Instrumental Variables Regression with Measurement Errors and Multicollinearity in Instruments
Authors
Abstract:
In this paper we obtain a consistent estimator when there exist some measurement errors and multicollinearity in the instrumental variables in a two stage least square estimation of parameters. We investigate the asymptotic distribution of the proposed estimator and discuss its properties using some theoretical proofs and a simulation study. A real numerical application is also provided for more illustration.
similar resources
Instrumental Variables Quantile Regression for Panel Data with Measurement Errors∗
This paper develops an instrumental variables estimator for quantile regression in panel data with fixed effects. Asymptotic properties of the instrumental variables estimator are studied for large N and T when Na/T → 0, for some a > 0. Wald and Kolmogorov-Smirnov type tests for general linear restrictions are developed. The estimator is applied to the problem of measurement errors in variables...
full textSpecification Test for Instrumental Variables Regression with Many Instruments∗
This paper considers specification testing for instrumental variables estimation in the presence of many instruments. The test is similar to the overidentifying restrictions test of Sargan (1958) but the test statistic asymptotically follows the standard normal distribution under the null hypothesis when the number of instruments is proportional to the sample size. It turns out that the new tes...
full textOptimal Inference for Instrumental Variables Regression with non-Gaussian Errors
A . This paper is concerned with inference on the coefficient 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 smoothness conditions on the error distribution it is possible develop tests which are “nearly” efficient...
full textOptimal Inference for Instrumental Variables Regression with non-Gaussian Errors
A . This paper is concerned with inference on the coefficient 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 smoothness conditions on the error distribution it is possible develop tests which are “nearly” efficient...
full textInference on a Structural Parameter in Instrumental Variables Regression with Weak Instruments
In this paper we consider the problem of making inference on a structural parameter in instrumental variables regression when the instruments are only weakly correlated with the endogenous explanatory variables. Adopting a local-to-zero assumption as in Staiger and Stock (1994) on the coe cients of the instruments in the rst stage equation, the asymptotic distributions of various test statistic...
full textExactly Distribution-free Inference in Instrumental Variables Regression with Possibly Weak Instruments
This paper introduces a rank-based test for the instrumental variables regression model that dominates the Anderson-Rubin test in terms of Þnite sample size and asymptotic power in certain circumstances. The test has correct size for any distribution of the errors with weak or strong instruments. The test has noticeably higher power than the Anderson-Rubin test when the error distribution has t...
full textMy Resources
Journal title
volume 19 issue 2
pages 15- 31
publication date 2020-12
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023