tsls: Two-Stage Least Squares

In sem: Structural Equation Models

Two-Stage Least Squares

Description

Fits a regression equation, such as an equation in a structural-equation model, 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

## S3 method for class 'formula'
tsls(formula, instruments, data, subset, weights, 
	na.action, contrasts=NULL, ...)
## Default S3 method:
tsls(y, X, Z, w, names=NULL, ...)

## S3 method for class 'tsls'
print(x, ...)
## S3 method for class 'tsls'
summary(object, digits=getOption("digits"), ...)
## S3 method for class 'summary.tsls'
print(x, ...)
## S3 method for class 'tsls'
anova(object, model.2, s2, dfe, ...)

## S3 method for class 'tsls'
fitted(object, ...)
## S3 method for class 'tsls'
residuals(object, ...)
## S3 method for class 'tsls'
coef(object, ...)
## S3 method for class 'tsls'
vcov(object, ...)

Arguments

formula	model formula for structural equation to be estimated; a regression constant is implied if not explicitly omitted.
instruments	one-sided model formula specifying instrumental variables.
data	an optional data frame containing the variables in the model. By default the variables are taken from the environment from which tsls is called.
subset	an optional vector specifying a subset of observations to be used in fitting the model.
weights, w	an optional vector of weights to be used in the fitting process; if specified should be a non-negative numeric vector with one entry for each observation, to be used to compute weighted 2SLS estimates.
na.action	a function that indicates what should happen when the data contain NAs. The default is set by the na.action option.
contrasts	an optional list. See the contrasts.arg argument of model.matrix.default.
y	Response-variable vector.
X	Matrix of predictors, including a constant (if one is in the model).
Z	Matrix of instrumental variables, including a constant (if one is in the model).
names	optional character vector of names for the columns of the X matrix.
x, object, model.2	objects of class tsls returned by tsls.formula (or of class summary.tsls), for anova containing nested models to be compared by an incremental F-test. One model should be nested in the other; the order of models is immaterial.
s2	an optional estimate of error variance for the denominator of the F-test. If missing, the error-variance estimate is taken from the larger model.
dfe	optional error degrees of freedom, to be specified when an estimate of error variance is given.
digits	number of digits for summary output.
...	arguments to be passed down.

Value

tsls.formula returns an object of class tsls, with the following components:

n	number of observations.
p	number of parameters.
coefficients	parameter estimates.
V	estimated covariance matrix of coefficients.
s	residual standard error.
residuals	vector of residuals.
response	vector of response values.
X	model matrix.
Z	instrumental-variables matrix.
response.name	name of response variable, or expression evaluating to response.
formula	model formula.
instruments	one-sided formula for instrumental variables.

Author(s)

John Fox [e-mail]

References

Fox, J. (1979) Simultaneous equation models and two-stage least-squares. In Schuessler, K. F. (ed.) Sociological Methodology 1979, Jossey-Bass.

Greene, W. H. (1993) Econometric Analysis, Second Edition, Macmillan.

See Also

sem

Examples

summary(tsls(Q ~ P + D, ~ D + F + A, data=Kmenta))     # demand equation

summary(tsls(Q ~ P + F + A, ~ D + F + A, data=Kmenta)) # supply equation

anova(tsls(Q ~ P + F + A, ~ D + F + A, data=Kmenta),
    tsls(Q ~ 1, ~ D + F + A, data=Kmenta))

sem documentation built on Sept. 12, 2024, 7:39 a.m.