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Estimating Standard Errors in Finance Panel Data Sets : Comparing Approaches

Petersen, Mitchell A. - 2005

In both corporate finance and asset pricing empirical work, researchers are often confronted with panel data. In these data sets, the residuals may be correlated across firms and across time, and OLS standard errors can be biased. Historically, the two literatures have used different solutions...

Persistent link: https://www.econbiz.de/10012467404

Rethinking Performance Evaluation

Harvey, Campbell R. - 2016

We show that the standard equation-by-equation OLS used in performance evaluation ignores information in the alpha population and leads to severely biased estimates for the alpha population. We propose a new framework that treats fund alphas as random effects. Our framework allows us to make...

Persistent link: https://www.econbiz.de/10012456541

Broken or Fixed Effects?

Gibbons, Charles E. - 2014

We replicate eight influential papers to provide empirical evidence that, in the presence of heterogeneous treatment effects, OLS with fixed effects (FE) is generally not a consistent estimator of the average treatment effect (ATE). We propose two alternative estimators that recover the ATE in...

Persistent link: https://www.econbiz.de/10012458325

Estimating Log Models : To Transform or Not to Transform?

Manning, Willard G. - 1999

-variety statistical problems - skewness, kurtosis, and heteroscedasticity - can lead to an appreciable bias for some estimators or …

Persistent link: https://www.econbiz.de/10012471372

Testing The Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions

Cumby, Robert E. - 1990

This paper derives the asymptotic distribution for a vector of sample autocorrelations of regression residuals from a quite general linear model. The asymptotic distribution forms the basis for a test of the null hypothesis that the regression error follows a moving average of order q...

Persistent link: https://www.econbiz.de/10012475572

An Adaptive Nonlinear Least Square Algorithm

Dennis, John E. - 1977

NL2SOL is a modular program for solving the nonlinear least-squares problem that incorporates a number of novel features. It maintains a secant approximation S to the second-order part of the least-squares Hessian and adaptively decides when to use this approximation. We have found it very...

Persistent link: https://www.econbiz.de/10012478936

On the Estimation of Treatment Effects with Endogenous Misreporting

Nguimkeu, Pierre - 2017

effect having an opposite sign from the true effect. We present an expression for the asymptotic bias of both OLS and IV … estimators and discuss the conditions under which sign reversal may occur. We provide a method for eliminating this bias when …

Persistent link: https://www.econbiz.de/10012453596

One Instrument to Rule Them All : The Bias and Coverage of Just-ID IV

Angrist, Joshua D.; Kolesar, Michal - National Bureau of Economic Research - 2021

-ID) IV estimates using a single instrument are also biased, but the importance of weak-instrument bias in just-ID IV … to be reliable. We then show that an estimator that's unbiased given a population first-stage sign restriction has bias … shown to halve the median bias of conventional IV without reducing coverage. To the extent that sign-screening is already …

Persistent link: https://www.econbiz.de/10012660095

When is TSLS Actually LATE?

Blandhol, Christine; Bonney, John; Mogstad, Magne; … - National Bureau of Economic Research - 2022

Linear instrumental variable estimators, such as two-stage least squares (TSLS), are commonly interpreted as estimating positively weighted averages of causal effects, referred to as local average treatment effects (LATEs). We examine whether the LATE interpretation actually applies to the types...

Persistent link: https://www.econbiz.de/10012814484

A Score Based Approach to Wild Bootstrap Inference

Kline, Patrick M. - 2010

We propose a generalization of the wild bootstrap of Wu (1986) and Liu (1988) based upon perturbing the scores of M-estimators. This "score bootstrap" procedure avoids recomputing the estimator in each bootstrap iteration, making it substantially less costly to compute than the conventional...

Persistent link: https://www.econbiz.de/10012462529