Open AccessPosted Content
Regression standard errors in clustered samples
TLDR
In this paper, the residuals are sorted and the observation is located in the residual corresponding to the quantile in question, taking into account weights if they are applied, and the square root of the sum of the weights is calculated.Abstract:
We first sort the residuals and locate the observation in the residuals corresponding to the quantile in question, taking into account weights if they are applied. We then calculate wn, the square root of the sum of the weights. Unweighted data is equivalent to weighted data where each observation has weight 1, resulting in wn p n. For analytically weighted data, the weights are rescaled so that the sum of the weights is the sum of the observations, resulting in p n again. For frequency weighted data, wn literally is the square of the sum of the weights.read more
Citations
More filters
Journal ArticleDOI
Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches
TL;DR: In this article, the authors examine the different methods used in the literature and explain when the different approaches yield the same (and correct) standard errors and when they diverge, and give researchers guidance for their use.
Journal ArticleDOI
A Practitioner’s Guide to Cluster-Robust Inference
TL;DR: This work considers statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters, when the number of clusters is large and default standard errors can greatly overstate estimator precision.
Journal ArticleDOI
Robust Inference with Multi-way Clustering
TL;DR: The authors proposed a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit, and GMM that enables cluster-robust inference when there is two-way or multiway clustering that is nonnested.
Journal ArticleDOI
Robust standard errors for panel regressions with cross–sectional dependence
TL;DR: In this article, the authors present a new Stata program, xtscc, that estimates pooled or dual least squares/weighted least squares regression and xed-eects (within) regression models with Driscoll and Kraay (Review of Economics and Statistics 80: 549{560) standard errors.
Journal ArticleDOI
The Real Effects of Financial Constraints: Evidence from a Financial Crisis
Murillo Campello,Murillo Campello,John R. Graham,John R. Graham,Campbell R. Harvey,Campbell R. Harvey +5 more
TL;DR: The authors survey 1,050 CFOs in the US, Europe, and Asia to assess whether their firms are credit constrained during the global financial crisis of 2008 and find that constrained firms planned deeper cuts in tech spending, employment, and capital spending.
References
More filters
Journal ArticleDOI
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
TL;DR: In this article, a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic is presented, which does not depend on a formal model of the structure of the heteroSkewedness.
The behavior of maximum likelihood estimates under nonstandard conditions
TL;DR: In this paper, the authors prove consistency and asymptotic normality of maximum likelihood estimators under weaker conditions than usual, such that the true distribution underlying the observations belongs to the parametric family defining the estimator, and the regularity conditions do not involve the second and higher derivatives of the likelihood function.