Combining Two Consistent Estimators

This chapter shows how a weighted average of a forward and reverse Jackknife IV estimator (JIVE) yields estimators that are robust against heteroscedasticity and many instruments. These estimators, called HFUL (Heteroscedasticity robust Fuller) and HLIM (Heteroskedasticity robust limited information maximum likelihood (LIML)) were introduced by Hausman, Newey, Woutersen, Chao, and Swanson (2012), but without derivation. Combining consistent estimators is a theme that is associated with Jerry Hausman and, therefore, we present this derivation in this volume. Additionally, and in order to further understand and interpret HFUL and HLIM in the context of jackknife type variance ratio estimators, we show that a new variant of HLIM, under specific grouped data settings with dummy instruments, simplifies to the Bekker and van der Ploeg (2005) MM (method of moments) estimator

An Expository Note on the Existence of Moments of Fuller and HFUL Estimators

In a recent paper, Hausman, Newey, Woutersen, Chao, and Swanson (2012) propose a new estimator, HFUL (Heteroscedasticity robust Fuller), for the linear model with endogeneity. This estimator is consistent and asymptotically normally distributed in the many instruments and many weak instruments asymptotics. Moreover, this estimator has moments, just like the estimator by Fuller (1977). The purpose of this note is to discuss at greater length the existence of moments result given in Hausman et al. (2012). In particular, we intend to answer the following questions: Why does LIML not have moments? Why does the Fuller modification lead to estimators with moments? Is normality required for the Fuller estimator to have moments? Why do we need a condition such as Hausman et al. (2012), Assumption 9? Why do we have the adjustment formula

Torsional-flexural buckling of single equal-legged angle struts

Thesis (M. Ing.) -- University of Stellenbosch, 1992.One copy microfiche.Full text to be digitised and attached to bibliographic record

Avoidable mortality: The mediating role of communication in health IT

The adoption of health IT transforms communication between care providers and patients. Unfortunately, research on the transformation of communication has produced conflicting results, creating tension regarding its efficacy among healthcare professionals. In this paper, we propose that nurse and physician communication performance mediate the relationship between health IT implementation and patient outcomes. We test the mediating role of communication with a hospital-level data set spanning 2011 through 2015. The specific health information technologies we investigate include EMR documentation, computerized physician order entry (CPOE) systems, clinical decision support (CDS) systems, and health information exchanges (HIE). Our results provide that EMR documentation, CPOE, and HIE directly improve communication between care providers and patients as well as patient outcomes. Further, nurse-patient and physician-patient communication mediates the relationship between health IT implementation and patient outcomes. The mediating effect extends the positive benefits to patient outcomes following technology implementation. We also find that poor communication with patients directly increases mortality, decreases satisfaction, and decreases loyalty. Surprisingly, CDS has a negative relationship on communication and patient outcomes. Our findings contribute to the information systems and healthcare literatures by demonstrating the need to account for the multidimensional nature of healthcare and by providing context for the positive and negative effects previously discovered. Furthermore, the results offer practical and theoretical implications for leveraging specific health IT adoption and for realigning federal incentive structures for hospitals.24 month embargo; available online: 28 February 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Instrumental variable estimation with heteroskedasticity and many instruments

This paper gives a relatively simple, well behaved solution to the problem of many instruments in heteroskedastic data. Such settings are common in microeconometric applications where many instruments are used to improve efficiency and allowance for heteroskedasticity is generally important. The solution is a Fuller (1977) like estimator and standard errors that are robust to heteroskedasticity and many instruments. We show that the estimator has finite moments and high asymptotic efficiency in a range of cases. The standard errors are easy to compute, being like White's (1982), with additional terms that account for many instruments. They are consistent under standard, many instrument, and many weak instrument asymptotics. We find that the estimator is asymptotically as efficient as the limited‐information maximum likelihood (LIML) estimator under many weak instruments. In Monte Carlo experiments, we find that the estimator performs as well as LIML or Fuller (1977) under homoskedasticity, and has much lower bias and dispersion under heteroskedasticity, in nearly all cases considered. Keywords: Instrumental variables; heteroskedasticity; many instruments; jack-knif

Testing overidentifying restrictions with many instruments and heteroskedasticity

This paper gives a test of overidentifying restrictions that is robust to many instruments and heteroskedasticity. It is based on a jackknife version of the overidentifying test statistic. Correct asymptotic critical values are derived for this statistic when the number of instruments grows large, at a rate up to the sample size. It is also shown that the test is valid when the number of instruments is fixed and there is homoskedasticity. This test improves on recently proposed tests by allowing for heteroskedasticity and by avoiding assumptions on the instrument projection matrix. This paper finds in Monte Carlo studies that the test is more accurate and less sensitive to the number of instruments than the Hausman–Sargan or GMM tests of overidentifying restrictions.National Science Foundation (U.S.

Testing Overidentifying Restrictions with Many Instruments and Heteroskedasticity

This paper gives a test of overidentifying restrictions that is robust to many instruments and heteroskedasticity. It is based on a jackknife version of the Sargan test statistic, having a numerator that is the objective function minimized by the JIVE2 estimator of Angrist, Imbens, and Krueger (1999). Correct asymptotic critical values are derived for this test when the number of instruments grows large, at a rate up to the sample size. It is also shown that the test is valid when the number instruments is fixed and there is homoskedasticity. This test improves on recently proposed tests by allowing for heteroskedasticity and by avoiding assumptions on the instrument projection matrix. The asymptotics is based on the heteroskedasticity robust many instrument asymptotics of Chao et. al. (2010).heteroskedasticity, instrumental variables, jackknife estimation, many instruments, weak instruments

Instrumental Variable Estimation with Heteroskedasticity and Many Instruments

This paper gives a relatively simple, well behaved solution to the problem of many instruments in heteroskedastic data. Such settings are common in microeconometric applications where many instruments are used to improve efficiency and allowance for heteroskedasticity is generally important. The solution is a Fuller (1977) like estimator and standard errors that are robust to heteroskedasticity and many instruments. We show that the estimator has finite moments and high asymptotic efficiency in a range of cases. The standard errors are easy to compute, being like White’s (1982), with additional terms that account for many instruments. They are consistent under standard, many instrument, and many weak instrument asymptotics. Based on a series of Monte Carlo experiments, we find that the estimators perform as well as LIML or Fuller (1977) under homoskedasticity, and have much lower bias and dispersion under heteroskedasticity, in nearly all cases considered.Instrumental Variables , Jackknife, Many Instruments, Heteroskedasticity