ivreg {AER} | R Documentation

## Instrumental-Variable Regression

### Description

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.

### Usage

    ivreg(formula, instruments, data, subset, na.action, weights, offset,
      contrasts = NULL, model = TRUE, y = TRUE, x = FALSE, ...)

### Arguments

`formula, instruments` | formula specification(s) of the regression relationship and 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` (only for backward compatibility).
`data` | an optional data frame containing the variables in the model. By default the variables are taken from the environment of the `formula`.
`subset` | an optional vector specifying a subset of observations to be used in fitting the model.
`na.action` | a function that indicates what should happen when the data contain `NA`s. The default is set by the `na.action` option.
`weights` | an optional vector of weights to be used in the fitting process.
`offset` | an optional offset that can be used to specify an a priori known component to be included during fitting.
`contrasts` | an optional list. See the `contrasts.arg` of `model.matrix.default`.
`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 `ivreg.fit`.

### Details

`ivreg` is the high-level interface to the work-horse function `ivreg.fit`, a set of standard methods (including `print`, `summary`, `vcov`, `anova`, `hatvalues`, `predict`, `terms`, `model.matrix`, `bread`, `estfun`) is available and described on `summary.ivreg`.

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.

### Value

`ivreg` returns an object of class `"ivreg"`, with the following components:

`coefficients` | parameter estimates.
`residuals` | a vector of residuals.
`fitted.values` | a vector of predicted means.
`weights` | either the vector of weights used (if any) or `NULL` (if none).
`offset` | either the offset used (if any) or `NULL` (if none).
`n` | number of observations.
`nobs` | number of observations with non-zero weights.
`rank` | the numeric rank of the fitted linear model.
`df.residual` | residual degrees of freedom for fitted model.
`cov.unscaled` | unscaled covariance matrix for the coefficients.
`sigma` | residual standard error.
`call` | the original function call.
`formula` | the model formula.
`terms` | a list with elements `"regressors"` and `"instruments"` containing the terms objects for the respective components.
`levels` | levels of the categorical regressors.
`contrasts` | the contrasts used for categorical regressors.
`model` | the full model frame (if `model = TRUE`).
`y` | the response vector (if `y = TRUE`).
`x` | a list with elements `"regressors"`, `"instruments"`, `"projected"`, containing the model matrices from the respective components (if `x = TRUE`). `"projected"` is the matrix of regressors projected on the image of the instruments.

### References

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

### See Also

`ivreg.fit`, `lm`, `lm.fit`

### Examples

    ## data
    data("CigarettesSW", package = "AER")
    CigarettesSW$rprice <- with(CigarettesSW, price/cpi)
    CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi)
    CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi)

    ## model
    fm <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + I(tax/cpi),
      data = CigarettesSW, subset = year == "1995")
    summary(fm)
    summary(fm, vcov = sandwich, df = Inf, diagnostics = TRUE)

    ## ANOVA
    fm2 <- ivreg(log(packs) ~ log(rprice) | tdiff, data = CigarettesSW, subset = year == "1995")
    anova(fm, fm2)

[Package AER version 1.2-5 Index]