dataesr/scientific-paragraphs-categorization huggingface dataset:
While the nature of BSJBSA data prevents this article from testing the proposition that Japan's SME policies such as the credit guarantee system supported zombie firms among Japanese SME, the finding of a high zombie ratio among small firms during the 2000s suggests that these zombies were underpinned by factors that did not similarly affect large firms. Future work should specifically examine the situation of firms using SME support policies to determine whether such measures have been responsible for generating zombie SME. This could be a key lesson from Japan for scholars and policymakers concerned with zombie firms. Zombie SME are likely problematic from the view of the efficiency of the industries to which they belong. But when one considers that many zombie firms seem to achieve revival as our findings suggest, it would appear inappropriate to uniformly promote their elimination.
Acknowledgements This work was supported by  JSPS KAKENHI  Grant Number  JP26285068 , a 2018  Murata Science Foundation  research grant, and the  Banque de France fellowship program  at the  EHESS-Fondation France-Japon , and conducted as a part of 2018 fiscal year project research with the  Kyoto Institute of Economic Research, Kyoto University (KIER) Joint Usage and Research Center. It utilizes micro data in the form of questionnaire information from the  Ministry of Economy, Trade and Industry (METI) Basic Survey of Japanese Business Structure and Activities. The authors are grateful for helpful comments and suggestions by  Yoshiaki Shikano  ( Doshisha Univ. ),  Nobuyoshi Yamori  ( Kobe Univ. ),  Kentaro Imai  ( Osaka Univ. ), discussion paper seminar participants at  RIETI ,  President Makoto Yano ,  Vice President Masayuki Morikawa ,  Senior Fellows Yoko Konishi  and  Yoichi Sekizawa , and comparative politics seminar participants at the  University of Southern California.
Finally, the VaR and CVaR portfolios produced by the copula simulation are compared to those produced from the historical record. To that end, extreme value theory (EVT) is applied to estimate the tails of abnormally distributed marginal density functions (see  Embrechts et al. 1997; Hotta et al. 2008 ; among others). The distribution of each return series is fitted semi-parametrically. A benefit of this approach is that it requires estimating the lower and upper tails parametrically, while the interior is estimated by the non-parametric, kernel-smoothed interior method. Our data cover the G7 group (the United States (US), Canada, France, the United Kingdom (UK), Italy, Japan and Germany), the BRICS group (Brazil (Bra), Russia (RS), China, India, South Africa (SA)) and 14 popular emerging stock-market returns other than the BRICS group (Chile, Mexico, Peru, Czech Republic (Czech.
R), Greece, Hungary, Poland, United Arab Emirates (UAE), Indonesia, Korea, Malaysia, Philippines, Taiwan and Thailand). The sample data recorded during the period between 31 March 1997 and 30 April 2018 were used to compose the global portfolio. The empirical results show that the historical simulation model produced aggressive VaR and CVaR estimates compared to those provided by copula simulations. This result thus makes VaR and copulas attractive and efficient tools for portfolio selection, as they can capture the extreme events that characterize our recent economic conditions. Furthermore, we find that US investors can find a minimum CVaR portfolio in developed economies rather than in many other developing or emerging stock markets.
More recently,  Nikusokhan (2018)  employed the CVaR Glosten-Jagannathan-Runkle (GJR) Copula method and the emerging market data for portfolio risk identification and portfolio optimization. The empirical evidence suggests that the performance of the GJR-Copula-CVaR method is relatively more accurate and more flexible than other common methods of optimization. Sahamkhadam et al. (2018)  used GARCH-EVT-copula and Auto-Regressive-Moving-Average (ARMA)-GARCH-EVT-copula models to perform out-of-sample forecasts and simulate one-day-ahead returns for 10 stock indexes. They constructed optimal portfolios based on the global minimum variance (GMV), minimum conditional value-at-risk (Min-CVaR) and certainty equivalence tangency (CET) criteria, and modeled the dependence structure between stock market returns by employing elliptical (Student-t and Gaussian) and Archimedean (Clayton, Frank and Gumbel) copulas.
Their main finding is that the CET portfolio, based on ARMA-GARCH-EVT-copula forecasts, outperforms the benchmark portfolio based on historical returns. Moreover, the regression analyses show that GARCH-EVT forecasting models, which use Gaussian or Student-t copulas, are best at reducing the portfolio risk. Fernando et al. (2017)  mixed Archimedean copula function and the CVaR minimization model to obtain efficient portfolios. Using data from the S&P500 stocks from 1990 to 2015, their empirical analysis shows that the Mixed Copula-CVaR approach generates portfolios with better downside risk statistics for any rebalancing period and it is more profitable than the Gaussian Copula-CVaR and the 1/N portfolios for daily and weekly rebalancing. Beyond the existing literature, our main contribution is in combining the Generalized Pareto distribution copula approach and VaR/CVaR risk measures to find an empirical evidence of efficient portfolios (i.
Results and Interpretations Figure  1  shows plots of the distribution probabilities produced with the returns of two arbitrary markets (i. e. , Brazil and Italy). It is important to note in this figure that the Pareto-distribution probability fits the data better than is the case with any other method, such as the normal distribution. In particular, we can show that the returns of the Brazilian and Italian stock indexes have a heavy left tail, which corresponds to the Pareto tail distribution with negative shape parameters. Risks 2019, 7, x FOR PEER REVIEW 5 of 22 it exceeds VaR. Following  Rockafellar and Uryasev (2000, 2002) , the () of the loss associated with any w, it is found that 𝐶𝑉𝑎𝑅(𝛼) = 𝑙 + 1 1 -𝛼 𝑚𝑎𝑥 (𝑓(𝑤, 𝑟) -𝑙) 𝑝(𝑟)𝑑𝑟. (4) The problem of minimizing the CVaR can thus be formulated as follows:  (), (5) 𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑤 = 1 , (6) -𝑤 𝐸(𝑟) ≤ -𝑟 * , ( 7 ) where Equation (  6 ) is the weight constrain condition, Equation (  7 ) the expected return of the portfolio and p(r) is the joint distribution of the uncertain return of stocks exceeding a certain amount  *.
Indeed, despite its volatility profile, the emerging market offers diversification to US investors (see  Ted 2016). Nevertheless, it is essential to note that the correlation estimations in Figure  2  have to be considered with great caution because, while they measure the linear dependence, other types of dependences (e. g. , tail dependence) may not be captured. To alleviate this problem, we used a copula approach. Although the calibration of the linear correlation matrix of a Gaussian copula is straightforward, the calibration of a t-copula is not. For this reason, we transformed t-centered returns to uniform variates by the piecewise, semi-parametric CDFs derived above (Note that the transformed dataset and corresponding distribution are available upon request). The transformed marginal distribution, which was modelled on the basis of GDP, was then used to fit the Gaussian and t-copulas.
For this reason, we transformed t-centered returns to uniform variates by the piecewise, semi-parametric CDFs derived above (Note that the transformed dataset and corresponding distribution are available upon request). The transformed marginal distribution, which was modelled on the basis of GDP, was then used to fit the Gaussian and t-copulas. Figure  3  presents an accurate and interpretable characterization of the local dependence of the Gaussian and t-copula by means of a red/blue surface plot (Please see Appendix A for more details on elliptical copula parameters). This makes the interpretation much easier. As may be seen from Panels A and B, that the Gaussian and t-copula give very similar results. Moreover, the dependence structure among the US and considered stock markets is somewhat different.
The obtained results depend on the characteristics of the dataset. On the other hand, Table  2  supports the conclusion that developed stock markets are a more effective tool in reducing portfolio risks than are many emerging stock markets. When extreme market events are taken into account, however, the emerging markets are more volatile. Referring again to Table  2 , it may be seen that the structures of the optimum portfolio given by the multivariate normal distribution are significantly different from those of the optimum portfolios created by simulating copulas. This kind of information may be of interest to the risk manager who wishes to formulate a hedging plan (e. g. , derivatives) against potential losses. Figure  6  shows the efficient-frontier graphs using Gaussian (Panel A) and t-copula (Panel B) estimations for various sets of observations.
Acknowledgements The authors wish to thank the  ECRIN-ERIC Network Committee  members as well as other staff of the  ECRIN Scientific Partners  for their contributions to the survey and feedback on the report and publication. Thanks to  Michaela Egli  ( Swiss Clinical Trial Organisation ) for helping with the manuscript. HECRIN -Hungarian Clinical Research Infrastructure Network, Budapest, Hungary. 7HRB   CRCI -Health Research board, Clinical Research Coordination Ireland, Dublin, Ireland. 8F-CRIN -French Clinical Research Infrastructure Network, Paris, France. 9PTCRIN -Portuguese Clinical Research Infrastructure Network, Lisboa, Portugal. 10ItaCRIN -Italian Clinical Research Infrastructure Network, Rome, Italy. 11KKSN -Netzwerk der Koordinierungszentren für Klinische Studien, Hannover, Germany.
Acknowledgements The author expresses gratitude to  Professor Natalia KOVTUN ,  D. Sc. for providing insights and motivational inputs at different stages of this research study. Open access funding is provided by  University of Eastern Finland (UEF)  including  Kuopio University Hospital, Kuopio, Finland. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
All authors agree that the initial acceleration phase is dominated by drag  [14]  but once the boat reaches its steady motion lift plays a role  [15, 16]  as well as the elasticity of the shaft  [17] [18] [19]. Theoretically, most of the studies are developed in the footprints of the pioneering work of Alexander  [20]  and show that observations can be satisfactorily approached with a one dimensional momentum balance, infinitely stiff oars with inertia and non-infinitesimal stroke angles, and quadratic relationships between force and velocity for the boat and oar blade  [21]. We will follow those footprints to develop our model. Concerning the effect of oars' length, Nolte  [6]  performed an empirical study on a dataset of rowing races. He reported that 'Shorter Oars Are More Effective'. However, Laschowski et al  [22]  studied experimentally the effect of oar-shaft stiffness and length with elite athletes.
The oldest data points were obtained from race photographs, while the more recent ones come from  [6]  or were provided by the French athlete Thomas Baroukh. (d) Evolution of the mean speed of the winner boat at the Oxford and Cambridge Boat Race (data gathered from [7]). (e) Evolution of the dimensionless blade area β=S/S c with S the blade area and S c =S h C h /(NC d ), where N is the number of blades, C d the drag coefficient of a blade, S h the hull wetted surface and C h the hull drag coefficient (for all the points, the hull wetted surface is taken constant as that of a coxless four rowing boat S h =5. 92 m 2 ). The black dots are for sculling blades and red dots for sweep blades. Data come from  [6]. we present master plots on the efficiency of rowing boats and discuss the particular case of sweep oars optimization.
Consistent with actual data which reveals a catch angle of ≈-55°and finish angle of ≈35°(see figure  3(a) ), the angular travel of the oar was fixed to θ 0 =90°  [4]. The recovery phase and the blade flips were ensured by two servomotors and position sensors connected to an Arduino TM board. The masses are suspended to a unique support (see figure  4(a) (3)) and the stability of the boat was ensured by four polystyrene floats (see figure  4 (a)(2)). The experiments were performed at the swimming pool of Ecole polytechnique. Setting the recovery phase time to a constant value T (r) =1. 3 s (such that it roughly matches empirical conditions: 40% propulsion against 60% recovery), we video recorded the model rowing boat over 25 m for four different oar lengths, with corresponding aspect ratios spanning from α=5 to α=8.
(d) Evolution of the mean speed of the winner boat at the Oxford and Cambridge Boat Race (data gathered from [7]). (e) Evolution of the dimensionless blade area β=S/S c with S the blade area and S c =S h C h /(NC d ), where N is the number of blades, C d the drag coefficient of a blade, S h the hull wetted surface and C h the hull drag coefficient (for all the points, the hull wetted surface is taken constant as that of a coxless four rowing boat S h =5. 92 m 2 ). The black dots are for sculling blades and red dots for sweep blades. Data come from [6].
Pioneering domestic environmental regulation may foster the creation of new eco-industries. These industries could benefit from a competitive advantage in the global market place. This article examines empirical evidence of the impact of domestic renewable energy policies on the export performance of renewable energy products (wind and solar PV). We use a gravity model of international trade with a balanced dataset of 49 (for wind) and 40 (for PV) countries covering the period 1995e2013. The stringency of renewable energy policies is proxied by installed capacities. Our econometric model shows evidence of competitive advantage positively correlated with domestic renewable energy policies, sustained in the wind industry but brief in the solar PV industry. We suggest that the reason for the dynamic difference lies in the underlying technologies involved in the two industries.
Contrary to Groba  [40] ; however, we find no evidence of a robust first-mover advantage in the solar industry as we find that policy-induced competitive advantages in this industry are only short-lived. Also using the gravity model of international trade, we expand on the results of Costantini and Crespi  [38] ; Costantini and Mazzanti  [39] ; Groba  [40]  and Groba and Cao  [41]  in several aspects. First, our regressions covers the period 1995e2013, five to six years more, which may matter as renewables' industries evolve extremely quickly. Second, our dataset is more comprehensive on sectoral and geographical coverage: we focus on both wind and solar PV, and use balanced dataset of 49 (for wind) and 40 (for PV) countries comprising major developed and emerging countries. Given the recent emergence of wind and solar PV manufacturing industries in countries such as China and India, the traditional focus on the exports of EU or OECD countries is no longer warranted.
Model specification The dependent variables are bilateral export flows for wind and solar PV goods, from country o to country d at time t. We include time t in the in the model as there are a number of well-known advantages of using panel data instead of cross-sectional data to estimate a gravity model. These advantages include the efficiency of estimation and the treatment and estimation of time-invariant bilateral trade costs between any pair of countries with countrypair fixed effects  [43]. We clearly separate wind and solar PV industries in the regression in order to reduce aggregation biases as suggested by Anderson and Yotov  [62]. We use a balanced dataset of 49 and 40 countries for wind and solar PV respectively (see Table  A1  in Appendix). The choice of countries is data-driven: we kept countries which either had significant installed capacities (virtually all installed capacities are in these countries) or are big exporters (in the world top 20 of exporters, such as Malaysia for solar PV).
Trade data in renewable energy technologies Data from export flows in the solar and wind industries are extracted from the UNCTAD COMTRADE database. A caveat for using trade data is that the matching between 6-digit HS codes and renewable energy technologies is far from being perfect. Indeed HS codes are related to components for which the usage is unknown: the same components may be used in renewable energy or other industries. In addition the categories may be relatively wide and correspond to several products. Following Wind  [70] ; the International Centre for Trade and Sustainable Development (ICTSD) identified HS 6-digits product category codes according to the different renewable energy sectors  [71, 72]. Their product 5 The CEPII database has recently been updated with trade agreement variables up to 2015.
For our country dataset, the difference in trade agreement variable between 2006 and 2013 occurs only in 5% of country pairs. We re-ran the main regressions with the updated variable and the results were almost similar and did not affect our conclusions. 6 Those are guestimates, but changing them does not change significantly the results. First, data is piecemeal for decommissioned capacity. In the EU 324 MW were decommissioned in 2013  [80]  out of 106 454 installed MW in the beginning of this year (so 0. 3%). Data for maintenance (replacing parts of wind turbines for example) is even harder to estimate. 7 http://www. gwec. net/global-figures/interactive-map/. 8 Changing this threshold does not significantly change the results. categorization is displayed in the Appendix,Tables  A2  and A3.
In 2011, they reached 120 billion US dollars (a six times increase in less than ten years) but further decreased in 2012 and 2013 (85 billion US dollars). In terms of market shares, the top 7 exporters account for around 60% of total exports of wind goods in our dataset. Japan, the US and France lost market shares during the last decade. China gained continuously market shares from a couple percent of world exports to about 10%, and three European countries (Germany, Denmark and Spain) increased their market shares especially before 2010 (in 2008 they accounted for about 35% of exports). In the solar PV industry, the top 7 exporters represent between 60 and 70% of world exports. China which was already among the top exporters in the early 2000's with a 15% market share, doubled its position in 2011, being the world leader by far.
The US and Japan lost substantial market shares during the last decade while Korea and Malaysia increased significantly their market share after 2008. Because trade flows are bilateral, information about exporters only give a partial view of international trade. First, international trade is highly concentrated, with a few bilateral trade flows representing a significant amount of total trade. In addition, the concentration is noticeably higher in the PV sector than in the Wind sector. At their highest level of international trade (2011 for solar PV and 2012 for Wind), 10% of the country pairs in the dataset accounted for 85% of total trade for both PV and wind. However the top 5 bilateral trade flows represented 24% of total trade for PV compared to 12% for wind. Even with a high concentration, the global picture is still complex, as there is a very high number of potential bilateral trade flows.
For both industries, the estimated coefficients are statistically significant at the 1% level for both estimators. The estimated values are quite similar for the PV industry for both estimators: 0. 06 and 0. 08. However they don't have the same order of magnitude for the Wind industry (0. 15 for the Heckman estimator and 0. 05 for the PPML estimator). It means that everything else hold constant, a country where wind power represented 10% of electric capacities three years earlier will have exports 112% 13 higher than a country where wind power represented 5% of electric capacities. The figure would be 35% for PV under the same configuration. In Table  2  we test the robustness of the results by removing China from the dataset, and testing two time periods, before and after 2003 (we only display estimations with the Heckman methodology).
2003 splits our dataset into two roughly equal sets and 9 The original categorization is made by HS 2007 codes. We used UN Stats conversion tables to extract trade data up to 1995. 10 HS 854140 also includes light-emitting diodes, unrelated to solar PV products  [32]. 11 Only exports of our country dataset are represented. 12 If we use the average of both coefficients estimations (PPML and Heckman), we have a coefficient of 0. 0665. Then the increase is 2 0. 0665 e1 ¼ 5%. 13 ¼ e 5Â0:15 À 1. also corresponds to a turning point in renewable energy technologies trade (see Figs. 1  and 2 ). Except the estimation of the elasticity of GDP o for the wind industry, results are robust when China is removed from the dataset. Further, the elasticities of GDP are much lower or insignificant after 2003, mainly for the PV industry but also to a lesser extent for the wind industry.
[64]n from the World Development Indicator database of the World Bank. G o;d;t is a vector of variables for geography. The main geographical variable is ln DIST o,d which is the natural logarithm of geographical distance weighted by population between two countries as computed by CEPII[64]. We also tested common variables used in gravity models: LANG o,d , COLONY o,d , RTA o,d,t and CONTIG o,d are dummy variables for respectively common language, past colonial relationship, regional trade agreements and contiguity (common border). These variables are also taken from the CEPII database (data end in 2006 but the historical, cultural and geographical variables will not change and we assume that RTA o,d,t is invariant thereafter 5 ). RDEMAND d;t is the demand in the destination country. We expect that an increase in demand in the destination country will lead to more exports to that country.
Data sources We obtained data from the 2796 Cochrane reviews, which correspond to all the reviews available in the Cochrane library between March 2011 and September 2014, including updates (March 2011 corresponds to the last update of the risk of bias tool 5 ). Data consisted of one XML file per review, each file containing all data entered by review authors in RevMan, the software used for managing Cochrane reviews. 20 All individual XML files were merged in a single database by using R V. 3. 2. 2  21  with the XML package. 22 The vocabulary used for risk of bias items slightly varied across reviews (eg, some reviews could refer to 'allocation concealment' as 'allocation masking'). For this reason, two authors independently evaluated all terms used and classified them according to the vocabulary of the tool.
Comparison of risk of bias assessment between reviews For each item, we compared the risk of bias assessment in terms of 'high', 'low' or 'unclear' risk of bias between the two reviews. According to the Cochrane handbook, the items blinding of outcome assessment and incomplete outcome data should be assessed for each outcome. Therefore, when the reviews reported an assessment of these items at the outcome level, we manually checked that outcomes were identical in both reviews and we retained for our analysis only the assessments that focused on the same outcomes. For blinding, we followed the last version of the Cochrane handbook and we retained only assessments of blinding of participants and personnel and blinding of outcome assessment as two independent items, excluding different types of assessment (ie, blinding as a single item, blinding of only participants or of only personnel).