{it} = \mathbf{x}_{it}^{\prime} \boldsymbol{\beta} + \alpha_{i} + \gamma_{t} \, ,$

where $\mathbf{X}$ consists of three independent standard normally distributed regressors. Both parameter referring to the unobserved heterogeneity ( $\alpha_{i}$ and $\gamma_{t}$ ) are generated as iid. standard normal and the structural parameters are set to $\boldsymbol{\beta} = [1, - 1, 1]^{\prime}$ .

Note: The poisson and gamma model are based on the logarithmic link function.

Usage

simGLM(n = NULL, t = NULL, seed = NULL, model = c("logit", "poisson", "gamma"))
simGLM(n = NULL, t = NULL, seed = NULL, model = c("logit", "poisson", "gamma"))

Arguments

`n`	a strictly positive integer equal to the number of cross-sectional units.
`t`	a strictly positive integer equal to the number of time periods.
`seed`	a seed to ensure reproducibility.
`model`	a string equal to `"logit"`, `"poisson"`, or `"gamma"`. Default is `"logit"`.

Value

The function simGLM returns a data.frame with 6 variables.

Summarizing models of class `APEs`

Description

Summary statistics for objects of class "APEs".

Usage

## S3 method for class 'APEs'
summary(object, ...)
## S3 method for class 'APEs'
summary(object, ...)

Arguments

`object`	an object of class `"APEs"`.
`...`	other arguments.

Value

Returns an object of class "summary.APEs" which is a list of summary statistics of object.

Summarizing models of class `feglm`

Description

Summary statistics for objects of class "feglm".

Usage

## S3 method for class 'feglm'
summary(
  object,
  type = c("hessian", "outer.product", "sandwich", "clustered"),
  cluster = NULL,
  cluster.vars = NULL,
  ...
)
## S3 method for class 'feglm'
summary(
  object,
  type = c("hessian", "outer.product", "sandwich", "clustered"),
  cluster = NULL,
  cluster.vars = NULL,
  ...
)

Arguments

`object`	an object of class `"feglm"`.
`type`	the type of covariance estimate required. `"hessian"` refers to the inverse of the negative expected Hessian after convergence and is the default option. `"outer.product"` is the outer-product-of-the-gradient estimator, `"sandwich"` is the sandwich estimator (sometimes also refered as robust estimator), and `"clustered"` computes a clustered covariance matrix given some cluster variables.
`cluster`	a symbolic description indicating the clustering of observations.
`cluster.vars`	deprecated; use `cluster` instead.
`...`	other arguments.

Details

Multi-way clustering is done using the algorithm of Cameron, Gelbach, and Miller (2011). An example is provided in the vignette "Replicating an Empirical Example of International Trade".

Value

Returns an object of class "summary.feglm" which is a list of summary statistics of object.

References

Cameron, C., J. Gelbach, and D. Miller (2011). "Robust Inference With Multiway Clustering". Journal of Business & Economic Statistics 29(2).

Compute covariance matrix after estimating `APEs`

Description

vcov.APEs estimates the covariance matrix for the estimator of the average partial effects from objects returned by getAPEs.

Usage

## S3 method for class 'APEs'
vcov(object, ...)
## S3 method for class 'APEs'
vcov(object, ...)

Arguments

`object`	an object of class `"APEs"`.
`...`	other arguments.

Value

The function vcov.APEs returns a named matrix of covariance estimates.

Compute covariance matrix after fitting `feglm`

Description

vcov.feglm estimates the covariance matrix for the estimator of the structural parameters from objects returned by feglm. The covariance is computed from the Hessian, the scores, or a combination of both after convergence.

Usage

## S3 method for class 'feglm'
vcov(
  object,
  type = c("hessian", "outer.product", "sandwich", "clustered"),
  cluster = NULL,
  cluster.vars = NULL,
  ...
)
## S3 method for class 'feglm'
vcov(
  object,
  type = c("hessian", "outer.product", "sandwich", "clustered"),
  cluster = NULL,
  cluster.vars = NULL,
  ...
)

Arguments

`object`	an object of class `"feglm"`.
`type`	the type of covariance estimate required. `"hessian"` refers to the inverse of the negative expected Hessian after convergence and is the default option. `"outer.product"` is the outer-product-of-the-gradient estimator, `"sandwich"` is the sandwich estimator (sometimes also referred as robust estimator), and `"clustered"` computes a clustered covariance matrix given some cluster variables.
`cluster`	a symbolic description indicating the clustering of observations.
`cluster.vars`	deprecated; use `cluster` instead.
`...`	other arguments.

Details

Multi-way clustering is done using the algorithm of Cameron, Gelbach, and Miller (2011). An example is provided in the vignette "Replicating an Empirical Example of International Trade".

Value

The function vcov.feglm returns a named matrix of covariance estimates.

References

Cameron, C., J. Gelbach, and D. Miller (2011). "Robust Inference With Multiway Clustering". Journal of Business & Economic Statistics 29(2).

Package 'alpaca'

Help Index

alpaca: A package for fitting glm's with high-dimensional kkk-way fixed effects

Description

alpaca: A package for fitting glm's with high-dimensional kkk-way fixed effects

Description

Asymptotic bias correction after fitting binary choice models with a one-/two-/three-way error component

Description

Usage

Arguments

Value

References

See Also

Examples

Extract estimates of average partial effects

Description

Usage

Arguments

Value

See Also

Extract estimates of structural parameters

Description

Usage

Arguments

Value

See Also

Extract coefficient matrix for average partial effects

Description

Usage

Arguments

Value

See Also

Extract coefficient matrix for structural parameters

Description

Usage

Arguments

Value

See Also

Efficiently fit glm's with high-dimensional kkk-way fixed effects

Description

Usage

Arguments

Details

Value

References

Examples

Set feglm Control Parameters

Description

Usage

Arguments

Value

See Also

Efficiently fit negative binomial glm's with high-dimensional kkk-way fixed effects

Description

Usage

Arguments

Details

Value

References

See Also

Set feglm Control Parameters

Description

Usage

Arguments

Value

See Also

Extract feglm fitted values

Description

Usage

Arguments

Value

See Also

Compute average partial effects after fitting binary choice models with a one-/two-/three-way error component

Description

Usage

Arguments

Value

References

See Also

Examples

alpaca: A package for fitting glm's with high-dimensional $k$ -way fixed effects

alpaca: A package for fitting glm's with high-dimensional $k$ -way fixed effects

Efficiently fit glm's with high-dimensional $k$ -way fixed effects

Set `feglm` Control Parameters

Efficiently fit negative binomial glm's with high-dimensional $k$ -way fixed effects

Set `feglm` Control Parameters

Extract `feglm` fitted values

Efficiently recover estimates of the fixed effects after fitting `feglm`

Predict method for `feglm` fits

Print `APEs`

Print `feglm`

Print `summary.APEs`

Print `summary.feglm`

Summarizing models of class `APEs`

Summarizing models of class `feglm`

Compute covariance matrix after estimating `APEs`

Compute covariance matrix after fitting `feglm`