dataesr/scientific-paragraphs-categorization huggingface dataset:
In practice, one may therefore verify that S⊥ ⊥X and, if this holds, proceed by ignoring X in the duration analysis. 23 ith discrete X, nonparametric inference would typically lead to separate estimations for each value of X. This would also allow for the selection of the most similar control cohort for each value of X separately. To aggregate the estimated average effects over X, one may average the estimated effects given X over the relevant distribution of X. 24  (iii) Discrete time Now let us reconsider the continuous nature of the duration variable. Sometimes a continuous-time analysis may be unfeasible. For example, the data may be time-aggregated in the sense that events are recorded in time intervals (e. g. , unemployment duration is collected in months even though individuals may enter employment on any given workday).
They find that a reduced cap for sickness benefits lowers the transition rate to sickness absence by about 35% in the treated population. In their study, they also estimate MPH-type model extensions that allow for unobserved heterogeneity as proportional fixed effects in the individual hazard rates, exploiting the fact that the data contain multiple unemployment spells for many subjects. Interestingly, they find that the estimated policy effect is virtually identical to that in their main analysis, suggesting that, indeed, this coefficient is estimated correctly even when ignoring unobserved heterogeneity.
28 Notice that it is not possible to identify effects of the actual participation in the NDYP at 6 months among those who are unemployed for less than 6 months at τ * , since such individuals may act upon the information about the future treatment throughout the time interval between τ * and the moment that t = 6. Two minor institutional features constitute from the above description of the introduction of the policy. First, individuals with t < 6 at τ * can try to apply for early enrollment, especially if they are disadvantaged (disabled, former convict or lacking basic skills). Such applications were rare at the initial stages of the program. Secondly, the NDYP was introduced in a few small pilot areas on January 1, 1998, i. e. 3 months before the national rollout. We use the data from these areas and shift calendar time with 3 months when combining these data with the data from the rest of the country.
We include data on both pilot and nonpilot regions in all that follows but designate each cohort by the month of inflow in non-pilot areas for simplicity. Following the discussion in Section 4. 2, different candidate groups are assessed based on two outcomes: the distribution of observed characteristics X among survivors at 182 days and the distribution of T on days 1 to 181. We start by contrasting the observed characteristics of survivors at 6 months in the treatment and comparison groups. Table  1  shows some summary statistics. The first take away from the table is that the cohorts are small. This is a consequence of the time window for inflow being narrowly defined to capture the discontinuous change in policy while ensuring that the cohorts are comparable and live through the same economic environment.
ACKNOWLEDGMENTS We are grateful for comments on early versions received from  Irina Prokofieva ,  Mario Torralba , and an anonymous journal reviewer; statistical research assistance from  Dario Schulz (ZEF, Bonn) ; and financial support from the  European Commission  (SINCERE,  H2020 GA 773702 ),  Norad  ( CIFOR GCS-REDD ), and the  German Federal Ministry of Education and Research (BMBF).
The authors acknowledge the financial support of the  French Agence Nationale de la Recherche (ANR). Mabel Tidball  received funding from the project  GREEN-Econ  ( ANR-16-CE03-0005 ) and the  LabEx Entreprendre  ( ANR-10-LABX-11-01 ). Denis Claude received funding from the project RILLM. The authors would like to thank the participants of the 18th International Symposium on Dynamic Games and Applications in July 2018 and the seminar participants at  Center for Environmental Economics -Montpellier  and the  University of Valladolid  for helpfull discussions. The usual disclaimer applies.
Edwards and Jenkins (2015)  used a database of 44 manufacturing industries at the firm level covering the period 1992-2010, and a Chenery-type decomposition and econometric estimation, to evaluate the impact of Chinese trade on prices, production, and employment in the South African manufacturing sector. Fu et al. (2015)  evaluated the impact of trade with China on firm productivity in Ghana. These sectorial studies were limited to one country, however, and are therefore unable to provide a picture for African countries generally.
METHODOLOGY AND DATABASE The main objective of this study is to examine the direct trade effects: the impact of importing Chinese textile products by African countries on the production and exportation of products in their textile sector. It does not cover the indirect trade effects, for instance, the thirdmarket effect mentioned in Section 2, which may have equal importance in explaining African textile production and exportation. The study is motivated by the idea that an increase in imports of intermediate goods from a more advanced country could increase the less-developed country's exports. This idea comes from the theory of appropriate (or intermediate) technology based on  Schumacher (1973). More recently,  Xie (1999)  has shown that the benefit of learning from the more developed country can be realized in the less-developed country only when the technology gap is within a certain range such that technological interaction between the two countries exists.
As the database covers 1990-2017, 53 African countries, and 14 subsectors, a complete set of observation numbers must be 28 (years) × 53 (countries) × 14 (sectors) = 20,776. I drop 1790 observations due to the lack of data on imports from the chosen partners (by country, sector, and year). As such, 18,986 observations are available for the regressions. The 14 categories of textile products, as listed in Table  1 , correspond to 14 subsectors. From this table, based on the Harmonized System (HS) classification, goods are arranged in order of their degree of manufacture: from raw materials, unworked products, semifinished products, to finished products. The pertinence of this method of classification, however, is relative. The articles classified into 57-63, more likely regarded as finished products, can also be used as intermediates, and transformed into new varieties of clothing.
Moreover, the same goods may be recorded in different categories by the exporter and the importer. Important missing data in the import and export reporting of a number of African countries are also observed owing to the imperfections in their customs and statistical systems. A natural way to fix these problems would be to consider the mirror data method by automatically using the data used by the partner countries. As the provider of Comtrade data recommends caution in the use of this method, in general, the data of African reporting countries are used. I use the mirror data method only in the case of missing data-for example, when the African country's reported value was zero, whereas that of its partner country was positive. Besides this, modifications are made in two cases. First, if the African country (e.
Data and Descriptives Our sample consists of 139 firms listed on the Athens Stock Exchange for the period 2006-2017, as the IFRS were adopted in Greece in 2005. Accounting data were collected from firms' annual balance sheets and financial statements. Stock prices were retrieved from internet  [48]. To be included in the sample, necessary accounting and market data must have been available. In addition, the sample was confined to firms with December fiscal year-ends. Banks, financial, assurance, and real estate companies was excluded. The exact sample size was 1668 total observations. The market value of common stock (MV it+1 ) was as of the first day of April in year t + 1. This allows a 3-month filing period for year t financial statements, to ensure that market value is measured after the release of the information.
Concluding Remarks This study explored the impact of the reported R & D expenditure on the value relevance of financial statements. We measured the R & D expenditure under the assumption that it is reflected in the R & D disclosed expenses. Ohlson's model was adopted for the estimation of the value relevance of the disclosed accounting information. We imported, in addition to the typical variables of Ohlson's equation, R & D intensity and R & D disclosure as independent variables in the model. The research period was divided into the "pre-crisis" and "during the crisis" periods. Two different quantitative analysis approaches were implemented in order to test our research hypotheses. Although OLS regression analysis has been used in previous studies, we applied the WLS panel data regression analysis to avoid heteroscedasticity.
Data The information about market systemic risk measures of financial institutions is publicly available. We took the data from the Volatility Laboratory (V-Lab) website. The data concern the whole world for which financial data are publicly available. To be able to compare them with stress test results, we need to use exactly the same banks. We know that there are 123 European banks for which the stress test exercise was conducted. We have the possibility to select 57 banks from the V-Lab website. The main reason for this difference is that the banks in the stress test exercises, which are not available on the V-Lab website, are not publicly traded. We show in Table  1  (Panels A and B) the aggregate results of common banks between the V-Lab and the stress tests. From this table, we can notice the large difference between the "loss" and the "net loss" of the stress tests for both scenarios.
If we compare the V-Lab methodology with the regulatory stress tests, we can conclude that the V-Lab scenario is much easier than the complex multi-factor scenarios of the stress tests. We know that the methodology used for the stress tests is hard to understand. Thus, the V-Lab results are robust to any economic environment. We also know, on the one hand, that stress tests are constructed from the details in the information given to supervisors. On the other hand, the V-Lab outcomes are based on publicly available market data that allow forecasting. Thus, the V-Lab is considered as a benchmark that regulators should use to evaluate their own stress test results. The only results taken from the V-Lab website are the and. We construct the using the STATA software and following  Adrian and Brunnermeier's (2016)   Standard Industry Classification Code (sic).
The main reason for this gap is that the stress test losses are projected over three years whereas the V-Lab loss is a six-month loss. The rank correlations of the V-Lab loss with the stress tests' projected losses (total loss, trading losses and impairment losses) are very high and significant for both scenarios (baseline and adverse) and for the three years (Table  2 , Panel A). This makes the V-Lab's ranking and the ranking of losses in supervisory stress scenarios very consistent. However, the correlations of the V-Lab loss with the total net loss are negative or almost equal to zero for both scenarios and during the three years. This means that banks with higher profits in EBA stress scenarios are predicted to have larger losses in the V-Lab, and this arises because we find in our databases that some banks still report positive profits in the baseline and adverse scenarios, in which stressed revenues cover stressed losses.
ACKNOWLEDGEMENTS We would like to thank  Editage  (www. editage. com)for English language editing. YH is supported by  JSPS   Grant-in-Aid for Scientific Research  (C)  JP18K11461. YS is supported by  JSPS   Grant-in-Aid for Scientific Research  (C) No. 18K03441. DF and YS have are supported by a  PICS-CNRS  grant "Dessert".
7. Figure  5  shows the cross section of the contrast function at θ = θ 0 = 0. 5. We see that the maximum in (α, σ) is reached near the true value (α 0 , σ 0 ). Eventually, maximizing with respect to the three parameters, by Python Numpy package, yields to βn = (0. 495, 0. 849, 0. 709) and the quasi-Newton maximization algorithm converges after 18 steps.
In the first stage, we measured the level of DTE advertising efficiency for a sample of US pharmaceutical firms using data envelopment analysis (DEA). In the second phase, using advanced econometric modelling, we explored whether or not the level of DTE advertising efficiency affects the profitability of the sample firms. The results showed that firms do vary in their DTE advertising efficiency and that this variation is correlated with varying levels of profitability. In sum, firms with a higher level of DTE advertising efficiency also tend to be more profitable. The paper is organized as follows. Section two below briefly describes the research context, explains the theoretical background of the study as well as developing the central hypothesis to be tested. Section three outlines the research methodology used and describes the data sources and measurement issues.
Data sources and Sampling frame The sample for this study was drawn from a single B2B industry, namely, pharmaceuticals, and from a single country, namely, the USA. This research context was chosen for a few reasons. Firstly, due to the deregulation of direct to end-user (DTE) advertising by the FDA, US pharmaceutical firms are increasingly using DTE advertising as pull activities alongside their push promotional activities. Furthermore, the yearly budget for DTE advertising is growing at a much faster rate compared to other forms of promotional initiatives. The choice of a single industry was made to satisfy a requirement of data envelopment analysis which assumes homogeneity among the firms under investigation. The study focused on US-based pharmaceutical firms because only the USA and New Zealand allow DTE advertising for prescription drugs.
The sampling frame for this study was the top USA advertisers published by Advertising Age. Advertising Age data have been used extensively by earlier studies (e. g. Luo & Donthu, 2001; Cheong, et al. , 2014). Advertising expenditure data were collected for the large US-based pharmaceutical companies which appeared among Advertising Age's leading national advertisers. This fits well with data envelopment analysis which measures the relative efficiency of a focal firm in comparison to other firms in the sample. Furthermore, these firms are large suggesting that they are equally likely to enjoy economies of scale in their operations compared to smaller firms  (Wei & Varela 2003). Data were collected in two stages. In the first stage, advertising expenditure data were collected from the Advertising Age's datacentre.
Data, variables, and methodology This study examines the link between bank stability, competition, and economic growth in a sample of 38 European countries over 2001 to 2017. 2  The macroeconomic data is from World Development Indicators published by the World Bank. Bank competition and stability measures are from Bankscope, Orbis Bank Focus and the Global Financial Development Database. This study uses two measures of economic growth (ECG), two measures of bank stability (BST), and one measure of banking competition (BNE). In the economics literature, annual growth rate and per capita growth rate are widely used measures of economic growth. This study uses both measures for the robustness of the results. The economic growth measures are annual GDP growth rate (AGR) and annual GDP per capita growth rate (CGR).
The bank stability measures are bank Z-score (BZS), which is the distance to default for a bank in standard deviations, and the ratio of non-performing loans to gross loans (NPR). The measure of bank competition is the Boone indicator, which measures bank competition in relation to marginal costs. Economic growth is measured at the country level. Hence, both stability measures are transformed to the country level by taking the weighted average; and weights are based on asset size of banks in each country. Appendix A provides definitions of the variables and data sources. The following equation estimates the effect of bank stability on economic growth. , , 1 1 , 2 3 , 4 ,. • • • •. • i t i i t i t i t i t i i t ECG BST TPN LFF GEX EAL - = α +β + γ + γ + γ + γ + ε + µ (1) In Eq. (  1 ), ECG represents the two proxies of economic growth: annual GDP growth rate (AGR) and annual GDP per capita growth rate (CGR); α is the country fixed effect and β is the main coefficient of interest in this study.
Samples Data were collected online by an independent polling company (Ipsos) in April and May 2015. Quota sampling was employed to obtain samples representative of the general population with respect to sex, age, occupation, region, and population density of the UK (n = 1509), France (n = 1501), and Germany (n = 1502). Sample weights were calculated using the random iterative method (RIM) to match the latest data available in each country (census 2011 for the UK and Germany, census 2012 for France). Participation in our general population samples was voluntary and data protection laws obeyed by Ipsos. If a respondent chose to drop out at some point, the data given until that point was not included. As skipping items was not possible, there were no missing data.
We use cross-validation to check for overfitting  [42]. With this in-sample cross-validation technique, the initial dataset is randomly split into 10 subsamples of approximately equal size. One of these subsamples is kept for validation, while the other nine subsamples are used for parameter estimation. This process is repeated ten times, and the results are averaged across repetitions. Overfitting would show when a model's nRMSE is substantially smaller than the average nRMSE of the models of the 10 subsamples. We used R version 3.
e. , the specific values of the regression coefficients used). Because we were also interested in which factor is mainly responsible for the differences in prediction performance, we moved stepwise from Revicki's model to our models as follows: First, we used the five health domains of Revicki's model, but with regression coefficients optimized towards the data collected in each country separately. Second, we investigated the incremental value of adding either sleep disturbance, participation, or both to the prediction equation. Third, we allowed for incorporation of quadratic and/or cubic effects. EQ -5D = 1. 0266 + 0. 0077 × Physical functioning -0. 0021 × fatigue -0. 0040 × Pain interference -0. 0023 × anxiety -0. 0022 × depression to obtain coefficients which can be used for prediction independently.
Our results in terms of the nRMSE and nMAE perform very well compared to what is usually reported for mapping algorithms  [17, [43] [44] [45] [46] [47]. The major comparator to our models is Revicki's OLS linear US model, the only one predicting the EQ-5D-3L index value from PROMIS-29. All our models perform better in terms of R-squared and ICC while the LoA were comparable. Revicki did neither report MAE nor RMSE. Furthermore, Revicki's uses the former version of the EQ-5D, the EQ-5D-3L with the US value set as target measure, while we use the EQ-5D-5L crosswalk value sets from the UK, France, and Germany, respectively. We demonstrated that the application of Revicki's US model to European data will yield biased results, especially for poor health states. However, this model performs well in upper ranges of health.
Conclusion Our mapping functions can be used to predict the EQ-5D-5L crosswalk from the PROMIS-29 for CUA in HTA for the UK, France and Germany. The inclusion of polynomial regression terms decreases the prediction bias for lower HSU. Our results support the assertion that mapping functions are country-specific. The application of Revicki's model to the data collected in the three European countries leads to biased HSU estimates for the UK and France and to less precise estimates in all three countries. Estimation of country-specific regression coefficients for the five health domains identified by Revicki strongly improves the average prediction performance but does not remedy the overestimation of low HSU.
adjR 2 trial is obtained from the regression model on the estimates of the trial-specific treatment effects on both the surrogate and the true endpoints, after adjusting on the estimation errors obtained in the first-stage model. The programs that implement this method are available in the R package surrosurv  [9]  and the SAS macro %COPULA  [10]. However, the practical use of the two-stage copula model is often difficult, mainly due to convergence issues or model estimation with the adjustment on the estimation errors  [11] [12] [13]. This drawback led to the development since Burzykowski et al. [8]  of alternative approaches  [11, [13] [14] [15] [16] [17]. Most of the novel methods, except that of Sofeu et al. [17]  and Rotolo et al. [13] , are based on a two-stage validation strategy. Alonso and Molenberghs  [14]  proposed an information theory approach, with a new definition and quantification of surrogacy at the individual level and the trial level.
For the approaches described in the previous paragraph, the R package surrogate  [19] , the SAS macros %TWOSTAGECOX and %TWOSTAGEKM, and the SAS programs available in Alonso et al. [10]  were provided to carry out the evaluation exercise. Rotolo et al. [13]  proposed a one-step validation approach based on auxiliary mixed Poisson models, which employs a bivariate survival model with an individual random effect shared between the two endpoints and correlated treatment-by-trial interactions. Simulation results described by the authors showed estimation biases on the surrogacy assessment measures, especially in the event of a high association and when heterogeneity of baseline risk is taken into account. The associated program was implemented in the R package surrosurv  [9]. Renfro et al. [11]  suggested estimating the second-stage model in a Bayesian framework and the estimate of the adjusted R 2 trial was then based on the posterior distribution of the parameters of the adjusted model.
The proposed joint surrogate model showed satisfactory results compared to the existing two-step copula and one-step Poisson approaches. We aim in this paper to popularize this new surrogate endpoints validation approach by making the method available in a user-friendly R package (frailtypack). We have developed a prediction tool for the treatment effect on true endpoints based on the observed treatment effect on surrogate endpoints. Interpretation of R 2 trial and decision-making about the validity of the candidate surrogate endpoint are possible thanks to the classification suggested by the Institute for Quality and Efficiency in Health Care (IQWiG)  [21] , and surrogate threshold effect (STE) introduced by Burzykowski and Buyse  [22]. Other tools are for displaying the basic risks and survival functions, for model assessment, and for data generation based on the joint surrogate model.
Another attractive goal of this article is to provide a tool to perform simulation studies. frailtypack is an R package that fits a variety of frailty models containing one or more random effects, or shared frailty. It includes a shared frailty model, a joint frailty model for recurrent events and terminal event, others forms of advanced joint frailty models  [23] , and now a joint frailty model for evaluating surrogate endpoints in meta-analyses of randomized controlled trials with failure-time endpoints. In this paper we focus on a particular subset of features applicable for evaluating surrogate endpoints. The rest of this paper is organized as follows. In the next section, we summarize the joint surrogate model with the estimation methods and the surrogacy evaluation criteria. We end it with the definition of STE.