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Mark J Ready - One of the best experts on this subject based on the ideXlab platform.
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on the Robustness of size and book to market in cross sectional Regressions
Journal of Finance, 1997Co-Authors: Peter J Knez, Mark J ReadyAbstract:We use a Robust Regression Estimator to analyze the risk premia on size and bookto-market. We find that the risk premium on size that was estimated by Fama and French (1992) completely disappears when the 1 percent most extreme observations are trimmed each month. We also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the 16 months with the most extreme coefficients. We argue that further investigation of these results could lead to an understanding of the economic forces underlying the size effect, and may also yield important insights into how firms grow. IN RELATED ARTICLES, FAMA and French (1992 and 1993) argue that size and book-to-market play a dominant role in explaining cross-sectional differences in expected returns.' In the first article, Fama and French show that there appear to be risk premia associated with these factors, and in the second they show that long-short portfolios constructed to maximize the "loading" on these factors are useful in explaining the common variation in returns. In this article, we investigate the Robustness of the estimated risk premia for size and book-to-market. If the estimates are driven by a small subset of firms or months, then our Robust techniques will allow us to isolate these influential observations. We believe that identifying and investigating these observations could deepen our understanding of the economic reasons for the risk premia associated with size and book-to-market, which is particularly important since neither of these factors is identified with any well articulated equilibrium asset pricing model.2
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on the Robustness of size and book to market in cross sectional Regressions
Journal of Finance, 1997Co-Authors: Peter J Knez, Mark J ReadyAbstract:The authors use a Robust Regression Estimator to analyze the risk premia on size and book-to-market. They find that the risk premium on size that was estimated by Eugene F. Fama and Kenneth R. French (1992) completely disappears when the 1 percent most extreme observations are trimmed each month. The authors also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the sixteen months with the most extreme coefficients. They argue that further investigation of these results could lead to an understanding of the economic forces underlying the size effect, and may also yield important insights into how firms grow. Copyright 1997 by American Finance Association.
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on the Robustness of size and book to market in cross sectional Regressions
1997Co-Authors: Peter J Knez, Mark J ReadyAbstract:We use a Robust Regression Estimator to analyze the risk premia on size and book-to-market. We find that the risk premium on size that was estimated by Fama and French (1992) completely disappears when the 1% most extreme observations are trimmed each month. We also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the 16 months with the most extreme coefficients. We argue that further investigation of these results could lead to an understanding of the economic forces underlying the size effect, and may also yield important insights into how firms grow.
Peter J Knez - One of the best experts on this subject based on the ideXlab platform.
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on the Robustness of size and book to market in cross sectional Regressions
Journal of Finance, 1997Co-Authors: Peter J Knez, Mark J ReadyAbstract:We use a Robust Regression Estimator to analyze the risk premia on size and bookto-market. We find that the risk premium on size that was estimated by Fama and French (1992) completely disappears when the 1 percent most extreme observations are trimmed each month. We also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the 16 months with the most extreme coefficients. We argue that further investigation of these results could lead to an understanding of the economic forces underlying the size effect, and may also yield important insights into how firms grow. IN RELATED ARTICLES, FAMA and French (1992 and 1993) argue that size and book-to-market play a dominant role in explaining cross-sectional differences in expected returns.' In the first article, Fama and French show that there appear to be risk premia associated with these factors, and in the second they show that long-short portfolios constructed to maximize the "loading" on these factors are useful in explaining the common variation in returns. In this article, we investigate the Robustness of the estimated risk premia for size and book-to-market. If the estimates are driven by a small subset of firms or months, then our Robust techniques will allow us to isolate these influential observations. We believe that identifying and investigating these observations could deepen our understanding of the economic reasons for the risk premia associated with size and book-to-market, which is particularly important since neither of these factors is identified with any well articulated equilibrium asset pricing model.2
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on the Robustness of size and book to market in cross sectional Regressions
Journal of Finance, 1997Co-Authors: Peter J Knez, Mark J ReadyAbstract:The authors use a Robust Regression Estimator to analyze the risk premia on size and book-to-market. They find that the risk premium on size that was estimated by Eugene F. Fama and Kenneth R. French (1992) completely disappears when the 1 percent most extreme observations are trimmed each month. The authors also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the sixteen months with the most extreme coefficients. They argue that further investigation of these results could lead to an understanding of the economic forces underlying the size effect, and may also yield important insights into how firms grow. Copyright 1997 by American Finance Association.
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on the Robustness of size and book to market in cross sectional Regressions
1997Co-Authors: Peter J Knez, Mark J ReadyAbstract:We use a Robust Regression Estimator to analyze the risk premia on size and book-to-market. We find that the risk premium on size that was estimated by Fama and French (1992) completely disappears when the 1% most extreme observations are trimmed each month. We also show that the negative average of the monthly size coefficients reported by Fama and French can be entirely explained by the 16 months with the most extreme coefficients. We argue that further investigation of these results could lead to an understanding of the economic forces underlying the size effect, and may also yield important insights into how firms grow.
Rand R. Wilcox - One of the best experts on this subject based on the ideXlab platform.
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linear Regression Robust heteroscedastic confidence bands that have some specified simultaneous probability coverage
Journal of Applied Statistics, 2017Co-Authors: Rand R. WilcoxAbstract:ABSTRACTLet M(Y|X)=β0+β1X be some conditional measure of location associated with the random variable Y, given X, where the unknown parameters β0 and β1 are estimated based on the random sample (X1,Y1),…,(Xn,Yn). When using the ordinary least squares (OLS) Estimator and M(Y|X)=E(Y|X), several methods for computing a confidence band have been derived that are aimed at achieving some specified simultaneous probability coverage assuming a homoscedastic error term and normality. There is an extant technique that allows heteroscedasticity, but a remaining concern is that the OLS Estimator is not Robust. Extant results indicate how a confidence interval can be computed via a Robust Regression Estimator when there is heteroscedasticity and attention is focused on a single value of X. The paper extends this method by describing a heteroscedastic technique for computing a confidence interval for each M(Y|X=Xi) (i=1,…,n) such that the simultaneous probability coverage has some specified value. The small-sample prop...
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A HETEROSCEDASTIC METHOD FOR COMPARING Regression LINES AT SPECIFIED DESIGN POINTS WHEN USING A Robust Regression Estimator.
Journal of data science : JDS, 2013Co-Authors: Rand R. WilcoxAbstract:It is well known that the ordinary least squares (OLS) Regression Estimator is not Robust. Many Robust Regression Estimators have been proposed and inferential methods based on these Estimators have been derived. However, for two independent groups, let θj(X) be some conditional measure of location for the jth group, given X, based on some Robust Regression Estimator. An issue that has not been addressed is computing a 1 − α confidence interval for θ1(X) − θ2(X) in a manner that allows both within group and between group hetereoscedasticity. The paper reports the finite sample properties of a simple method for accomplishing this goal. Simulations indicate that, in terms of controlling the probability of a Type I error, the method performs very well for a wide range of situations, even with a relatively small sample size. In principle, any Robust Regression Estimator can be used. The simulations are focused primarily on the Theil–Sen Estimator, but some results using Yohai’s MM-Estimator, as well as the Koenker and Bassett quantile Regression Estimator, are noted. Data from the Well Elderly II study, dealing with measures of meaningful activity using the cortisol awakening response as a covariate, are used to illustrate that the choice between an extant method based on a nonparametric Regression Estimator, and the method suggested here, can make a practical difference.
R Powell - One of the best experts on this subject based on the ideXlab platform.
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a Robust approach to the calculation of paleostress fields from fault plane data
Journal of Structural Geology, 1992Co-Authors: Thomas M Will, R PowellAbstract:Algebraic methods combined with Robust Regression techniques are used to calculate paleostress tensors from field observations on faults. Previously, such calculations have involved least-squares Regression; however such Regression estimates are likely to break down and produce meaningless results if data are included that are inconsistent with the main body of the data. Such inconsistent data are called outliers, i.e. measurements that are discrepant with respect to the majority of the observations. In two dimensions, the trend of the main body of the data, and their outliers, can be found by plotting the data and examining them visually. Least-squares Regression can then be safely applied to the data-set once the outliers have been manually removed. However, the paleostress problem possesses a four-dimensional parameter space, and, as a consequence, this approach cannot be used. To overcome this difficulty, a Robust Regression Estimator, involving the least median of squares (LMS), is applied to the estimation of paleostress tensors from fault plane data; not only can the parameters of the tensor be estimated but also the quality of the data-set assessed. For a data-set that is composed of data from several stress fields the dominant reduced stress tensor will be found by the LMS Estimator. A computer program, PSALMS, that calculates paleostress directions using this Robust Estimator is presented.
Bent Nielsen - One of the best experts on this subject based on the ideXlab platform.
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an analysis of the indicator saturation Estimator as a Robust Regression Estimator
CREATES Research Papers, 2008Co-Authors: Soren Johansen, Bent NielsenAbstract:An algorithm suggested by Hendry (1999) for estimation in a Regression with more regressors than observations, is analyzed with the purpose of finding an Estimator that is Robust to outliers and structural breaks. This Estimator is an example of a one-step M-Estimator based on Huber's skip function. The asymptotic theory is derived in the situation where there are no outliers or structural breaks using empirical process techniques. Stationary processes, trend stationary autoRegressions and unit root processes are considered.
