Hasil untuk "Competition"

Jing-Chang Liang, B. Qu, P. Suganthan

Dimosthenis Karatzas, F. Shafait, S. Uchida et al.

David H. Autor, David Dorn, Gordon H. Hanson

We analyze the effect of rising Chinese import competition between 1990 and 2007 on U.S. local labor markets, exploiting cross-market variation in import exposure stemming from initial differences in industry specialization and instrumenting for U.S. imports using changes in Chinese imports by other high-income countries. Rising imports cause higher unemployment, lower labor force participation, and reduced wages in local labor markets that house import-competing manufacturing industries. In our main specification, import competition explains one-quarter of the contemporaneous aggregate decline in U.S. manufacturing employment. Transfer benefits payments for unemployment, disability, retirement, and healthcare also rise sharply in more trade-exposed labor markets.

Michael E. Hibbing, C. Fuqua, M. Parsek et al.

S. Makridakis, Evangelos Spiliotis, V. Assimakopoulos

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naive2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.

S. Nickell

J. Bain

N. Hairston, F. E. Smith, L. Slobodkin

Joel Podolny

G. Becker

M. Katz, C. Shapiro

Song-Chun Zhu, A. Yuille

Wenge Zhu, Yuanjie He

D. Hannah, K. Eisenhardt

Italo Colantone, Piero Stanig

Yuanmiao Sun, Hanbin Liao, Jiarui Wang et al.

P. Aghion, M. Dewatripont, L. Du et al.

This paper argues that sectoral policy aimed at targeting production activities to one particular sector, can enhance growth and efficiency if it is made competition-friendly. First, we develop a model in which two firms can operate either in the same (higher growth) sector or in different sectors. To escape competition, firms can either innovate vertically or dif-ferentiate by choosing a different sector from their competitor By forcing firms to operate in the same sector, sectoral policy induces them to innovate ”vertically” rather than differentiate in order to escape competition with the other firm. The model predicts that sectoral targeting enhances average growth and productivity more when competition is more intense within a sector and when competition is preserved by policy. In the second part of the paper, we test these predictions using a panel of medium and large Chinese enterprises for the period 1998 through 2007. Our empirical results suggest that if subsidies are allocated to competitive sectors (as measured by the Lerner index) or allocated in such a way as to preserve or increase competition, then the net impacts of subsidies, tax holidays, and tariffs on total factor productivity levels or growth become positive and significant. We address the potential endogeneity of targeting and competition by using variations in targeting across Chinese cities that are exogenous to the individual firm.

D. Kollias, Attila Schulc, Elnar Hajiyev et al.

The Affective Behavior Analysis in-the-wild (ABAW) 2020 Competition is the first Competition aiming at automatic analysis of the three main behavior tasks of valencearousal estimation, basic expression recognition and action unit detection. It is split into three Challenges, each one addressing a respective behavior task. For the Challenges, we provide a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one annotated for all these three tasks. In this paper, we describe this Competition, to be held in conjunction with the IEEE Conference on Face and Gesture Recognition, May 2020, in Buenos Aires, Argentina. We present the three Challenges, with the utilized Competition corpora. We outline the evaluation metrics, present both the baseline system and the top-3 performing teams’ methodologies per Challenge and finally present their obtained results. More information regarding the Competition, the leaderboard of each Challenge and details for accessing the utilized database, are provided in the Competition site: http://ibug.doc.ic.ac.uk/ resources/fg-2020-competition-affective-behavior-analysis.

D. Kollias, I. Kotsia, Elnar Hajiyev et al.

The Affective Behavior Analysis in-the-wild (ABAW2) 2021 Competition is the second Competition -following the first very successful ABAW Competition held in conjunction with IEEE Conference on Face and Gesture Recognition 2020- that aims at automatically analyzing affect. ABAW2 is split into three Challenges, each one addressing one of the three main behavior tasks of Valence-Arousal Estimation, Seven Basic Expression Classification and Twelve Action Unit Detection. All three Challenges are based on a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one to be annotated for all these three tasks.In this paper, we describe this Competition, to be held in conjunction with the International Conference on Computer Vision (ICCV) 2021. We present the three Challenges, with the utilized Competition corpora. We outline the evaluation metrics and present both the baseline systems and the top-5 performing teams’ per Challenge; finally we present the obtained results of the baseline systems and of all participating teams. More information regarding the Competition, the leaderboard of each Challenge and de-tails for accessing the utilized database, are provided in the Competition website: https://ibug.doc.ic.ac.uk/resources/iccv-2021-2nd-abaw/.

F. Hayek

It would not be easy to defend macroeconomists against the charge that for 40 or 50 years they have investigated competition primarily under assumptions which, if they were actually true, would make competition completely useless and uninteresting. If anyone actually knew everything that economic theory designated as “data,” competition would indeed be a highly wasteful method of securing adjustment to these facts. Hence it is also not surprising that some authors have concluded that we can either completely renounce the market, or that its outcomes are to be considered at most a first step toward creating a social product that we can then manipulate, correct, or redistribute in any way we please. Others, who apparently have taken their notion of competition exclusively from modern textbooks, have concluded that such competition does not exist at all. By contrast, it is useful to recall that wherever we make use of competition, this can only be justified by our not knowing the essential circumstances that determine the behavior of the competitors. In sporting events, examinations, the awarding of government contracts, or the bestowal of prizes for poems, not to mention science, it would be patently absurd to sponsor a contest if we knew in advance who the winner would be. Therefore, as the title of this lecture suggests, I wish now to consider competition systematically as a procedure for discovering facts which, if the procedure did not exist, would remain unknown or at least would not be used.