1,721,046 research outputs found
Weighted-average least squares: Beyond the classical linear regression model
No full textThis paper introduces four new commands for the weighted-average least squares approach to model uncertainty: the hetwals command fits linear models with multiplicative forms of heteroskedasticity; the ar1wals command fits linear models with stationary AR(1) errors; the xtwals command fits fixed-effects and random-effects panel-data models with either i.i.d. or AR(1) idiosyncratic errors; while the glmwals command fits univariate generalized linear models. These commands extend the new functionalities of the wals command (version 3.0) introduced by De Luca and Magnus (2025a), and enlarge the classes of models that can be fitted by this model-averaging method. We also provide an illustration of the hetwals and glmwals commands by means of real data applications
Weighted-average least squares: Improvements and extensions
No full textThis paper presents version 3.0 of the wals command, which implements the weighted-average least squares estimator of Magnus et al. (2010, Journal of Econometrics 154, 139–153). Version 3.0 improves earlier versions of wals in several respects: a new syntax supporting factor variables, time-series operators, and weights; an enlarged set of prior distributions; extended quadrature methods for computing the posterior mean; new plug-in estimates of the sampling moments; simulation-based confidence intervals; and other options to control accuracy, computational speed, and output of wals. We also offer three new post-estimation commands: the predict command associated with wals; the lcwals command for estimating linear combinations of the parameters; and the margwals command for estimating smooth, possibly nonlinear, functions of the parameters at given values of regressors. Finally, we compare our new commands with two suites of Stata commands for tackling issues of model uncertainty
Weighted-Average Least Squares Estimation of Panel Data Models
No full textThis paper extends the weighted-average least squares (WALS) model averaging estimator to fixed-effects and random-effects panel data models with strictly exogenous regressors. We consider both the case where the errors are independent and identically distributed, and the case with first-order autocorrelation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Weak versus strong dominance of shrinkage estimators
Full text linkWe consider the estimation of the mean of a multivariate normal distribution with known variance. Most studies consider the risk of competing estimators, that is the trace of the mean squared error matrix. In contrast we consider the whole mean squared error matrix, in particular its eigenvalues. We prove that there are only two distinct eigenvalues and apply our findings to the James-Stein and the Thompson class of estimators. It turns out that the famous Stein paradox is no longer a paradox when we consider the whole mean squared error matrix rather than only its trace
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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