156,665 research outputs found

    Upper Missouri Waterkeeper v. EPA

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    State water quality standards developed under the Clean Water Act play a key role in curtailing the negative environmental, economic, and human health impacts of water pollution. Under the state water quality regulatory framework, EPA may grant variances to state standards should the state demonstrate the compliance with its standards is infeasible for a certain pollutant discharger or waterbody. Montana DEQ developed a variance for nutrients based on evidence that compliance with those standards would cause economic harm. EPA approved Montana\u27s nutrient pollutant variance, and Upper Missouri Waterkeeper challenged EPA\u27s approval on the grounds that the variance violates the Clean Water Act. The Ninth Circuit held that (1) EPA may consider the cost of implementing pollution control technology to attain compliance with state standards when approving variance requests, and (2) EPA properly interpreted its regulations as requiring compliance with the variance standard only at the end of the variance term. This note will explore how the decision may incentivize states to engage in a water quality race-to-the-bottom, sacrificing improvements in the name of cost, failing to protect the health of our nation\u27s waters, and further exposing low-income communities to degraded resources

    Large-scale linear regression: Development of high-performance routines

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    In statistics, series of ordinary least squares problems (OLS) are used to study the linear correlation among sets of variables of interest; in many studies, the number of such variables is at least in the millions, and the corresponding datasets occupy terabytes of disk space. As the availability of large-scale datasets increases regularly, so does the challenge in dealing with them. Indeed, traditional solvers---which rely on the use of black-box" routines optimized for one single OLS---are highly inefficient and fail to provide a viable solution for big-data analyses. As a case study, in this paper we consider a linear regression consisting of two-dimensional grids of related OLS problems that arise in the context of genome-wide association analyses, and give a careful walkthrough for the development of {\sc ols-grid}, a high-performance routine for shared-memory architectures; analogous steps are relevant for tailoring OLS solvers to other applications. In particular, we first illustrate the design of efficient algorithms that exploit the structure of the OLS problems and eliminate redundant computations; then, we show how to effectively deal with datasets that do not fit in main memory; finally, we discuss how to cast the computation in terms of efficient kernels and how to achieve scalability. Importantly, each design decision along the way is justified by simple performance models. {\sc ols-grid} enables the solution of 101110^{11} correlated OLS problems operating on terabytes of data in a matter of hours

    Coherence-based Partial Exact Recovery Condition for OMP/OLS

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    We address the exact recovery of the support of a k-sparse vector with Orthogonal Matching Pursuit (OMP) and Orthogonal Least Squares (OLS) in a noiseless setting. We consider the scenario where OMP/OLS have selected good atoms during the first l iterations (l<k) and derive a new sufficient and worst-case necessary condition for their success in k steps. Our result is based on the coherence \mu of the dictionary and relaxes Tropp's well-known condition \mu<1/(2k-1) to the case where OMP/OLS have a partial knowledge of the support

    Integrated Modified OLS Estimation and Fixed-b Inference for Cointegrating Regressions

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    This paper is concerned with parameter estimation and inference in a cointegrating regression, where as usual endogenous regressors as well as serially correlated errors are considered. We propose a simple, new estimation method based on an augmented partial sum (integration) transformation of the regression model. The new estimator is labeled Integrated Modified Ordinary Least Squares (IM-OLS). IM-OLS is similar in spirit to the fully modified approach of Phillips and Hansen (1990) with the key difference that IM-OLS does not require estimation of long run variance matrices and avoids the need to choose tuning parameters (kernels, bandwidths, lags). Inference does require that a long run variance be scaled out, and we propose traditional and fixed-b methods for obtaining critical values for test statistics. The properties of IM-OLS are analyzed using asymptotic theory and finite sample simulations. IM-OLS performs well relative to other approaches in the literature.Bandwidth, cointegration, fixed-b asymptotics, Fully Modified OLS, IM-OLS, kernel

    Increasing Returns to Scale in U.S. manufacturing industries: evidence from direct and reverse regression.

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    In this paper, I compare the OLS and IV estimators for the direct and reverse regression models in the context of estimating returns to scale and technical progress. It shows that the direct and reverse OLS estimators are inconsistent, that the direct OLS is always more precise than the reverse OLS under the normality assumption, and that the direct IV estimator and its reverse counterpart are consistent and asymptotically equivalent. Working with data from U.S. manufacturing industries over the last half-century, the estimation results show that in most industries increasing returns to scale are important and technical progress is small when it comes to explaining productivity growth.
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