SteinIV (version 0.1-1)

sps.internal: Internal function for Semi-parametric Stein-like (SPS) estimator.

Description

Computes the SPS estimator for a two-stage structural model, as well as a sample estimate of the alpha parameter controlling the degree of combination between the OLS and TSLS estimators.

Usage

sps.internal(y,X,Z,REF="TSLS",ALPHA=FALSE,n.btj=10)

Arguments

y

Numeric: A vector of observations, representing the outcome variable.

X

Numeric: A matrix of observations, whose number of columns corresponds to the number of predictors in the model, and the number of rows should be conformal with the number of entries in $y$. This matrix may contain both endogenous and exogenous variables.

Z

Numeric: A matrix of observations representing the intrumental variables (IVs) in the first-stage structural equation. The number of IVs should be at least as large as the number of endogenous variables in $X$.

REF

Character: Controls the choice of the reference estimator in the SPS framework. This can accept two values: "TSLS" or "JIVE", with the former being the default option. The alternative estimator is always the OLS estimator.

ALPHA

Logical: If TRUE, the function returns the value of the sample estimate of the parameter controlling the respective contribution of the reference estimator (by default, this is the TSLS estimator), and the one of the alternative estimator (by default, this is the OLS estimator).

n.btj

Numeric: The number of boostrap iterations performed, when computing the SPS estimator, when using the JIVE as reference estimator. This option is only relevant, when JIVE has been selected as the reference estimator. These iterations are used to compute the various components entering in the calculation of the SPS estimator.

Value

list

The first term (est) is a vector of estimates for the coefficients of interest, and the second term (alpha) representing the estimate of the contribution of the OLS to the combined SPS estimator.

Details

See documentaion for the sps.est function. Users should use the sps.est function, instead.

References

Judge, G.G. and Mittelhammer, R.C. (2004). A semiparametric basis for combining esti- mation problems under quadratic loss. Journal of the American Statistical Association, 99(466), 479--487.

Judge, G.G. and Mittelhammer, R.C. (2012a). An information theoretic approach to econo- metrics. Cambridge University Press.

Judge, G. and Mittelhammer, R. (2012b). A risk superior semiparametric estimator for over-identified linear models. Advances in Econometrics, 237--255.

Judge, G. and Mittelhammer, R. (2013). A minimum mean squared error semiparametric combining estimator. Advances in Econometrics, 55--85.

Mittelhammer, R.C. and Judge, G.G. (2005). Combining estimators to improve structural model estimation and inference under quadratic loss. Journal of econometrics, 128(1), 1--29.