Inference in Linear Regression Models with Many Covariates and Heteroskedasticity

The linear regression model is widely used in empirical work in Economics, Statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroskedasticity. Our results are obtained using high-dimensional approximations, where the number of included covariates are allowed to grow as fast as the sample size. We find that all of the usual versions of Eicker-White heteroskedasticity consistent standard error estimators for linear models are inconsistent under this asymptotics. We then propose a new heteroskedasticity consistent standard error formula that is fully automatic and robust to both (conditional)\ heteroskedasticity of unknown form and the inclusion of possibly many covariates. We apply our findings to three settings: parametric linear models with many covariates, linear panel models with many fixed effects, and semiparametric semi-linear models with many technical regressors. Simulation evidence consistent with our theoretical results is also provided. The proposed methods are also illustrated with an empirical application

Binscatter Regressions

We introduce the \texttt{Stata} (and \texttt{R}) package \textsf{Binsreg}, which implements the binscatter methods developed in \citet*{Cattaneo-Crump-Farrell-Feng_2019_Binscatter}. The package includes the commands \texttt{binsreg}, \texttt{binsregtest}, and \texttt{binsregselect}. The first command (\texttt{binsreg}) implements binscatter for the regression function and its derivatives, offering several point estimation, confidence intervals and confidence bands procedures, with particular focus on constructing binned scatter plots. The second command (\texttt{binsregtest}) implements hypothesis testing procedures for parametric specification and for nonparametric shape restrictions of the unknown regression function. Finally, the third command (\texttt{binsregselect}) implements data-driven number of bins selectors for binscatter implementation using either quantile-spaced or evenly-spaced binning/partitioning. All the commands allow for covariate adjustment, smoothness restrictions, weighting and clustering, among other features. A companion \texttt{R} package with the same capabilities is also available

On Binscatter

Binscatter is very popular in applied microeconomics. It provides a flexible, yet parsimonious way of visualizing and summarizing large data sets in regression settings, and it is often used for informal evaluation of substantive hypotheses such as linearity or monotonicity of the regression function. This paper presents a foundational, thorough analysis of binscatter: we give an array of theoretical and practical results that aid both in understanding current practices (i.e., their validity or lack thereof) and in offering theory-based guidance for future applications. Our main results include principled number of bins selection, confidence intervals and bands, hypothesis tests for parametric and shape restrictions of the regression function, and several other new methods, applicable to canonical binscatter as well as higher-order polynomial, covariate-adjusted and smoothness-restricted extensions thereof. In particular, we highlight important methodological problems related to covariate adjustment methods used in current practice. We also discuss extensions to clustered data. Our results are illustrated with simulated and real data throughout. Companion general-purpose software packages for \texttt{Stata} and \texttt{R} are provided. Finally, from a technical perspective, new theoretical results for partitioning-based series estimation are obtained that may be of independent interest

Targeted population surveys on drug use in recreational settings across Europe

In this chapter, we focus on in situ targeted population surveys (TPS) of drug use in recreational settings across Europe. Such surveys feed into European Union (EU) drug monitoring systems aimed at drug prevention and harm reduction. Specifically, we explore how TPS shape knowledge production about drug use. To do this, we situate TPS research within contemporary drug use trends, such as the emergence of new psychoactive substances (NPS) and darknet markets for pharmaceutical medications. We also use critical drug studies and sociological work on leisure spaces and times to explore how ‘recreational settings’ are understood within this research literature. From there, we argue that specific drugs, drug-using populations, and recreational settings dominate investigations, while others are largely ignored. To counter this, we suggest a critical, reflexive approach to processes of definition and conceptualisation by in situ TPS, including recreational setting inclusion/exclusion processes. Keywords: recreational settings, in situ targeted population surveys, leisure spaces/times, knowledge production, European drug research

Topologies related to (I)-envelopes

We investigate the question whether the (I)-envelope of any subset of a dual to a Banach space $X$ may be described as the closed convex hull in a suitable topology. If $X$ contains no copy of $\ell^1$ then the weak topology generated by functionals of the first Baire class in the weak$^*$ topology works. On the other hand, if $X$ contains a complemented copy of $\ell^1$ or $X=C([0,1])$ no locally convex topology works. If we do not require the topology to be locally convex, the problem is still open. We further introduce and compare several natural intermediate closure operators on a dual Banach space. Finally, we collect several intringuing open problems related to (I)-envelopes.Comment: 23 page

Identification of transcriptional and metabolic programs related to mammalian cell size

SummaryBackgroundRegulation of cell size requires coordination of growth and proliferation. Conditional loss of cyclin-dependent kinase 1 in mice permits hepatocyte growth without cell division, allowing us to study cell size in vivo using transcriptomics and metabolomics.ResultsLarger cells displayed increased expression of cytoskeletal genes but unexpectedly repressed expression of many genes involved in mitochondrial functions. This effect appears to be cell autonomous because cultured Drosophila cells induced to increase cell size displayed a similar gene-expression pattern. Larger hepatocytes also displayed a reduction in the expression of lipogenic transcription factors, especially sterol-regulatory element binding proteins. Inhibition of mitochondrial functions and lipid biosynthesis, which is dependent on mitochondrial metabolism, increased the cell size with reciprocal effects on cell proliferation in several cell lines.ConclusionsWe uncover that large cell-size increase is accompanied by downregulation of mitochondrial gene expression, similar to that observed in diabetic individuals. Mitochondrial metabolism and lipid synthesis are used to couple cell size and cell proliferation. This regulatory mechanism may provide a possible mechanism for sensing metazoan cell size

Phase-lags in large scale brain synchronization : Methodological considerations and in-silico analysis

Architecture of phase relationships among neural oscillations is central for their functional significance but has remained theoretically poorly understood. We use phenomenological model of delay-coupled oscillators with increasing degree of topological complexity to identify underlying principles by which the spatio-temporal structure of the brain governs the phase lags between oscillatory activity at distant regions. Phase relations and their regions of stability are derived and numerically confirmed for two oscillators and for networks with randomly distributed or clustered bimodal delays, as a first approximation for the brain structural connectivity. Besides in-phase, clustered delays can induce anti-phase synchronization for certain frequencies, while the sign of the lags is determined by the natural frequencies and by the inhomogeneous network interactions. For in-phase synchronization faster oscillators always phase lead, while stronger connected nodes lag behind the weaker during frequency depression, which consistently arises for in-silico results. If nodes are in antiphase regime, then a distance Pi is added to the in-phase trends. The statistics of the phases is calculated from the phase locking values (PLV), as in many empirical studies, and we scrutinize the method's impact. The choice of surrogates do not affects the mean of the observed phase lags, but higher significance levels that are generated by some surrogates, cause decreased variance and might fail to detect the generally weaker coherence of the interhemispheric links. These links are also affected by the non-stationary and intermittent synchronization, which causes multimodal phase lags that can be misleading if averaged. Taken together, the results describe quantitatively the impact of the spatio-temporal connectivity of the brain to the synchronization patterns between brain regions, and to uncover mechanisms through which the spatio-temporal structure of the brain renders phases to be distributed around 0 and Pi.Peer reviewe

Optical energies of AllnN epilayers

Optical energy gaps are measured for high-quality Al1−xInxN-on-GaN epilayers with a range of compositions around the lattice match point using photoluminescence and photoluminescence excitation spectroscopy. These data are combined with structural data to determine the compositional dependence of emission and absorption energies. The trend indicates a very large bowing parameter of 6 eV and differences with earlier reports are discussed. Very large Stokes' shifts of 0.4-0.8 eV are observed in the composition range 0.13<x<0.24, increasing approximately linearly with InN fraction despite the change of sign of the piezoelectric fiel