dataesr/scientific-paragraphs-categorization huggingface dataset:
We assume that the growth rate of expected future dividends converges from its short-run towards its long-run value over a period of H=10 years. Because the cost of equity is by essence a forward looking measure and that we use market data measured at the end of each year, we define the contemporary cost of equity (at time t) as the cost of equity computed at the beginning of the period (i. e. at the end of period t-1). CoE s,t = ( D MV ) s,t-1 [(1 + g LT s,t-1 ) + H 2 (g ST s,t-1 -g LT s,t-1 )] + g LT s,t-1 (3) where ( D MV ) s,t-1 is the sector s weighted average dividend yield at the beginning of period t (time t -1). As I/B/E/S does not define sectors on the basis of the NACE, we match I/B/E/S and NACE rev. 2 classification. The firm-specific cost of debt is measured as the apparent cost of debt (interest paid over total financial debt) and computed from income statements and balance-sheet data.
9 Sector-level measures of competition and uncertainty. We also construct sector-level measures of competition and uncertainty. Regarding competition, our baseline indicator is the sector-level Herfindhal index of firms sales, which we compute using income statement data from the FIBEN database on French legal entities (in contrast to consolidated group data). We resort to this database for two reasons: first, it covers a much broader number of firms (some 250,000 a year, accounting for some 70% of the value added of French non-financial corporations) and second, the notion of sector is more accurate for legal entities than it is for groups which are more likely to combine production activities across different sectors. Regarding uncertainty, our baseline indicator is the dispersion of firms' ROA (net income over lagged total assets) within a given year and sector.
10 For this purpose, and considering the unbalanced nature of our panel, we first regress gross investment rates on year dummies and define firms with quasi-zero gross investment rates as firms with average residual values below the 10th percentile. Last, gross and net investment rates, profitability and the costs of equity and debt are trimmed at the 1st and 99th percentiles in order to mitigate the effect of outliers. Finally, we only keep observations for the years 2003-2015 due to the low number of clean observations in our database before this period and impose that a group appears for at least three consecutive years in the sample. C. Descriptive statistics. We end-up with a sample of 8,819 group-years observations and 1,847 unique groups, of which only 182 are listed. The average group is present in our sample 5.
Last, note that our estimation sample is still strongly unbalanced with a changing firm-size composition which may affect some aggregated statistics but not our estimations as far as we  10  Capital expenditures tend to be discontinuous for small firms as many small firms do not invest every year and as many zeros appear to be "'false"' missing values. Dealing properly with the truncated nature of the dependent variable would then call for a Tobit regression. However, as we consider corporate groups instead of individual firms, therefore relatively large SMEs and larger corporations, we hardly ever observe zero and quasi-zero gross investment rates in our dataset. We therefore prefer to clean up the data from these firms and keep continuously investing firms. If we do not proceed to this cleaning, we end up with a slightly larger sample and our results remain identical (tables upon request)  11  To sort corporate groups into legal size categories, we apply the legal sales and assets thresholds using consolidated balance sheets and income statements.
A. Baseline specification This section details our results. We first consider the whole sample of corporate groups in our cleaned dataset, which is mostly populated by non-listed firms. The results for our baseline regression are shown in column 2 of Table  2. Controlling for both firm and time fixed effects, as well as various relevant firm and sector characteristics, we find that the investment rate neg-atively correlates with the lagged WACC. Our main coefficient of interest, -0. 33, is similar to what Frank and Shen (2016) find for their sample of listed US corporations when they estimate the cost of equity using similar methods: between -0. 22 and -0. 57. 12 It is somewhat larger than what Melolinna et al. (2018) put forward for a smaller sample of UK listed firms (some -0. 15). As our additional regressions on firm sub-groupings by size suggest (see below), this discrepancy may however be accounted for by the large proportion of small, non-listed corporations in our sample.
To deal more explicitly with this issue, we re-estimate our baseline regression using a generalized-method-of-moments (GMM), two-stage least squares approach, where we instrument the (lagged) WACC with its 3rd and 4th lags and the (lagged) leverage with its 2nd and 3rd lags. 15 Implementing this method implies a sharp reduction in the sample size since more than six consecutive observations are required for each firms. Furthermore, since the coverage of the initial database increases with time, the remaining sample is concentrated on the post-2009 period. Table  4  presents the results of the GMM regressions. For comparison purpose, columns 1 and 2 show the results of OLS regressions when the sample is reduced to the data used for the GMM regressions. Due to the smaller size of the sample (almost three times smaller), the WACC coefficient is not significant anymore but still of the same magnitude as before.
On the other hand, parametric models fitted using the entire dataset are subject to undue influence of the central observations possibly compromising the fit in the tail regions. Such a fully parametric approach naturally gives rise to a significant model risk. An effective approach in situations dealing with extreme events is to rely on asymptotic models in the spirit of extreme value theory (EVT). The study of conditional extreme value models was initiated by  Heffernan and Tawn [2004] , followed by  Heffernan and Resnick [2007]  and  Das and Resnick [2011] , among others. Similar to  Abdous et al. [2005a] , we focus on the estimation of η(x, y) for x large but extend their methodology from the class of elliptical distributions to a more general framework of asymmetric models describing the stochastic behaviour of underlying tail risks.
For details, please refer to Section 2. 3. 1. In practice, above results can be applied to perform inference for block (e. g. , annual) maxima data using the maximum-likelihood method. However, this method can be wasteful of data due to the trade-off between the size of the blocks and the number of blocks to be constructed from a given dataset. In contrast, the peaks over threshold (POT) method, which employs the data of exceedances over a high threshold to estimate the tail of distribution, is a more efficient approach. The generalized Pareto distribution (GPD) is the pivotal distribution for modelling the data of exceedances over a high threshold, see  Embrechts et al. [1997] and McNeil et al. [2005] , among others. Definition 2. 1. 2. (Generalized Pareto Distribution). The distribution function of the GPD is given by H ξ,β (x) =    1 -(1 + ξ x β ) -1 ξ , ξ = 0 1 -exp{-x β }, ξ = 0 , where ξ is the shape parameter and β is an additional scale parameter.
The time period from June 1, 2006 to May 31, 2007 is defined as the precrisis window (same as in  Girardi and Tolga [2013] ), and the remaining one-year is the crisis window. The panel contains U. S. financial firms in the depositories industry with a market capitalization greater than 5 billion USD as of the end of June 2007. The Dow Jones US Financials Index (DJUSFN) is used as a proxy for the aggregate financial system. The daily prices and capitalization information are extracted from Yahoo Finance. Daily losses are calculated as negative log returns. Due to the presence of serial dependence and volatility clustering in daily financial returns, it is common to first filter the data using the AR(1)-GARCH(1,1) model. Then residuals can be treated as a sequence of independent and identically distributed random variables.
Other data collected Besides information on participants' workload of care, we obtained information on their selfreported adherence behaviors for ART using the questionnaire by Sidorkiewicz et al. [25]  This 5-item tool describes patients' adherence behaviors for each drug taken on a scale from 1 (absence of schedule errors and no omissions or drug holidays) to 6 (drug discontinuation). We considered that patients had good adherence behaviors for ART if they reported no drug omissions or drug holidays for any of their drugs; moderate adherence if they reported some drug omissions or short drug holidays for at least one of their drugs; and poor adherence if they reported long drug holidays (>6 days), systematically skipped a daily dose, or discontinued at least one of their drugs. Participants' self-reported data were also enriched by data extracted from their medical records (age, sex, date of diagnosis, last CD4 count, current antiretroviral treatment, etc.
Acknowledgments The authors thank  Annick Tchabert ,  Sorel Tomeba  and  Simon Pierre Bony  for their help in data collection;  Laura Smales (BioMedEditing)  for editing; and the  French National Agency for Research on HIV and viral hepatitis (Agence Nationale de Recherche sur le Sida et les Hepatites Virales-ANRS)  for logistic support.
Study design We conducted an observational cross-sectional study from 29 January to 15 June 2015. We sent questionnaires to General Practitioners (GPs) in the Champagne Ardenne region [see Additional file 1]. The questionnaire was developed specifically for this study. The Champagne-Ardenne region is one of the least densely populated regions in France, with a population of 1. 33 million. The age structure of the region is similar to the pooled age structure of the country. The physicians were identified with the French Order of Physicians database (accessed on 12 December 2014, available online). Addresses and telephone numbers were extracted from the yearbook.
-Prescriber's personal choice for other reasons (routine, desire to use drug names the patient is familiar with …). Physicians selected their answer in a list containing the following: never (0%); rarely (1-24%), sometimes (25-49%); often (50-74%); very often / always (75-100%). Prescriber-related variables that were collected included: -Age and sex of prescribers. Age was treated as a continuous variable. -Rate of INN prescriptions: lower or higher than 15% of total prescriptions (national average in 2013: 13. 2%) -Participation in a quality circle with reviews of clinical cases -The use of E-Prescribing system software (EPS) -Setting (city/ rural setting). Physicians were also asked to select in a multiplechoice question the most frequent reason for using the NGS mention for 18 distinct therapeutic classes (antiplatelet agents, thyroid hormones, antiepileptic drugs, antiacids, antispasmodics, step I analgesics, step II and III analgesics, NSAIDs / SAIDs, antibiotics, antidepressants, hypnotics, antipsychotics, lipid-lowering agents, oral anti-diabetic drugs, diuretics, Angiotensin Converting Enzyme inhibitors or Angiotensin II Receptor Blockers, beta blockers, oral contraceptives).
Globalisation indeed seems to link local territories, and especially cities  (Alderson and Beckfield, 2004; Beaverstock et al. , 1999; Derudder, 2006; Rozenblat and Pumain, 1993; Sassen, 1991; Taylor, 2004) , as transnational firms have preferential local fixing points and cities are the major receptors of economic activity  (Bairoch, 1985; Berger, 2005; Castells, 1998). In order to observe FDI in local places, FDI stocks and flows data are here disaggregated to be transposed in the urban system. Localized FDI stocks (LFDI stocks) give each foreign transnational firm precise locations at a given date (whenever the investment was made and whatever its mode), and Localized FDI flows (LFDI flows) register transnational investment decisions during a given period, without taking into account the already existing foreign investments.
g. with a Poisson or a negative binomial model). Formally fhurdle(y; x, z, βcount, βzero) = fzero(0; z, βzero) if y = 0 = (1 -fzero(0; z, βzero)) • fcount(y; x, βcount)/(1 -fcount(0; x, βcount)) if y ≠ 0 with z and βzero the parameters of the binomial logit model and x and βcount the parameters of the count model  (Zeileis et al. , 2008). In the results, we use this specification in its R implementation form in the 'pscl' package  (Jackman, 2008). We interpret mainly the parameters (estimated with maximum likelihood) relating to the scaling effects of the population on the probability of the attribute to be present (Hurdle component) and on its numeric positive value (Poisson component). We also run a classic Poisson model (without the Hurdle component), which fails to predict zeros, but as a benchmark for comparing the results of the Hurdle models.
LFDI stocks database relies on the detection of companies located in France directly controlled by a foreign one (min. 50% participation into their capital) and their cascading indirect subsidiaries (same criterion), thanks to ORBIS (http://orbis. bvdinfo. com) and LIFI databases (www. insee. fr/fr/information/2044918). The breakdown of these subsidiaries into establishments is operated thanks to the CLAP database (www. insee. fr/fr/information/2044773). More details in Finance, 2016.
(2) The study's sample size, or the degrees of freedom respectively, must be extractable in order to calculate the variation of the extracted effect-sizes and the study weights. (3) The study must also report country-specific results to allow an unambiguous assignment of regional variables to the specific regression coefficients. We started our four-step literature search procedure with (1) an extensive search using the following electronic databases: ABI/INFORM Complete, Business Source Premier, EconLit, and GreenFILE. Next, we explicitly searched for unpublished literature by screening the electronic working paper database SSRN using the same search strategy as for published articles. The resulting sample of relevant studies serves as a basis for (2) a backward search in the reference lists of the studies and (3) a forward search via the "cited by"-option in Google Scholar.
The extracted variables are either directly extracted from the primary studies or derived from external databases. The variables are summarized in   (Harris & Raviv, 1991). In general, capital structure can be divided into market versus book leverage, as well as total versus long-term debt (among others,  Cole, 2013; Feld et al. , 2013; Frank & Goyal, 2009). We include two dummy variables to capture differences among variable definitions in our analyses. The variable ML refers to differences in the measurement of corporate capital structure. As noted by  Feld et al. (2013) , primary studies usually examine market leverage (among others,  Bathala, Moon, & Rao, 1994; Mittoo & Zhang, 2008) , book leverage (among others,  Cole, 2013; Johnson, 1997) , or both (among others,  Berger, Ofek, & Yermack, 1997; Faulkender & Petersen, 2006)  as a proxy for capital structure.
In order to control for systematic regional differences (among others,  Jong et al. , 2008; Kayo & Kimura, 2011) , we add several variables from external databases. The variables are chosen on the basis of availability of the underlying comprehensive data set. First, we control for differences in economic development. Thus, we include the country-specific (logarithm of) GDP per capita (GDP)  (Frank & Goyal, 2009)  as well as the growth rate in GDP per capita (GDPGROW). The former represents the development of a certain country, while the latter relates to good growth opportunities for investors  (Kayo & Kimura, 2011). Following Carney, Gedajlovic, Heugens, van Essen, and van Oosterhout (2011), the variable US addresses the fact that a remarkable part of our sample is based on data from US firms.
Experimental design, materials and methods DYSIS II ACS was a multicenter, longitudinal, observational study that included 3867 patients from 18 countries in Europe, the Middle East, South-, Southeast-and East-Asia. The study was approved by the relevant ethics committees and carried out in agreement with local laws. Inclusion criteria were as follows: 1) provision of written informed consent, 2) aged Z18, 3) hospitalized for an ACS in 2013-2014, 4) availability of a full fasting or non fasting lipid profile based on blood drawn within 24 hours of admission, and 5) not participating in a clinical trial. Data were assessed at presentation for an acute coronary syndrome (ACS), at hospital stay and discharge, as well as after a predefined period of 120 days. The ESC/EAS dyslipidemia guidelines (2011) were used as a reference in order to determine target value attainment after 120 days  [1].
Low density lipoprotein (LDL-C) treatment target thus was o70 mg/dl. Since the guidelines also highlight the use of statins in highest tolerated dose, followed by the use of lipid lowering combination therapy, we determined statin dose administered, calculated as atorvastatin equivalent doses  [2]. Data were collected in an electronic case report form and processed in a central web-based database at the Institut für Herzinfarktforschung, Ludwigshafen, Germany. It was used for both collection and storage of the data.
The Gothenburg-based "Project 200" demonstrated that having developed a close relationship between the project team and users during the initial phase  (1992) (1993)  enabled to acquire a very detailed feedback  (Hammarlund, Iivari, Liljemark, 2000). In both territorial contexts, project leaders sought to monitor the public's satisfaction with hardware and software products provided in the framework of urban experiments. Technological functionalities and design features were evaluated by means of questionnaires and telephone surveys, enabling to understand what kind of improvements people expected from the industry. In addition to gathering information from users, data was collected by means of electronic equipment inside the vehicle (this is the case of "Project 200" in Gothenburg and "Elcidis in La Rochelle).
Case-based modelers can/should expect case anomalies. Such case anomalies are identifiable by discretizing continuous variables into quintiles-thus, not just by using 5-level responses to variables as appearing in Table  1. Calibrating values of continuous variables (or variables with a limited number of responses) into membership scores ranging from 0. 00 to 1. 00 is simply an extreme form of discretizing data. Software programs (e. g. , fsQCA. com) are available to provide a smooth-fitting logarithmic calibration scores for continuous data. Nearly all researchers in the behavioral and management sciences using symmetric tests (e. g. , correlations and regression coefficients) stop their analysis after finding statistical significant support of directional relationships and then reporting that the relationship is positive or negative.
Including Part A conditions in directional studies examining firms' strategy behaviors and outcomes as integral to model construction appears only rarely in the literature. To be sure, firm demographic data are collected and their frequency distributions appear in early tables of journal articles but then testing is rarely performed on firm demographics in directional tests of firms' strategies or outcomes. Taking a small step away from presenting simple main effects directional hypotheses, some NHST studies examine one or two demographic variables as "moderators" to the main directional hypotheses. Part B in Fig. 1  is a second Venn diagram that represents four firm strategic orientations (SOs) and their combinations. SOs represent the broad strategic choices firms make  (Gatignon & Xuereb, 1997)  and/or unconsciously or consciously decide not to adopt.
Method The data for examining the propositions related to Fig. 1   2017 ) provide an asymmetric based theory and SPOT analysis of the data. For the first time the theory construction and data analyses in the present study are present in asymmetric analyses of complex antecedents to the four-corner firm performance outcomes. Data were used from samples of 820 and 300 SMEs with different industry affiliations located in Finland and Hungary, respectively. The data were collected in late 2009 and early 2010 using a survey sent to the CEOs and managing directors of these firms. Firms operating in different industries offer variation in the rate of change and uncertainty of the firm's competitive environments that would not be available focusing on a single industry. The questionnaire was sent to 9454 Finnish firms, yielding a response rate of 8.
00 to 1. 00. Thoughtful attention is necessary for calibrating values into membership scores. Most differences in different calibrations of values into scores have small impacts on the final analysis of the impacts of complex antecedent conditions on outcomes-but big differences in calibrations can dramatically reduce or increase in the impact of a simple condition in affecting an outcome condition-an example of this observation appears below. The software program at fsQCA. com has a subroutine that converts a scale's values into a logarithmic scale whereby the researcher needs to specify three membership scores for the "threshold for full membership" equal to 0. 95, the "threshold for maximum ambiguity" equal to 0. 50, and the "threshold for full non-membership" equal to 0. 05. See  Ragin (2008)  for full details on calibrating values into membership scores.
The "coverage index" measures the number of cases high in the outcome condition for the cases high in X relative to all the cases high in the outcome condition. If 80 cases have scores high in Y and a complex antecedent condition identifies 8 of these 80 cases, then the complex antecedent condition has a coverage equal to 0. 10. The fsQCA. com software program reports raw and unique coverage indexes for the findings for each model. The unique coverage refers to only cases that a specific model identifies as high in the outcome (Y). The raw coverage includes the cases the specific model and alternative complex antecedent condition models identify as high in the outcome (Y). Using the XY plot subroutine in the fsQCA. com software reports the consistency and raw coverage indexes. A useful rule to apply is that an antecedent model is applicable for identifying high Y cases when consistency is above 0.
80 even if coverage is very small (e. g. , <0. 02). An application of achieving requisite variety is to construct a small basket of applicable models with each model having a consistency above 0. 80 and a coverage greater than 0. 01. In constructing models using the fsQCA. com program, the researcher can specific a "frequency cutoff" and a "consistency cutoff" that models must achieve. The higher the cutoffs the fewer the models constructed via the analyze sub-routine in the fsQCA. com software. Of course, the researcher should construct fuzzy statements of specific models for testing their applicability in identifying high Y outcomes. For example, testing the models appearing as equations 2-4 in this essay is done and reported in the findings section. However, due to the newness in applications of complexity theory and asymmetric case-based modeling in the late teen years of the 21st century, doing both approaches to model construction may be best-especially since the analyze function in the software frequently generates counter-intuitive models.