1,721,060 research outputs found
Kernel-based time-varying IV estimation: handle with care
Full text linkGiraitis et al. (J Econom 224(2):394-415, 2021) proposed a kernel-based time-varying coefficients IV estimator. By using entirely different code, we broadly replicate the simulation results and the empirical application on the Phillips curve, but we note that a possible oversight might have affected some of the reported results. Further, we extend the results by using a different sample and a wider choice of smoothing kernels, including data-based ones; we find that the estimator is remarkably robust across a wide range of smoothing choices, but the effect of outliers may be less obvious than expected
Optimal chemotherapy counteracts cancer adaptive resistance in a cell-based, spatially-extended, evolutionary model
No full textMost aggressive cancers are incurable due to their fast evolution of drug resistance. We model cancer growth and adaptive response in a simplified cell-based (CB) setting, assuming a genetic resistance to two chemotherapeutic drugs. We show that optimal administration protocols can steer cells resistance and turned it into a weakness for the disease. Our work extends the population-based model proposed by Orlando et al (2012 Phys. Biol.), in which a homogeneous population of cancer cells evolves according to a fitness landscape. The landscape models three types of trade-offs, differing on whether the cells are more, less, or equal effective when generalizing resistance to two drugs as opposed to specializing to a single one. The CB framework allows us to include genetic heterogeneity, spatial competition, and drugs diffusion, as well as realistic administration protocols. By calibrating our model on Orlando et al's assumptions, we show that dynamical protocols that alternate the two drugs minimize the cancer size at the end of (or at mid-points during) treatment. These results significantly differ from those obtained with the homogeneous model - suggesting static protocols under the pro-generalizing and neutral allocation trade-offs - highlighting the important role of spatial and genetic heterogeneities. Our work is the first attempt to search for optimal treatments in a CB setting, a step forward toward realistic clinical applications
Can you do the wrong thing and still be right? Hypothesis Testing in I(2) and near-I(2) cointegrated VARs
No full textWe review the I(2) model with a focus on its application to near-I(2) data, i.e. I(1) data with a second root very close to unity, and report the results of some Monte Carlo experiments. We show that with I(2) data tests on the long-run coefficients in the I(2) model have small sample properties consistent with asymptotic results. More importantly, we show also that with near-I(2) data the properties of the tests are (i) similar to those found with genuine I(2) data, (ii) systematically superior to those of the analogous tests constructed in the I(1) model, even if the latter is in principle correctly specified and the former is not. Our results thus provide strong support to the suggestion to model near-I(2) data using the I(2) model
Artificial regression testing in the GARCH-in-mean model
Full text linkThe issue of finite-sample inference in Generalised Autoregressive Conditional Heteroskedasticity (GARCH)-like models has seldom been explored in the theoretical literature, although its potential relevance for practitioners is obvious. In some cases, asymptotic theory may provide a very poor approximation to the actual distribution of the estimators in finite samples. The aim of this paper is to propose the application of the so-called double length regressions (DLR) to GARCH-in-mean models for inferential purposes. As an example, we focus on the issue of Lagrange Multiplier tests on the risk premium parameter. Simulation evidence suggests that DLR-based Lagrange Multiplier (LM) test statistics provide a much better testing framework than the more commonly used LM tests based on the outer product of gradients (OPG) in terms of actual test size, especially when the GARCH process exhibits high persistence in volatility. This result is consistent with previous studies on the subject. Copyright 2005 Royal Economic Society
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
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