Zelig-ivreg-class: Instrumental-Variable Regression
In Zelig: Everyone's Statistical Software

Description Arguments Details Value Methods Source See Also Examples

Instrumental-Variable Regression

`model`	the name of a statistical model to estimate. For a list of other supported models and their documentation see: http://docs.zeligproject.org/articles/.
`data`	the name of a data frame containing the variables referenced in the formula or a list of multiply imputed data frames each having the same variable names and row numbers (created by `Amelia` or `to_zelig_mi`).
`by`	a factor variable contained in `data`. If supplied, `zelig` will subset the data frame based on the levels in the `by` variable, and estimate a model for each subset. This can save a considerable amount of effort. You may also use `by` to run models using MatchIt subclasses.
`cite`	If is set to 'TRUE' (default), the model citation will be printed to the console.
`formula`	specification(s) of the regression relationship
`instruments`	the instruments. Either `instruments` is missing and formula has three parts as in `y ~ x1 + x2 \| z1 + z2 + z3` (recommended) or formula is `y ~ x1 + x2` and instruments is a one-sided formula `~ z1 + z2 + z3`. Using `instruments` is not recommended with `zelig`.
`model, x, y`	logicals. If `TRUE` the corresponding components of the fit (the model frame, the model matrices , the response) are returned.
`...`	further arguments passed to methods. See also `zelig`.

Additional parameters avaialable to many models include:

weights: vector of weight values or a name of a variable in the dataset by which to weight the model. For more information see: http://docs.zeligproject.org/articles/weights.html.
bootstrap: logical or numeric. If FALSE don't use bootstraps to robustly estimate uncertainty around model parameters due to sampling error. If an integer is supplied, the number of boostraps to run. For more information see: http://docs.zeligproject.org/articles/bootstraps.html.

Regressors and instruments for ivreg are most easily specified in a formula with two parts on the right-hand side, e.g., y ~ x1 + x2 | z1 + z2 + z3, where x1 and x2 are the regressors and z1, z2, and z3 are the instruments. Note that exogenous regressors have to be included as instruments for themselves. For example, if there is one exogenous regressor ex and one endogenous regressor en with instrument in, the appropriate formula would be y ~ ex + en | ex + in. Equivalently, this can be specified as y ~ ex + en | . - en + in, i.e., by providing an update formula with a . in the second part of the formula. The latter is typically more convenient, if there is a large number of exogenous regressors.

Depending on the class of model selected, zelig will return an object with elements including coefficients, residuals, and formula which may be summarized using summary(z.out) or individually extracted using, for example, coef(z.out). See http://docs.zeligproject.org/articles/getters.html for a list of functions to extract model components. You can also extract whole fitted model objects using from_zelig_model.

zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE): The zelig function estimates a variety of statistical models

ivreg is from Christian Kleiber and Achim Zeileis (2008). Applied Econometrics with R. New York: Springer-Verlag. ISBN 978-0-387-77316-2. URL https://CRAN.R-project.org/package=AER

Vignette: http://docs.zeligproject.org/articles/zelig_ivreg.html Fit instrumental-variable regression by two-stage least squares. This is equivalent to direct instrumental-variables estimation when the number of instruments is equal to the number of predictors.

zelig, Greene, W. H. (1993) Econometric Analysis, 2nd ed., Macmillan.

library(Zelig)
library(dplyr) # for the pipe operator %>%
# load and transform data
data("CigarettesSW")
CigarettesSW$rprice <- with(CigarettesSW, price/cpi)
CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi)
CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi)
# log second stage independent variables, as logging internally for ivreg is
# not currently supported
CigarettesSW$log_rprice <- log(CigarettesSW$rprice)
CigarettesSW$log_rincome <- log(CigarettesSW$rincome)
z.out1 <- zelig(log(packs) ~ log_rprice + log_rincome |
log_rincome + tdiff + I(tax/cpi),data = CigarettesSW, subset = year == "1995",model = "ivreg")
summary(z.out1)
library(Zelig)
library(AER) # for sandwich vcov
library(dplyr) # for the pipe operator %>%

# load and transform data
data("CigarettesSW")
CigarettesSW$rprice <- with(CigarettesSW, price/cpi)
CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi)
CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi)

# log second stage independent variables, as logging internally for ivreg is
# not currently supported
CigarettesSW$log_rprice <- log(CigarettesSW$rprice)
CigarettesSW$log_rincome <- log(CigarettesSW$rincome)

# estimate model
z.out1 <- zelig(log(packs) ~ log_rprice + log_rincome |
                    log_rincome + tdiff + I(tax/cpi),
                    data = CigarettesSW,
                    model = "ivreg")
summary(z.out1)