Results 1  10
of
143
SplitSample Score Tests in Linear Instrumental Variables Regression
, 2007
"... In this paper we design two splitsample tests for subsets of structural coefficients in a linear Instrumental Variables (IV) regression. Sample splitting serves two purposes – 1) validity of the resultant tests does not depend on the identifiability of the coefficients being tested and 2) it combin ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
In this paper we design two splitsample tests for subsets of structural coefficients in a linear Instrumental Variables (IV) regression. Sample splitting serves two purposes – 1) validity of the resultant tests does not depend on the identifiability of the coefficients being tested and 2
A Robust Instrumental Variables Estimator
"... Abstract. The classical instrumental variables (IV) estimator is extremely sensitive to the presence of outliers in the sample. This is a concern as outliers can strongly distort the estimated effect of a given regressor on the dependent variable. Although outlier diagnostics exist, they frequently ..."
Abstract
 Add to MetaCart
Abstract. The classical instrumental variables (IV) estimator is extremely sensitive to the presence of outliers in the sample. This is a concern as outliers can strongly distort the estimated effect of a given regressor on the dependent variable. Although outlier diagnostics exist
Instrumental variable estimation in a survival context
"... Bias due to unobserved confounding can seldom be ruled out with certainty when using nonexperimental data to draw inferences about causal e¤ects. The instrumental variable (IV) design o¤ers under certain assumptions, the opportunity to tame confounding bias, without directly observing all confound ..."
Abstract
 Add to MetaCart
confounders. The IV approach is very well developed in the context of linear regression but also for certain generalized linear models with nonlinear link function. However, IV methods are not as well developed for censored survival outcomes. In this paper, the authors develop the instrumental variable
Consistent estimation with a large number of weak instruments
 Econometrica
, 2005
"... This paper analyzes the conditions under which consistent estimation can be achieved in instrumental variables (IV) regression when the available instruments are weak, in the localtozero sense of Staiger and Stock (1997) and using the manyinstrument framework of Morimune (1983) and Bekker (1994). ..."
Abstract

Cited by 59 (5 self)
 Add to MetaCart
This paper analyzes the conditions under which consistent estimation can be achieved in instrumental variables (IV) regression when the available instruments are weak, in the localtozero sense of Staiger and Stock (1997) and using the manyinstrument framework of Morimune (1983) and Bekker (1994
Selecting Instrumental Variables in a Data Rich Environment’,
 Journal of Time Series Econometrics
, 2009
"... Abstract Practitioners often have at their disposal a large number of instruments that are weakly exogenous for the parameter of interest, but using too many instruments can induce bias. We consider two ways of handling this problem. The first is to form principal components from the observed instr ..."
Abstract

Cited by 16 (2 self)
 Add to MetaCart
instruments, and the second is to reduce the number of instruments by subset variable selection. For the latter, we consider boosting, a method that does not require an a priori ordering of the instruments. We also suggest a way to preorder the instruments and then screen the instruments using the goodness
doi:10.1017/S0266466611000120 ASYMPTOTIC DISTRIBUTION OF JIVE IN A HETEROSKEDASTIC IV REGRESSION WITH MANY INSTRUMENTS
"... This paper derives the limiting distributions of alternative jackknife instrumental variables (JIV) estimators and gives formulas for accompanying consistent standard errors in the presence of heteroskedasticity and many instruments. The asymptotic framework includes the many instrument sequence of ..."
Abstract
 Add to MetaCart
, the number of instruments and the concentration parameter. This is in contrast to the asymptotic behavior of such classical instrumental variables estimators as limited information maximum likelihood, biascorrected twostage least squares, and twostage least squares, all of which are inconsistent
Estimating and testing multiple structural changes in multivariate regressions
 Econometrica
, 2007
"... We consider the problem of estimating and testing for multiple breaks in a single equation framework with regressors that are endogenous, i.e., correlated with the errors. First, we show based on standard assumptions about the regressors, instruments and errors that the second stage regression of th ..."
Abstract

Cited by 23 (4 self)
 Add to MetaCart
We consider the problem of estimating and testing for multiple breaks in a single equation framework with regressors that are endogenous, i.e., correlated with the errors. First, we show based on standard assumptions about the regressors, instruments and errors that the second stage regression
On the Inconsistency of Instrumental Variables Estimators for the Coefficients of Certain Dummy Variables
, 2011
"... In this paper we consider the asymptotic properties of the Instrumental Variables (IV) estimator of the parameters in a linear regression model with some random regressors, and other regressors that are dummy variables. The latter have the special property that the number of nonzero values is fixed ..."
Abstract
 Add to MetaCart
In this paper we consider the asymptotic properties of the Instrumental Variables (IV) estimator of the parameters in a linear regression model with some random regressors, and other regressors that are dummy variables. The latter have the special property that the number of nonzero values
Twostage instrumental variable methods for estimating the causal odds ratio: analysis of bias
 Statistics in Medicine 2011
"... We present closed form expressions of asymptotic bias for the causal odds ratio from two estimation approaches of instrumental variable logistic regression: 1) the twostage predictor substitution (2SPS) method; and 2) the twostage residual inclusion (2SRI) approach. Under the 2SPS approach, the fi ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
We present closed form expressions of asymptotic bias for the causal odds ratio from two estimation approaches of instrumental variable logistic regression: 1) the twostage predictor substitution (2SPS) method; and 2) the twostage residual inclusion (2SRI) approach. Under the 2SPS approach
2007): Optimal Inference for Instrumental Variables Regression with NonGaussian Errors, unpublished manuscript
"... Abstract. This paper is concerned with inference on the coe ¢ cient on the endogenous regressor in a linear instrumental variables model with a single endogenous regressor, nonrandom exogenous regressors and instruments, and i:i:d: errors whose distribution is unknown. It is shown that under mild sm ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
Abstract. This paper is concerned with inference on the coe ¢ cient on the endogenous regressor in a linear instrumental variables model with a single endogenous regressor, nonrandom exogenous regressors and instruments, and i:i:d: errors whose distribution is unknown. It is shown that under mild
Results 1  10
of
143