Econometrics ≈ EconometricsEconometrics
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Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator Open
Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Men…
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Power-law distributions in empirical data Open
Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the empirical detection and characterization of power laws is…
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mice: Multivariate Imputation by Chained Equations in R Open
The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation an…
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W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis Open
This article presents W-IQ-TREE, an intuitive and user-friendly web interface and server for IQ-TREE, an efficient phylogenetic software for maximum likelihood analysis. W-IQ-TREE supports multiple sequence types (DNA, protein, codon, bina…
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Sample sizes for saturation in qualitative research: A systematic review of empirical tests Open
To review empirical studies that assess saturation in qualitative research in order to identify sample sizes for saturation, strategies used to assess saturation, and guidance we can draw from these studies.We conducted a systematic review…
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Unobservable Selection and Coefficient Stability: Theory and Evidence Open
A common approach to evaluating robustness to omitted variable bias is to observe coefficient movements after inclusion of controls. This is informative only if selection on observables is informative about selection on unobservables. Alth…
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Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects Open
Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they estimate weighted sums of the average treatment effects (ATE ) in each group and period, with weights that may be negat…
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Planck 2018 results. VI. Cosmological parameters Open
We present cosmological parameter results from the final full-mission Planck\nmeasurements of the CMB anisotropies. We find good consistency with the\nstandard spatially-flat 6-parameter $\\Lambda$CDM cosmology having a power-law\nspectrum…
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Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption Open
The MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in sensitivity analyses.
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Bootstrap your own latent: A new approach to self-supervised Learning Open
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from each other. From an…
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Evaluating Effect Size in Psychological Research: Sense and Nonsense Open
Effect sizes are underappreciated and often misinterpreted—the most common mistakes being to describe them in ways that are uninformative (e.g., using arbitrary standards) or misleading (e.g., squaring effect-size rs). We propose that effe…
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Interrupted time series regression for the evaluation of public health interventions: a tutorial Open
Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to eva…
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Opening the black box: an open‐source release of Maxent Open
This software note announces a new open‐source release of the Maxent software for modeling species distributions from occurrence records and environmental data, and describes a new R package for fitting such models. The new release (ver. 3…
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How much should we trust staggered difference-in-differences estimates? Open
We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in fina…
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WaveNet: A Generative Model for Raw Audio Open
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonethel…
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Testing measurement invariance of composites using partial least squares Open
Purpose – Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of the…
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A tutorial on regularized partial correlation networks. Open
Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused …
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Introduction to the Analysis of Survival Data in the Presence of Competing Risks Open
Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular c…
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Descriptive statistics and normality tests for statistical data Open
Descriptive statistics are an important part of biomedical research which is used to describe the basic features of the data in the study. They provide simple summaries about the sample and the measures. Measures of the central tendency an…
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Mendelian Randomization as an Approach to Assess Causality Using Observational Data Open
Mendelian randomization refers to an analytic approach to assess the causality of an observed association between a modifiable exposure or risk factor and a clinically relevant outcome. It presents a valuable tool, especially when randomiz…
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Technological Innovation, Resource Allocation, and Growth* Open
We propose a new measure of the economic importance of each innovation. Our measure uses newly collected data on patents issued to U.S. firms in the 1926 to 2010 period, combined with the stock market response to news about patents. Our pa…
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Robust causal inference using directed acyclic graphs: the R package ‘dagitty’ Open
Directed acyclic graphs (DAGs), which offer systematic representations of causal relationships, have become an established framework for the analysis of causal inference in epidemiology, often being used to determine covariate adjustment s…
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Aggregate Confusion: The Divergence of ESG Ratings Open
This paper investigates the divergence of environmental, social, and governance (ESG) ratings based on data from six prominent ESG rating agencies: Kinder, Lydenberg, and Domini (KLD), Sustainalytics, Moody’s ESG (Vigeo-Eiris), S&P Global …
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Multicollinearity and misleading statistical results Open
Multicollinearity represents a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to incorrect results of regression analyses. Diagnostic tools of multicollinearity include the var…
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Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants Open
Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations…
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Neural Discrete Representation Learning. Open
Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Va…
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A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization Open
Mendelian randomization (MR) uses genetic data to probe questions of causality in epidemiological research, by invoking the Instrumental Variable (IV) assumptions. In recent years, it has become commonplace to attempt MR analyses by synthe…
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CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting Open
This study compares the two widely used methods of Structural Equation Modeling (SEM): Covariance based Structural Equation Modeling (CB-SEM) and Partial Least Squares based Structural Equation Modeling (PLS-SEM). The first approach is bas…
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Neural Discrete Representation Learning Open
Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Va…
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Empirical Asset Pricing via Machine Learning Open
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some c…