ivreg | R Documentation |

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.

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

`formula, instruments` |
formula specification(s) of the regression
relationship and the instruments. Either |

`data` |
an optional data frame containing the variables in the model.
By default the variables are taken from the environment of the |

`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 |

`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 |

`model, x, y` |
logicals. If |

`...` |
further arguments passed to |

`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.

`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 |

`offset` |
either the offset used (if any) or |

`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 |

`levels` |
levels of the categorical regressors. |

`contrasts` |
the contrasts used for categorical regressors. |

`model` |
the full model frame (if |

`y` |
the response vector (if |

`x` |
a list with elements |

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

`ivreg.fit`

, `lm`

, `lm.fit`

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

AER documentation built on June 14, 2022, 5:06 p.m.

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