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29
Estimating standard errors in finance panel data sets: comparing approaches.
- Review of Financial Studies
, 2009
"... Abstract 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 solut ..."
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Cited by 890 (7 self)
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Abstract 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 to this problem. Corporate finance has relied on clustered standard errors, while asset pricing has used the Fama-MacBeth procedure to estimate standard errors. This paper examines the different methods used in the literature and explains when the different methods yield the same (and correct) standard errors and when they diverge. The intent is to provide intuition as to why the different approaches sometimes give different answers and give researchers guidance for their use.
Labor Pains: Change in Organizational Models and Employee Turnover in Young, High-Tech Firms
- American Journal of Sociology
, 2001
"... Organizational theories, especially ecological perspectives, emphasize the disruptive effects of change. However, the mechanisms producing these effects are seldom examined explicitly. This article examines one such mechanism—employee turnover. Analyzing a sample of high-technology start-ups, we sho ..."
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Cited by 48 (1 self)
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Organizational theories, especially ecological perspectives, emphasize the disruptive effects of change. However, the mechanisms producing these effects are seldom examined explicitly. This article examines one such mechanism—employee turnover. Analyzing a sample of high-technology start-ups, we show that changes in the employment models or blueprints embraced by organizational leaders increase turnover, which in turn adversely affects subsequent organizational performance. Turnover associated with organizational change appears to be concentrated among the most senior employees, suggesting “old guard disenchantment ” as the primary cause. The results are consistent with the claim of neoinstitutionalist scholars that founders impose cultural blueprints on nascent organizations and with the claim of organizational ecologists that altering such blueprints is disruptive and destabilizing.
Estimation and Prediction of Forest Growth Variables Based on Multilevel Nonlinear Mixed Models. Department of Statistics
, 2000
"... ABSTRACT. In this article, we describe estimation and prediction methods for nonlinear modeling of forest growth variables that are subject to nested sources of variability. The multilevel nonlinear mixedeffects models that we consider are useful for a variety of forestry applications, but we conce ..."
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Cited by 20 (1 self)
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ABSTRACT. In this article, we describe estimation and prediction methods for nonlinear modeling of forest growth variables that are subject to nested sources of variability. The multilevel nonlinear mixedeffects models that we consider are useful for a variety of forestry applications, but we concentrate on the problem of estimating, and making projections from, growth curves for tree height based on longitudinal data grouped by location. Wolfinger and Lin consider estimating equation approaches to fitting more general nonlinear mixed-effects models, and we adapt their zero-expansion estimating equations to the multilevel case. We develop methods of prediction based on these models that allow predictions of future height both for individual trees and for plot averages. We illustrate these methods by fitting and making predictions from a Chapman-Richards type growth model for tree height data from a loblolly pine spacing study in Putnam County, Georgia. The mean and variance of prediction errors based on our methods are examined by means of cross-validation. We provide a more complete and unified presentation of linearization-based estimation and prediction based on multilevel nonlinear mixed-effects models than has previously appeared in the forestry literature, and we argue that these models lead to substantial advantages in growth and yield prediction over traditional forestry methods. OR MANY PURPOSES IN FORESTRY, it is useful to be able to make accurate future predictions of the mean values of growth variables based on repeated measurements through time made on units that are grouped hierarchically. For example, many forest management decisions are based on yield projections that crucially depend on projections of plot level averages of tree height, basal area, and other morphometric variables. In the forestry literature, Lappi and Bailey (1988) have described the use of one example of a nonlinear mixed effects growth curve model to predict dominant tree height both at the plot level and at the individual tree level. In their model, random effects for plots, and for trees nested within plots enter into a ChapmanRichards-type growth model in a linear manner. Although not originally described in these terms, their model is an example of a multi-level nonlinear mixed effects model (ML-NLMM), because random effects are included in the model corresponding to units of heterogeneity (plots and trees) that have a hierarchical structure with multiple levels. Here we consider a more general version of Lappi and Bailey's model in which multilevel random effects are allowed to enter into the model nonlinearly. We describe methods of fitting such models based on first-order Taylor series linearization techniques that have been developed recently in the rapidly expanding statistical literature on nonlinear mixed-effects models (NLMMs), adapting these methods to the special challenges of the multilevel case. We also develop prediction methods based on these models that allow predictions to be made locally at each level in the model (e.g., to particular trees or plots of interest), as well as globally across the entire population represented in the data. When measurements are available on the units of interest (perhaps from ages prior to the target age), these predictors make use of this relevant information and thus can be ex- Forest Science 47(3) 2001 pected to outperform some other methods of prediction that have appeared in the literature on NLMMs applied to forestry problems (e.g., Schabenberger 1996a, 1996b). The methods we describe apply quite generally to nonlinear modeling of responses that are subject to nested sources of variability. However, we will concentrate on the problem of estimating, and making projections from, growth curves for tree height based on longitudinal data (repeated measures through time) that are grouped by location (typically by plots, but possibly by plots within stands, etc.). In the Example section below, we illustrate these methods by fitting Chapman-Richards growth curves to tree height data from an old-field loblolly pine (Pinus taeda L.) spacing study conducted in Putnam County, Georgia, by the D.B. Warnell School of Forest Resources, University of Georgia (Pienaar and Shiver 1993). Our goals are twofold: (1) to deliver the state-of-the-art in nonlinear mixed-effects modeling methodology to a forest biometrics audience in the context of an important forestry application of these models; and (2) to illustrate the advantages of this approach over traditional methods for growth and yield modeling based on site-index curves.
S (2005) Household-based Ceramic Water Filters for the Prevention of Diarrhea: A Randomized, Controlled Trial of a Pilot Program in Colombia
- American Journal of Tropical Medicine and Hygiene
"... Abstract. Household water treatment is increasingly recognized as an effective means of reducing the burden of diarrheal disease among low-income populations without access to safe water. Oxfam GB undertook a pilot project to explore the use of household-based ceramic water filters in three remote c ..."
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Cited by 16 (4 self)
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Abstract. Household water treatment is increasingly recognized as an effective means of reducing the burden of diarrheal disease among low-income populations without access to safe water. Oxfam GB undertook a pilot project to explore the use of household-based ceramic water filters in three remote communities in Colombia. In a randomized, controlled trial over a period of six months, the filters were associated with a 75.3 % reduction in arithmetic mean thermotolerant coliforms (TTCs) (P < 0.0001). A total of 47.7 % and 24.2 % of the samples from the intervention group had no detectible TTCs/100 mL or conformed to World Health Organization limits for low risk (1–10 TTCs/100 mL), respectively, compared with 0.9 % and 7.3 % for control group samples. Overall, prevalence of diarrhea was 60 % less among households using filters than among control households (odds ratio � 0.40, 95 % confidence interval � 0.25, 0.63, P < 0.0001). However, the microbiologic performance and protective effect of the filters was not uniform throughout the study communities, suggesting the need to consider the circumstances of the particular setting before implementing this intervention.
ENCOURAGING BEST PRACTICE IN QUANTITATIVE MANAGEMENT RESEARCH: AN INCOMPLETE LIST OF OPPORTUNITIES
"... The paper identifies some common problems encountered in quantitative methodology and provides information on current best practice to resolve these problems. We first discuss issues pertaining to variable measurement and concerns regarding the underlying relationships among variables. We then highl ..."
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Cited by 11 (0 self)
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The paper identifies some common problems encountered in quantitative methodology and provides information on current best practice to resolve these problems. We first discuss issues pertaining to variable measurement and concerns regarding the underlying relationships among variables. We then highlight several advances in estimation methodology that may circumvent issues encountered in common practice. Finally, we discuss approaches that move beyond existing research designs, including the development and use of datasets that embody linkages across levels of analysis, or combine qualitative and quantitative methods.
The Wilcoxon Signed Rank Test for paired comparisons of clustered data, Biometrics 62
, 2006
"... Summary. The Wilcoxon signed rank test is a frequently used nonparametric test for paired data (e.g., consisting of pre- and posttreatment measurements) based on independent units of analysis. This test cannot be used for paired comparisons arising from clustered data (e.g., if paired comparisons ar ..."
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Cited by 9 (0 self)
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Summary. The Wilcoxon signed rank test is a frequently used nonparametric test for paired data (e.g., consisting of pre- and posttreatment measurements) based on independent units of analysis. This test cannot be used for paired comparisons arising from clustered data (e.g., if paired comparisons are available for each of two eyes of an individual). To incorporate clustering, a generalization of the randomization test formulation for the signed rank test is proposed, where the unit of randomization is at the cluster level (e.g., person), while the individual paired units of analysis are at the subunit within cluster level (e.g., eye within person). An adjusted variance estimate of the signed rank test statistic is then derived, which can be used for either balanced (same number of subunits per cluster) or unbalanced (different number of subunits per cluster) data, with an exchangeable correlation structure, with or without tied values. The resulting test statistic is shown to be asymptotically normal as the number of clusters becomes large, if the cluster size is bounded. Simulation studies are performed based on simulating correlated ranked data from a signed log-normal distribution. These studies indicate appropriate type I error for data sets with ≥20 clusters and a superior power profile compared with either the ordinary signed rank test based on the average cluster difference score or the multivariate signed rank test of Puri and Sen (1971, Nonparametric Methods in Multivariate Analysis, New York: John Wiley). Finally, the methods are illustrated with two data sets, (i)
The impact of heat waves on mortality in 9 European cities: Results from the EuroHEAT project. Environ
- Health 2010
"... Background: The present study aimed at developing a standardized heat wave definition to estimate and compare the impact on mortality by gender, age and death causes in Europe during summers 1990-2004 and 2003, separately, accounting for heat wave duration and intensity. Methods: Heat waves were def ..."
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Background: The present study aimed at developing a standardized heat wave definition to estimate and compare the impact on mortality by gender, age and death causes in Europe during summers 1990-2004 and 2003, separately, accounting for heat wave duration and intensity. Methods: Heat waves were defined considering both maximum apparent temperature and minimum temperature and classified by intensity, duration and timing during summer. The effect was estimated as percent increase in daily mortality during heat wave days compared to non heat wave days in people over 65 years. City specific and pooled estimates by gender, age and cause of death were calculated. Results: The effect of heat waves showed great geographical heterogeneity among cities. Considering all years, except 2003, the increase in mortality during heat wave days ranged from + 7.6 % in Munich to + 33.6 % in Milan. The increase was up to 3-times greater during episodes of long duration and high intensity. Pooled results showed a greater impact in Mediterranean (+ 21.8 % for total mortality) than in North Continental (+ 12.4%) cities. The highest effect was observed for respiratory diseases and among women aged 75-84 years. In 2003 the highest impact was observed in cities where heat wave episode was characterized by unusual meteorological conditions. Conclusions: Climate change scenarios indicate that extreme events are expected to increase in the future even in regions where heat waves are not frequent. Considering our results prevention programs should specifically target the elderly, women and those suffering from chronic respiratory disorders, thus reducing the impact on mortality.
Multivariate Dispersion Models
, 2005
"... : We introduce a class of multivariate dispersion models suitable as error distributions for generalized linear models with multivariate non-normal responses. The models preserve some of the main properties of the multivariate normal distribution, and include the elliptically contoured distributions ..."
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Cited by 2 (0 self)
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: We introduce a class of multivariate dispersion models suitable as error distributions for generalized linear models with multivariate non-normal responses. The models preserve some of the main properties of the multivariate normal distribution, and include the elliptically contoured distributions and certain other known distributions as special cases. We give explicit methods for constructing multivariate proper dispersion models. This is exemplified by constructing multivariate gamma, Laplace, hyperbola and von Mises distributions. KEYWORDS: Elliptically contoured distribution; Multivariate Gamma Distribution; Multivariate Proper Dispersion Model; Generalized Yoke 1 Introduction The multivariate normal distribution occupies a central position in multivariate analysis of continuous data, and much multivariate distribution theory is directed towards creating alternatives to the multivariate normal for skewed or otherwise non-normal data, while preserving some of its main properties....
Identifying the Precipitants of Homeless Protest Across 17
- Social Forces
, 2005
"... During the 1980s, homeless people formed social movement organizations and mobilized collective action events in cities across the U.S. From the vantage point of social movement theories and scholarship on homelessness, it is surprising that homeless protest was so prevalent in the 1980s. Yet we fin ..."
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During the 1980s, homeless people formed social movement organizations and mobilized collective action events in cities across the U.S. From the vantage point of social movement theories and scholarship on homelessness, it is surprising that homeless protest was so prevalent in the 1980s. Yet we find evidence of homeless protest events across no fewer than 50 U.S. cities in the 1980-90 period. Drawing on social movement theories about the precipitants of mobilization, we examine the extent to which city-level contextual factors, and their change over time, affect variation in the frequency of homeless mobilization across 17 of these cities. Our findings reveal that a mix offactors congruent with strain and resource mobilization theories helps to account for variation in the frequency of homeless protest across U.S. cities in the 1980s. During the 1980s, many u.s. cities witnessed the rise of insurgency by homeless people who engaged in protest rallies, marches, housing takeovers, and encampments on government property to express their collectivegrievances about their dire situation. While many readers will recall national events-i-such as the "The research wassupported in part by a grantfrom theNationalScience Foundation (SES 9008809, David Snow, principalinvestigator). Thefirst draft of the article wasinitiatedwhile thefirst author was a fellow at the Centerfor AdvancedStudy in the Behavioral Sciences. We thank the centerfor the financial supportprovided by its general funds. We also thank Kelly Smith for her assistance in the initial coding of the data; Martha Burt of the Urban Institute for access to the institute's 1987nationaldata set on homelessness; David Meyer, Susan Olzak,and Su Yang for their incisive comments; and especially Susan G. Baker, who helpedassemble a portion of the data employed in this article and providedfeedback on its earlier drafts. Directcorrespondence to David A. Snow,
Aggressive Cholesterol Lowering Delays Saphenous Vein Graft Atherosclerosis in Women, the Elderly, and Patients With Associated Risk Factors NHLBI Post Coronary Artery Bypass Graft Clinical Trial
"... moderate lowering of low-density lipoprotein-cholesterol (LDL-C) decreased obstructive changes in saphenous vein grafts (SVGs) by 31%.1 Using lovastatin and cholestyramine when necessary, the annually determined mean LDL-C level ranged from 93 to 97 mg/dL in aggressively treated patients and from 13 ..."
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moderate lowering of low-density lipoprotein-cholesterol (LDL-C) decreased obstructive changes in saphenous vein grafts (SVGs) by 31%.1 Using lovastatin and cholestyramine when necessary, the annually determined mean LDL-C level ranged from 93 to 97 mg/dL in aggressively treated patients and from 132 to 136 mg/dL in the others (P,0.001). Methods and Results—The present study evaluated the treatment effect in subgroups defined by age, gender, and selected coronary heart disease (CHD) risk factors, ie, smoking, hypertension, diabetes mellitus, high-density lipoprotein cholesterol (HDL-C),35 mg/dL, and triglyceride serum levels $200 mg/dL at baseline. As evidenced by similar odds ratio estimates of progression (lumen diameter decrease $0.6 mm) and lack of interactions with treatment, a similar beneficial effect of aggressive lowering was observed in elderly and young patients, in women and men, in patients with and without smoking, hypertension, or diabetes mellitus, and those with and without borderline high-risk triglyceride serum levels. The change in minimum lumen diameter was in the same direction for all subgroup categories, without significant interactions with treatment. Conclusions—Aggressive LDL-C lowering delays progression of atherosclerosis in SVGs irrespective of gender, age, and certain risk factors for CHD. (Circulation. 1999;99:3241-3247.) Key Words: bypass n grafting n atherosclerosis n risk factors The Post Coronary Artery Bypass Graft trial (Post CABG)showed that aggressive lowering of low-density