A megajournal is an open‐access journal that publishes any manuscript that presents scientifically trustworthy empirical results, without asking about the potential scientific contribution prior to publication. Megajournals have rapidly increased their output and are currently publishing around 50,000 articles per year. We report on a small pilot study in which we looked at the citation distributions for articles in megajournals compared with journals with traditional peer review, which also evaluate articles for contribution and novelty. We found that elite journals with very low acceptance rates have far fewer articles with no or few citations, but that the long tail of articles with two citations or less was actually bigger in a sample of selective traditional journals in comparison with megajournals. This indicates the need for more systematic studies, because the results raise many questions as to how efficiently the current peer review system in reality fulfils its filtering function.
A Lagrange multiplier test for testing the parametric structure of a constant conditional correlation generalized autoregressive conditional heteroskedasticity (CCC-GARCH) model is proposed. The test is based on decomposing the CCC-GARCH model multiplicatively into two components, one of which represents the null model, whereas the other one describes the misspeci…cation. A simulation study shows that the test has good …nite sample properties. We compare the test with other tests for misspeci…cation of multivariate GARCH models. The test has high power against alternatives where the misspeci…cation is in the GARCH parameters and is superior to other tests. The test is not greatly affected by misspeci…cation in the conditional correlations and is therefore well suited for considering misspeci…cation of GARCH equations.JEL Codes: C32, C52, C58
Tests for error autocorrelation (AC) are derived under the assumption of independent and identically distributed errors. The tests are not asymptotically valid if the errors are conditionally heteroskedastic. In this article we propose wild bootstrap (WB) Lagrange multiplier tests for error AC in vector autoregressive (VAR) models. We show that the WB tests are asymptotically valid under conditional heteroskedasticity of unknown form. WB tests based on a version of the heteroskedasticityconsistent covariance matrix estimator are found to have the smallest error in rejection probability under the null and high power under the alternative. We apply the tests to VAR models for credit default swap prices and Euribor interest rates. An important result that we find is that the WB tests lead to parsimonious models while the asymptotic tests suggest that a long lag length is required to get white noise residuals.
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