GEL methods for non-smooth moment indicators

Pre-print version dated July 2008 issued as working paper by Institute for Fiscal Studies. Final version published by Cambridge University Press; available online at http://journals.cambridge.org/This paper considers the first-order large sample properties of the generalized empirical likelihood (GEL) class of estimators for models specified by nonsmooth indicators. The GEL class includes a number of estimators recently introduced as alternatives to the efficient generalized method of moments (GMM) estimator that may suffer from substantial biases in finite samples. These include empirical likelihood (EL), exponential tilting (ET), and the continuous updating estimator (CUE). This paper also establishes the validity of tests suggested in the smooth moment indicators case for overidentifying restrictions and specification. In particular, a number of these tests avoid the necessity of providing an estimator for the Jacobian matrix that may be problematic for the sample sizes typically encountered in practice

Predicting the replicability of social science lab experiments

We measure how accurately replication of experimental results can be predicted by black-box statistical models. With data from four large-scale replication projects in experimental psychology and economics, and techniques from machine learning, we train predictive models and study which variables drive predictable replication. The models predicts binary replication with a cross-validated accuracy rate of 70% (AUC of 0.77) and estimates of relative effect sizes with a Spearman rho of 0.38. The accuracy level is similar to market-aggregated beliefs of peer scientists [1, 2]. The predictive power is validated in a pre-registered out of sample test of the outcome of [3], where 71% (AUC of 0.73) of replications are predicted correctly and effect size correlations amount to rho = 0.25. Basic features such as the sample and effect sizes in original papers, and whether reported effects are single-variable main effects or two-variable interactions, are predictive of successful replication. The models presented in this paper are simple tools to produce cheap, prognostic replicability metrics. These models could be useful in institutionalizing the process of evaluation of new findings and guiding resources to those direct replications that are likely to be most informative

Non-Standard Errors

In statistics, samples are drawn from a population in a data generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.Online appendix available at https://bit.ly/3DIQKrB.Please note a full list of authors is available in the working paper

Distribution of Eigenvalues for the Modular Group

The two-point correlation function of energy levels for free motion on the modular domain, both with periodic and Dirichlet boundary conditions, are explicitly computed using a generalization of the Hardy-Littlewood method. It is shown that ion the limit of small separations they show an uncorrelated behaviour and agree with the Poisson distribution but they have prominent number-theoretical oscillations at larger scale. The results agree well with numerical simulations.Comment: 72 pages, Latex, the fiogures mentioned in the text are not vital, but can be obtained upon request from the first Autho

Macrophages and glia are the dominant P2X7-expressing cell types in the gut nervous system—No evidence for the role of neuronal P2X7 receptors in colitis

The blockade or deletion of the pro-inflammatory P2X7 receptor channel has been shown to reduce tissue damage and symptoms in models of inflammatory bowel disease, and P2X7 receptors on enteric neurons were suggested to mediate neuronal death and associated motility changes. Here, we used P2X7-specific antibodies and nanobodies, as well as a bacterial artificial chromosome transgenic P2X7-EGFP reporter mouse model and P2rx7$^{-/-}$ controls to perform a detailed analysis of cell type-specific P2X7 expression and possible overexpression effects in the enteric nervous system of the distal colon. In contrast to previous studies, we did not detect P2X7 in neurons but found dominant expression in glia and macrophages, which closely interact with the neurons. The overexpression of P2X7 per se did not induce significant pathological effects. Our data indicate that macrophages and/or glia account for P2X7-mediated neuronal damage in inflammatory bowel disease and provide a refined basis for the exploration of P2X7-based therapeutic strategies

Evaluating replicability of laboratory experiments in economics

The reproducibility of scientific findings has been called into question. To contribute data about reproducibility in economics, we replicate 18 studies published in the American Economic Review and the Quarterly Journal of Economics in 2011-2014. All replications follow predefined analysis plans publicly posted prior to the replications, and have a statistical power of at least 90% to detect the original effect size at the 5% significance level. We find a significant effect in the same direction as the original study for 11 replications (61%); on average the replicated effect size is 66% of the original. The reproducibility rate varies between 67% and 78% for four additional reproducibility indicators, including a prediction market measure of peer beliefs