selection {sampleSelection} | R Documentation |
This is the frontend for estimating Heckman-style selection models either with one or two outcomes (also known as generalized tobit models).
For model specification and more details, see Henningsen and Toomet (2008) and the included vignette “Sample Selection Models”.
selection(selection, outcome, data = sys.frame(sys.parent()), subset, method = "ml", start = NULL, ys = FALSE, xs = FALSE, yo = FALSE, xo = FALSE, mfs = FALSE, mfo = FALSE, print.level = 0, ...) heckit( selection, outcome, data = sys.frame(sys.parent()), method = "2step", ... )
selection |
formula, the selection equation. |
outcome |
the outcome equation(s). Either a single equation (for tobit 2 models), or a list of two equations (tobit 5 models). |
data |
an optional data frame, list or environment (or object
coercible by |
subset |
an optional index vector specifying a subset of observations to be used in the fitting process. |
method |
how to estimate the model. Either |
start |
vector, initial values for the ML estimation. If
|
ys, yo, xs, xo, mfs, mfo |
logicals. If true, the response ( |
print.level |
integer. Various debugging information, higher value gives more information. |
... |
additional parameters for the corresponding fitting
functions |
The endogenous variable of the argument 'selection' must have exactly two levels (e.g. 'FALSE' and 'TRUE', or '0' and '1'). By default the levels are sorted in increasing order ('FALSE' is before 'TRUE', and '0' is before '1').
For tobit-2 (sample selection) models, only those observations are included in the second step estimation (argument 'outcome'), where this variable equals the second element of its levels (e.g. 'TRUE' or '1').
For tobit-5 (switching regression) models, in the second step the first outcome equation (first element of argument 'outcome') is estimated only for those observations, where this endogenous variable of the selections equation equals the first element of its levels (e.g. 'FALSE' or '0'). The second outcome equation is estimated only for those observations, where this variable equals the second element of its levels (e.g. 'TRUE' or '1').
NA
-s are allowed in the data. These are ignored if the
corresponding outcome is unobserved, otherwise observations which
contain NA
(either in selection or outcome) are
removed.
These selection models assume a known (multivariate normal) distribution of error terms. Because of this, the instruments (exclusion restrictions) are not necessary. However, if no instruments are supplied, the results are based solely on the assumption on multivariate normality. This may or may not be an appropriate assumption for a particular problem.
The (generic) function 'coef' ('coef.selection
')
can be used to extract the estimated coefficients.
The (generic) function 'vcov' ('vcov.selection
')
can be used to extract the estimated variance covariance matrix
of the coefficients.
The (generic) function 'print' ('print.selection
')
can be used to print a few results.
The (generic) function 'summary' ('summary.selection
')
can be used to obtain and print detailed results.
'selection' returns an object of class "selection". If the model estimated by Maximum Likelihood (argument method = "ml"), this object is a list, which has all the components of 'maxLik', and in addition the elements 'twoStep', 'start, 'param', termS, termO, and if requested 'ys', 'xs', 'yo', 'xo', 'mfs', and 'mfo'. If a tobit-2 (sample selection) model is estimated by the two-step method (argument method = "2step"), the returned object is list with components 'probit', 'coefficients', 'param', 'vcov', 'lm', 'sigma', 'rho', 'invMillsRatio', and 'imrDelta'. If a tobit-2 (sample selection) model is estimated by the two-step method (argument method = "2step"), the returned object is list with components 'coefficients', 'vcov', 'probit', 'lm1', 'lm2', 'rho1', 'rho2', 'sigma1', 'sigma2', 'termsS', 'termsO', 'param', and if requested 'ys', 'xs', 'yo', 'xo', 'mfs', and 'mfo'.
probit |
object of class 'probit' that contains the results of the 1st step (probit estimation) (only for two-step estimations). |
twoStep |
(only if initial values not given) results of the 2-step estimation, used for initial values |
start |
initial values for ML estimation |
termsS, termsO |
terms for the selection and outcome equation |
ys, xs, yo, xo, mfs, mfo |
response, matrix and frame of the selection- and outcome equations (as a list of two for the latter). NULL, if not requested. The response is represented internally as 0/1 integer vector with 0 denoting either the unobservable outcome (tobit 2) or the first selection (tobit 5). |
coefficients |
estimated coefficients, the complete model. coefficient for the Inverse Mills ratio is treated as a parameter (= rho * sigma). |
vcov |
variance covariance matrix of the estimated coefficients. |
param |
a list with following components: |
lm, lm1, lm2 |
objects of class 'lm' that contain the results
of the 2nd step estimation(s) of the outcome equation(s).
Note: the standard errors of this
estimation are biased, because they do not account for the
estimation of γ in the 1st step estimation
(the correct standard errors are returned by |
sigma, sigma1, sigma2 |
the standard error(s) of the error terms of the outcome equation(s). |
rho, rho1, rho2 |
the estimated correlation coefficient(s) between the error term of the selection equation and the outcome equation(s). |
invMillsRatio |
the inverse Mills Ratios calculated from the results of the 1st step probit estimation. |
imrDelta |
the δs calculated from the inverse Mills Ratios and the results of the 1st step probit estimation. |
The 2-step estimate of 'rho' may be outside of the [-1,1] interval. In that case the standard errors of invMillsRatio may be meaningless.
Arne Henningsen, Ott Toomet otoomet@ut.ee
Cameron, A. C. and Trivedi, P. K. (2005) Microeconometrics: Methods and Applications, Cambridge University Press.
Greene, W. H. (2003) Econometric Analysis, Fifth Edition, Prentice Hall.
Heckman, J. (1976) The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models, Annals of Economic and Social Measurement, 5(4), p. 475-492.
Johnston, J. and J. DiNardo (1997) Econometric Methods, Fourth Edition, McGraw-Hill.
Lee, L., G. Maddala and R. Trost (1980) Asymetric covariance matrices of two-stage probit and two-stage tobit methods for simultaneous equations models with selectivity. Econometrica, 48, p. 491-503.
Toomet, O. and A. Henningsen, (2008) Sample Selection Models in R: Package sampleSelection. Journal of Statistical Software 27(7), http://www.jstatsoft.org/v27/i07/
Wooldridge, J. M. (2003) Introductory Econometrics: A Modern Approach, 2e, Thomson South-Western.
## Greene( 2003 ): example 22.8, page 786 data( Mroz87 ) Mroz87$kids <- ( Mroz87$kids5 + Mroz87$kids618 > 0 ) # Two-step estimation summary( heckit( lfp ~ age + I( age^2 ) + faminc + kids + educ, wage ~ exper + I( exper^2 ) + educ + city, Mroz87 ) ) # ML estimation summary( selection( lfp ~ age + I( age^2 ) + faminc + kids + educ, wage ~ exper + I( exper^2 ) + educ + city, Mroz87 ) ) ## Wooldridge( 2003 ): example 17.5, page 590 data( Mroz87 ) # Two-step estimation summary( heckit( lfp ~ nwifeinc + educ + exper + I( exper^2 ) + age + kids5 + kids618, log( wage ) ~ educ + exper + I( exper^2 ), Mroz87, method = "2step" ) ) ## Cameron and Trivedi (2005): Section 16.6, page 553ff data( RandHIE ) subsample <- RandHIE$year == 2 & !is.na( RandHIE$educdec ) selectEq <- binexp ~ logc + idp + lpi + fmde + physlm + disea + hlthg + hlthf + hlthp + linc + lfam + educdec + xage + female + child + fchild + black outcomeEq <- lnmeddol ~ logc + idp + lpi + fmde + physlm + disea + hlthg + hlthf + hlthp + linc + lfam + educdec + xage + female + child + fchild + black # ML estimation cameron <- selection( selectEq, outcomeEq, data = RandHIE[ subsample, ] ) summary( cameron ) ## example using random numbers library( MASS ) nObs <- 1000 sigma <- matrix( c( 1, -0.7, -0.7, 1 ), ncol = 2 ) errorTerms <- mvrnorm( nObs, c( 0, 0 ), sigma ) myData <- data.frame( no = c( 1:nObs ), x1 = rnorm( nObs ), x2 = rnorm( nObs ), u1 = errorTerms[ , 1 ], u2 = errorTerms[ , 2 ] ) myData$y <- 2 + myData$x1 + myData$u1 myData$s <- ( 2 * myData$x1 + myData$x2 + myData$u2 - 0.2 ) > 0 myData$y[ !myData$s ] <- NA myOls <- lm( y ~ x1, data = myData) summary( myOls ) myHeckit <- heckit( s ~ x1 + x2, y ~ x1, myData, print.level = 1 ) summary( myHeckit ) ## example using random numbers with IV/2SLS estimation library( MASS ) nObs <- 1000 sigma <- matrix( c( 1, 0.5, 0.1, 0.5, 1, -0.3, 0.1, -0.3, 1 ), ncol = 3 ) errorTerms <- mvrnorm( nObs, c( 0, 0, 0 ), sigma ) myData <- data.frame( no = c( 1:nObs ), x1 = rnorm( nObs ), x2 = rnorm( nObs ), u1 = errorTerms[ , 1 ], u2 = errorTerms[ , 2 ], u3 = errorTerms[ , 3 ] ) myData$w <- 1 + myData$x1 + myData$u1 myData$y <- 2 + myData$w + myData$u2 myData$s <- ( 2 * myData$x1 + myData$x2 + myData$u3 - 0.2 ) > 0 myData$y[ !myData$s ] <- NA myHeckit <- heckit( s ~ x1 + x2, y ~ w, data = myData ) summary( myHeckit ) # biased! myHeckitIv <- heckit( s ~ x1 + x2, y ~ w, data = myData, inst = ~ x1 ) summary( myHeckitIv ) # unbiased ## tobit-5 example N <- 500 library(mvtnorm) vc <- diag(3) vc[lower.tri(vc)] <- c(0.9, 0.5, 0.6) vc[upper.tri(vc)] <- vc[lower.tri(vc)] eps <- rmvnorm(N, rep(0, 3), vc) xs <- runif(N) ys <- xs + eps[,1] > 0 xo1 <- runif(N) yo1 <- xo1 + eps[,2] xo2 <- runif(N) yo2 <- xo2 + eps[,3] a <- selection(ys~xs, list(yo1 ~ xo1, yo2 ~ xo2)) summary(a) ## tobit2 example vc <- diag(2) vc[2,1] <- vc[1,2] <- -0.7 eps <- rmvnorm(N, rep(0, 2), vc) xs <- runif(N) ys <- xs + eps[,1] > 0 xo <- runif(N) yo <- (xo + eps[,2])*(ys > 0) a <- selection(ys~xs, yo ~xo) summary(a)