Printed from https://ideas.repec.org
IDEAS/RePEc search
IDEAS search now includes synonyms. If you feel that some synonyms are missing, you are welcome to suggest them for inclusion
- Jeremy Rubin (2014): Nonparametric Instrumental Variable Estimation
Nonparametric Instrumental Variable Estimation
RePEc:eei:journl:v:57:y:2014:i:2:p:1-29 Save to MyIDEAS - Joshua D. Angrist & Alan B. Krueger (1993): Split Sample Instrumental Variables
Instrumental Variables (IV) estimates tend to be biased in the same direction as Ordinary Least Squares (OLS) in finite samples if the instruments are weak. To address this problem we propose a new IV estimator which we call Split Sample Instrumental Variables (SSIV). ... We use this estimate of the attenuation bias to derive an estimator that is asymptotically unbiased as the number of instruments tends to infinity, holding the number of observations per instrument fixed. We label this new estimator Unbiased Split Sample Instrumental Variables (USSIV).
RePEc:pri:indrel:320 Save to MyIDEAS - Windmeijer, Frank (1995): A Note on R2 in the Instrumental Variables Model
The properties of two goodness-of-fit measures are analyzed for the instrumental variables model.
RePEc:pra:mprapa:102511 Save to MyIDEAS - Whitney K. Newey & James L. Powell (2017): Instrumental variables estimation for nonparametric models
Whitney Newey and James Powell, founding CeMMAP Fellows, wrote an influential paper on instrumental variable estimation of an additive error non-parametrically specified structural equation, presented at the December 1988 North American Winter Meetings of the Econometric Society. A version circulated in 1989 but the results were not published until 14 years later: “Instrumental Variable Estimation of Nonparametric Models”, Econometrica, 71(5), (September 2003), 1565-1578.
RePEc:ifs:cemmap:07/17 Save to MyIDEAS - Joffe Marshall M & Small Dylan & Ten Have Thomas & Brunelli Steve & Feldman Harold I (2008): Extended Instrumental Variables Estimation for Overall Effects
We consider a method for extending instrumental variables methods in order to estimate the overall effect of a treatment or exposure. The approach is designed for settings in which the instrument influences both the treatment of interest and a secondary treatment also influenced by the primary treatment. We demonstrate that, while instrumental variables methods may be used to estimate the joint effects of the primary and secondary treatments, they cannot by themselves be used to estimate the overall effect of the primary treatment. However, instrumental variables methods may be used in conjunction with approaches for estimating the effect of the primary on the secondary treatment to estimate the overall effect of the primary treatment. We consider extending the proposed methods to deal with confounding of the effect of the instrument, mediation of the effect of the instrument by other variables, failure-time outcomes, and time-varying secondary treatments.
RePEc:bpj:ijbist:v:4:y:2008:i:1:n:4 Save to MyIDEAS - Sakata, S. (1998): Instrumental Variable Estimation Based on Mean Absolute Deviation
We propose a general estimation principle based on the assumption that instrumental variables (IV) do not explain the error term in a structural equation.
RePEc:fth:michet:98-08 Save to MyIDEAS - Klemp, Marc & Casey, Gregory (2018): Instrumental Variables in the Long Run
We study the interpretation of instrumental variable (IV) regressions that use historical or geographical instruments for contemporary endogenous regressors. We find that conventional IV regressions generally cannot estimate the long-run causal effect of an endogenous explanatory variable when there is a time gap between the instrument and the endogenous variable.
RePEc:cpr:ceprdp:12980 Save to MyIDEAS - Santos, Andres (2011): Instrumental variable methods for recovering continuous linear functionals
This paper develops methods for estimating continuous linear functionals in nonparametric instrumental variable problems.
RePEc:eee:econom:v:161:y:2011:i:2:p:129-146 Save to MyIDEAS - Qian, Hang (2011): Bayesian inference with monotone instrumental variables
Sampling variations complicate the classical inference on the analogue bounds under the monotone instrumental variables assumption, since point estimators are biased and confidence intervals are difficult to construct.
RePEc:pra:mprapa:32672 Save to MyIDEAS - Casey, Gregory & Klemp, Marc (2016): Instrumental Variables in the Long Run
In the field of long-run economic growth, it is common to use historical or geographical variables as instruments for contemporary endogenous regressors. We study the interpretation of these conventional instrumental variable (IV) regressions in a simple, but general, framework.
RePEc:pra:mprapa:68696 Save to MyIDEAS