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**A**: The oracle, visualized in red, is estimated from long term experimental data, i.e. joint observations of the randomized action D𝐷Ditalic_D and long term reward Y𝑌Yitalic_Y in Project STAR**B**: Our goal is to recover similar estimates without access to long term experimental data. Figure 4 shows that the oracle curve is typically decreasing: larger class sizes appear to cause lower test scores, across horizons**C**: In particular, the oracle estimates are nonlinearly decreasing, from positive counterfactual test scores (above average) to negative counterfactual test scores (below average). As the long term horizon increases, i.e. as the definition of Y𝑌Yitalic_Y corresponds to later grades, the oracle curves flatten: the effect of kindergarten class size on test scores appears to attenuate over time.
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Selection 1
**A**: More specifically, if there are more A−limit-from𝐴A-italic_A -group workers than B−limit-from𝐵B-italic_B -group workers, the firm that hires these workers must receive smaller average profit from each worker than the other firm receives from the average B−limit-from𝐵B-italic_B -group worker, so A−limit-from𝐴A-italic_A -group workers’ average wage is relatively higher. Notably, the directional effect of EPSW on the wage gap follows simply because the majority group has a larger population and, in particular, this conclusion holds regardless of the distributions of productivities of the two groups. Moreover, we also show that firm profit and the magnitude of increase in the wage gap co-move, implying that firms would benefit from selecting equilibria with larger wage gaps. **B**: We then show that EPSW moves the wage gap in favor of the majority group of workers**C**: This result follows from an equal profit condition between firms that must be satisfied in equilibrium
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Selection 1
**A**: Technology teams developing ML and AI technologies rely on outside entities to adapt, tweak, transfer, and integrate the general-purpose model**B**: Notably, the process of adapting a technology involves multiple parties**C**: This dynamic suggests a latent strategic interaction between producers of a foundational, general-purpose technology and specialists considering whether and how to adopt the technology in a particular context. Understanding this interaction is necessary to study the social, economic, and regulatory consequences of introducing the technology.
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Selection 1
**A**: Motivated by this case and several others that contradicted the ruling in Anil Kumar Gupta vs**B**: (1995), we proposed the 2SMG choice rule in Sönmez and Yenmez (2019) as a minimalist reform of the SCI-AKG choice rule.117117117As documented in Sönmez and Yenmez (2019, 2022),**C**: State of U.P
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Selection 4
**A**: Over the above trend, Integrated Sensing and Communications (ISAC) is focused on unifying the sensing operations and the communications ones and to pursue direct trade-offs between them as well as mutual performance gains (Tong and Zhu, 2021, Chap**B**: 21)**C**: ISAC will offer advantages in several case studies, such as sensing as a service, smart home and in-cabin sensing, vehicle to everything, smart manufacturing, geoscience, environmental sensing and human-computer interaction (Liu et al., 2022).
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Selection 1
**A**: Second, cryptocurrencies can be stored in infinite quantities and for indefinite periods. Therefore, cryptocurrency mining firms may not be subjected to the same market forces as in other industries.**B**: However, contrary to other industries, cryptocurrency mining firms are different in two ways. First, the exchange rate of cryptocurrencies is highly volatile**C**: The response of other industrial facilities to electricity prices has already been thoroughly discussed in the literature (see [Golmohamadi, 2022] for a recent review article). For example, in the aluminium smelting industry, [Depree et al., 2022] have discussed ‘arbitrage price,’ which identifies a correlation between electricity prices and aluminium prices
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Selection 4
**A**: One limitation of this approach is its reliance on reliable response time data, which may be challenging in crowdsourcing settings where participants’ focus can vary [45]**B**: Future work could integrate eye-tracking data into the DDM framework [39, 26, 57, 38, 76] to monitor attention and filter unreliable responses**C**: Another direction is to relax the assumption of known non-decision times by estimating them directly from data, following methods proposed by Wagenmakers et al. [67].
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Selection 2
**A**: We thus replicate the finding by Bartling \BOthers. (\APACyear2023) that a vast majority of CAs provide information to Chooser 4—even in the treatment.**B**: Model 1 in Table 7 and the preregistered analysis in Section D.4.2 in the Appendix demonstrate that not only do CAs not exploit strategic information provision, but CAs in the Plus treatment slightly exceed the degree of information provision observed in the baseline. This difference is not significant, but standard errors are very small**C**: We can test whether information is actually strategically provided. As stated in Section 2.4, if it is a given that the Chooser’s decision will be implemented, the CA’s calculus is fundamentally different then when she can combine both intervention and information provision
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Selection 2
**A**: Therefore, the scheme coordinator would find it tempting to offer less capable parties a good-performing model as long as it does not cause the more capable parties to cheat. For another, the administrable model rewards are constrained by the accuracy level of the model trained using all parties’ data or computational resources**B**: In the presence of private information, optimal contract design with models as the rewards poses unique challenges that distinguish it from its economic counterparts. For one, unlike money, models are a non-rivalrous and non-exclusive good, and can be replicated and offered to the participants at a nominal cost if not free of charge**C**: Due to incomplete information, the coordinator cannot observe the exact numbers of parties with different contribution costs in the CML scheme, and consequently cannot determine the exact accuracy level of the collectively trained model before the training completes. This makes the rewards of the contract stochastic ex-ante. The optimal contracting problem for CML needs to accommodate these challenges, whilst heeding the classical requirements of individual rationality and incentive compatibility. To this end, our paper makes the following contributions:
BCA
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Selection 3
**A**: Let i⁢(j)i𝑗\textbf{i}(j)i ( italic_j ) denote the unit to which subunit j𝑗jitalic_j belongs**B**: on Wi=(Qi,W~i)′subscript𝑊𝑖superscriptsubscript𝑄𝑖subscript~𝑊𝑖′W_{i}=\left(Q_{i},\tilde{W}_{i}\right)^{\prime}italic_W start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT = ( italic_Q start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT , over~ start_ARG italic_W end_ARG start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ) start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT**C**: Then β^^𝛽\hat{\beta}over^ start_ARG italic_β end_ARG from (6)
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Selection 3
**A**: In the United States, speech that is obscene according to “contemporary community standards” may be criminalized notwithstanding constitutional protection of free speech.333Miller v. California, 413 U.S. 15 (1973)**B**: See also Miller (2013). These standards are used in cases where absolute standards of behavior are undesirable because the standard is hard to define or expected to change over time.444Community standards are also found outside of the common law. For example, the Internal Revenue Code exempts from the prepaid interest rule points paid on the mortgage of a primary residence, provided that “such payment of points is an established business practice in the area in which such indebtedness is incurred.” 26 U.S.C. 461(g)**C**: The Statute of the International Court of Justice requires the court to apply, among other sources, “international custom, as evidence of a general practice accepted as law” when resolving international disputes. Statute of the International Court of Justice, art. 38, ¶¶\P¶ 1(b).
ACB
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ABC
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Selection 3
**A**: The estimate is neither significantly different from zero nor from -0.06. Likewise, if we define the nonlabor income elasticity as (dY/dR)(R/Y), and use the fact that Y/R is approximately 5.96 , the confidence interval for the nonlabor income effect would be (-0.371,0.459). This finding, that the income effect is estimated with a large standard error, aligns with many other results for panel data studies of taxable income. **B**: This implies a 95% confidence interval of (-0.0622, 0.0770)**C**: For λ=1.00⁢E−06𝜆1.00𝐸06\lambda=1.00E-06italic_λ = 1.00 italic_E - 06, the estimated nonlabor income elasticity is 0.0074 with a standard error of 0.0355
CAB
CBA
ABC
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Selection 2
**A**: If so, the soft intervention could be viewed as a substitute.**B**: We can test whether the proportion is identical**C**: Moreover, we can group together those CAs who intervene in any sense (hard or soft) if either provided or not provided the opportunity to intervene softly
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Selection 3
**A**: At a technical level, to perform this analysis we develop a new spectral description of the pass-through of an intervention**B**: These effects correspond to the projection of the intervention onto each eigenvector of the Slutsky matrix 𝑫𝑫\bm{D}bold_italic_D. By characterizing the pass-throughs of subsidies to prices, quantities, and welfare separately across these principal components, we are able to prove that targeting the high-eigenvalue principal components yields precisely predictable results achieving our claimed welfare properties. The spectral decomposition may be of independent interest, yielding a useful basis in which price and welfare pass-throughs of cost shocks behave intuitively despite the complexity of a system with arbitrary spillovers. **C**: That is, we diagonalize the Slutsky matrix to obtain a specific orthonormal basis in which we can express the implications of any intervention as a linear combination of orthogonal effects
ACB
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BAC
Selection 1
**A**: OLS estimates of any revascularization effects decline steadily over time, becoming negative by year five. We show below that the increasing divergence between OLS and 2SLS estimates in this figure is a consequence of increasing selection bias in an as-treated analysis.**B**: 2SLS (IV) estimates of any-revascularization effects are larger and more stable over time than ITT effects. For both SAQ outcomes, the latter fall from around four in the first wave to under two in wave 5**C**: This is consistent with the fact that the ITT estimand is diluted by a declining first stage (reported in column 3 of Table 1). 2SLS estimates of SAQ summary score effects, by contrast, are consistently close to four, while 2SLS estimates of angina gains decline from around 5.5 in the initial follow-up year to a fairly stable estimate of three in later waves
ACB
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Selection 4
**A**: In what follows, we extend the notion of admissible strategies to accommodate feedback maps.**B**: An alternative concept is that of (closed-loop) Markovian equilibrium (Dockner, , 2000, Ch. 4, Sec. 1)**C**: It is well-known that open-loop Nash equilibrium can be restrictive, as it relies on commitment and it does not incorporate the reactions of the players’ strategies to the other players’ choices
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Selection 4
**A**: Gross Domestic Product (GDPC1) relative to the preceding period.151515The series for the quarterly U.S**B**: GDP can be downloaded from: https://fred.stlouisfed.org/series/GDPC1.**C**: The target variable of the empirical analysis is the percentage change (calculated as log-difference) in the U.S
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Selection 4
**A**: Table 2 shows the places allocated for the six continents from 1998**B**: The expansion in 2026 has favoured mainly the Asian and African zones in absolute terms, while Europe and South America have benefited the least in relative terms.**C**: The distribution was fixed from 2006 until 2022, an additional spot varied due to the region of the host country
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Selection 2
**A**: There have been several blog posts advising average-score applicants to earn these extra points by choosing to be on the waitlist if they wish to be admitted to more selective centers. For example, Kanako Mishima, a working mother of two children, shares her experience in her blog post:222Source: FP Mishima, https://fpmishima.com/2020/03/05/hokatsuhtml**B**: Accessed July 4, 2024**C**: Translated using Google Translate.
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Selection 1
**A**: However, as discussed before, the actual stable points of the dynamics may not be reached in reality for various reasons**B**: While the structure of stable limit distributions changes over time due to external influences that are not included in our model, the system evolves only slowly in a complex landscape with many fixed points with unstable directions**C**: We have seen in Figures 12 and 15 that it would take hundreds of years to reach the stable distribution with model parameters fitted from today’s data.
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Selection 3
**A**: This paper presented an interpretable inflation forecasting with a Hidden Markov Model (HMM) and a Long Short-Term Memory (LSTM) fusion model with integrated gradients**B**: The fusion model provided valuable insights into the predictive capabilities to capture complex relationships within various economic variables.**C**: The study aimed to explore the effectiveness of incorporating HMM features into LSTM models for inflation prediction, leveraging the dynamic nature of economic data
ACB
ABC
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Selection 1
**A**: Number of observations used for the calculation of the cross-sectional averages are shown alongside the lines in Panel (b) and Panel (c). **B**: 3: Period averages of capital shares and Top 5% income share (a), cross-sectional average of capital shares (b) and Top 5% income share (c) by groups**C**: Fig
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Selection 4
**A**: Specifically, the data provide detailed UPC level information, including weekly price, quantity, product characteristics, and marketing mix variables, for each store in the sample. **B**: This analysis focuses on the yogurt category, using data from 95 stores located in New York market (defined by the IRI dataset) for a single week, the week of June 25 - July 1, 2012**C**: This sample selection allows us to make the sample size manageable while retaining sufficient variation in product characteristics, prices, and promotional activities
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Selection 1
**A**: In such cases, the role of government intervention becomes relevant in incentivizing and regulating enterprises’ low-carbon transformation efforts.**B**: As a result, some companies may hesitate to fully commit to low-carbon transformation, fearing the resulting financial burden and potential disruptions to their existing business models**C**: This reluctant proclivity to adopt green practices could undermine a firm’s progress towards sustainability goals and exacerbate environmental challenges
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Selection 1
**A**: The true DAG column shows the assumed ground truth, and the Variables in red are the observed variables in the MVGC. The green statements are the wrongly inferred dependency (false positives color coded as green)**B**: Table 1 summarizes our illustrations via logical examples for the three building blocks in CBN. We design the examples such that the time causality is obeyed**C**: Dependency is bi-directional. Hence, we wrote dependency statements in the causal directions. Blue texts show the correct and expected links that hold, and the missing inference occurred as a failure of the two cases passing the dependency tests.
ACB
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Selection 2
**A**: sansserif_crs , over~ start_ARG italic_c end_ARG start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT , over~ start_ARG italic_π end_ARG start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT start_POSTSUPERSCRIPT roman_com end_POSTSUPERSCRIPT ) ≠ italic_i ∥ over~ start_ARG italic_v end_ARG start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT ∥ over~ start_ARG italic_r end_ARG start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT; **B**: sansserif_FDec ( sansserif_NITC **C**: _{i}^{\rm com})\neq i\|\widetilde{v}_{i}\|\widetilde{r}_{i}sansserif_NITC
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ABC
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Selection 4
**A**: It’s important to note that there may be merit in including all voices in some capacity. The purpose of this part is not to criticize that, but to critically analyze her use of mathematical results to draw certain conclusions. Therefore, it is not reasonable—and borders on begging the question—to assume that democratic deliberation itself can weed out bad input without any further justification for this strong result. **B**: Thus, her claims like “But probabilistically, this superiority is bound to vanish over time” and that the expected cost is substantial are unfounded, and she provides no valid proof for such strong propositions**C**: However, this same uncertainty, when translated into uncertain abilities of the problem solvers, could lead to the inclusion of some problem solvers who, rather than aiding, actually obstruct us from reaching the optimal solution
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CBA
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Selection 2
**A**: For brevity reasons, we refer to the two-zone configurations computed using K-Means and Spectral Clustering as DE2 (k-means) and DE2 (spectral), respectively**B**: Similarly, the configurations with three and four zones are denoted as DE3 (spectral) and DE4 (spectral). Appendix A contains approximations of these configurations given the nodes we consider in our experiments and based on the maps provided by ACER (ACER, 2022a ). Since it is difficult to manually obtain an accurate geographic separation of the zones within the proposed configurations, the configurations from Appendix A are just approximations.**C**: The configurations proposed by ACER for Germany are specified in Annex I of ACER’s Decision on the alternative bidding zone configurations to be considered in the bidding zone review process (ACER, 2022a )
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Selection 3
**A**: First, we propose a simple regression-based method for estimating each CLATT**B**: Following Callaway and Sant’Anna (2021), we then propose a weighting scheme to construct the summary causal parameters from each CLATT. We also discuss the pre-trends tests for checking the plausibility of parallel trends assumptions in DID-IV designs.**C**: In this section, we propose a credible estimation method in staggered DID-IV designs that is robust to treatment effect heterogeneity
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Selection 4
**A**: 2**B**: This approach would better capture the evolving nature of regulatory frameworks, societal behaviors, and technological advancements.**C**: Dynamic Parameterization: Introducing time-varying parameters or adaptive calibration methods based on empirical data could enhance the realism and predictive power of the model
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Selection 2
**A**: Section 2 describes the data sources and variables used in our analysis**B**: Section 3 outlines the different steps of our nowcasting methodology and the models proposed. The results of the empirical analysis are presented in Section 4. Finally, Section 5 concludes. **C**: The remainder of the paper is organized as follows
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Selection 1
**A**: Also, we detect heterogeneous local effects induced by the selected explanatory variables on the regional market concentration. In particular, we find that variables associated with social, territorial and economic relevance of the agricultural sector seem to act differently throughout the spatial dimension, across the clusters and with respect to the pooled model, and temporal dimension. However, strong links and statistical consistency exist between the aggregate results and those reported for the southern and eastern regions.**B**: Such a concentration process is testified by increasingly values of the Gini index for the standard output throughout Europe. The SCSAR model is applied to a spatio-temporal dataset aiming at assessing the presence of local spatial spillovers of market concentration in the European regions (NUTS-2 level data) in 2010 and 2020. Empirical findings support the hypothesis of a marked fragmentation of the European agricultural market, which can be well represented by a clustering structure partitioning the whole are into three-groups, roughly approximated by a division among Western (i.e., France and the Iberian peninsula), North-Central (i.e., Mittel-European and Scandinavian regions) and South-Eastern regions**C**: The proposed methodology is then applied to investigate the structural changes that occurred in the agricultural industry in Europe in the last decade. In particular, available data on the number of active farms and their economic capacity and production (standard output) revel that between 2010 and 2020 the agricultural market suffered from a marked increase in its concentration as the number of small and micro farms decreased and the count of large holdings (i.e., with high volumes of production) increased sensibly
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Selection 2
**A**: Posted-pricing mechanisms are widely adopted in many real world applications due to their simplicity and transparency, in particular for online labor market and size-discovery mechanisms, including workup mechanisms and dark pools (Kang and Vondrák (2019)).**B**: We begin by investigating a broker who runs a posted-pricing mechanism,555This is often called a fixed-price mechanism in the literature**C**: i.e., a broker who offers a pair of fixed prices p𝑝pitalic_p and q𝑞qitalic_q to the buyer and seller, respectively
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Selection 1
**A**: Table 3 reports the efficiency, measured by the student’s average payoff, under each condition. Consistent with our hypothesis, the revealing policy results in significantly greater overall efficiency than the covering policy, irrespective of the ROL length**B**: However, the benefits of the revealing policy are not uniformly distributed among students. While students with the best or worst lottery numbers tend to benefit from this policy, students with intermediate lottery numbers tend to suffer**C**: The issue of unevenly distributed benefits is pronounced when the ROL length is one, but it is largely mitigated when the ROL length is two. Intuitively, when the possibility of mismatch is high (i.e., when the ROL length is short), prior knowledge of lottery numbers assists the most fortunate students in gaining admission to better schools while helping the least fortunate students avoid being left unmatched. However, this dynamic tends to disadvantage students in the middle, making them less likely to gain admission to top schools.
ABC
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Selection 1
**A**: 2020Q2-Q4 dropped when computing RMSE to avoid contamination due to COVID-19 outliers.**B**: AVG denotes the equal-weighted average across all states**C**: Notes: RMSE statistics x 100 for nowcasts for quarter τ𝜏\tauitalic_τ made at the end of month 1, 2, or 3 of quarter τ𝜏\tauitalic_τ and estimates for quarter τ−1𝜏1\tau-1italic_τ - 1 made at the end of month 1 and 2 of quarter τ𝜏\tauitalic_τ
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Selection 3
**A**: It is known from Arrow’s Impossibility Theorem that every voting method can exhibit at least one of a small subset of these failures**B**: We will demonstrate which failures are possible or will never occur when using the Borda count variations, which will be aided by grouping the failures into three distinct sets. **C**: We begin by defining the primary means of evaluating the fairness of election methods, which is through the observation of voting failures, sometimes referred to in the positive sense as fairness criteria
BCA
CBA
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ACB
Selection 1
**A**: This study addresses this gap by establishing a bipartite network that assesses the SDG status across Indian states and union territories and ranks them based on their contributions and current status concerning the SDGs. Using NITI Aayog’s rankings as a benchmark, the study provides a basis for comparison and highlights that while there is a positive correlation between this study’s index and the NITI Aayog rankings, the relationship remains weak. **B**: Although there are various composite indices to measure SDG attainment worldwide, there is a lack of state-level analysis in India, necessitating a complex approach to evaluate each state’s progress**C**: Concrete plans have been outlined globally to achieve the Sustainable Development Goals (SDGs) set under Agenda 2030, and India is following suit
ACB
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Selection 4
**A**: Preference towards environment include indicator variables reflecting the importance of preventing oil spills in coastal areas, spending to protect wildlife, and whether respondents consider themselves to be environmentalists or environmental activists. The attitudinal characteristics relate to respondents’ attitudes towards government programs such as whether people consider spending to be important and whether respondents consider taxes to be the appropriate payment method for protecting the environment**B**: The survey also collects data on five major characteristics of the respondents’ household which are economic, demographic, preferences and attitudes towards the environment, interest in and use of the affected natural resources, evaluations of the expected harm and prevention program, and interpretations of the payment mechanism. The economic and demographic variables include log-income of the household, whether household pays state taxes in 1994, and resides in the central coast primary sampling unit**C**: Variables reflecting interest and use of the affected natural resources are: driving along the central coast on highway 1 and familiarity with at least one of the five species of birds most often harmed by past oil spills. Variables measuring respondents’ evaluations of the expected harm and prevention program include identifying people who think oil spills over the next decade would cause more harm than mentioned in survey, who think oil spills would cause less harm, those who believe in the program’s effectiveness in achieving the desired goal, and those who have concerns regarding program’s effectiveness. The final set of variables relate to respondents’ interpretation of the payment mechanism, such as whether respondents believe the tax to not be limited to one year and those who protested that either the oil companies should pay for the program or those who thought that the companies would pass the costs to consumers in the form of higher gas and oil prices.
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Selection 1
**A**: Sections 4 and 5 provide theoretical justifications for our procedure**B**: Section 4 establishes that our test is asymptotically valid and—whenever the selection rule satisfies a condition we call improvement convergence—it is also consistent**C**: These results build on a bootstrap consistency lemma that we develop to handle two specific features of our setting: First, our test statistic is constructed using absolute values, and it is well-known that bootstrap consistency fails at points of non-differentiability (see e.g., Fang and Santos, 2019); second, the fact that we impose very few restrictions on the selection rule means that the distribution of our test statistics may vary with the sample size and are not guaranteed to “settle down” in the limit. Since (to the best of our knowledge) generally available bootstrap consistency results do not cover our exact setting, we develop a new result building on the work of Mammen (1992) (see Appendix B.2 for details).
BCA
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Selection 3
**A**: We apply the Blackwell optimality criterion to repeated games. This restricts equilibrium behavior by ruling out mixed (non-pure) strategies in general, except for particular profiles that depend on the monitoring structure**B**: Under imperfect public monitoring, they also help detection. Under private, conditionally independent monitoring, they must be part of any equilibrium that is not the repetition of the stage-game Nash equilibrium. **C**: This restriction on behavior implies bounds on equilibrium payoffs, which reflect and clarify the role that mixed strategies play under different monitoring structures. Under perfect monitoring, they are used during minmaxing
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Selection 3
**A**: Firstly, the Dutch auction is the most commonly used auction format for perishable goods in**B**: In this section, we provide further evidence for the advantages of the Istanbul Flower Auction by conducting numerical simulations for the model**C**: Although our analysis of auction characteristics confirms the advantages of the Istanbul Flower Auction over both standard auction formats, we focus on the comparison with the Dutch auction in our numerical analysis for a number of reasons
ACB
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Selection 3
**A**: This is the traditional domain of psychophysics**B**: Weber’s law has traditionally been applied to the perception of weight, loudness, and brightness, i.e., phenomena where stimulus compression occurs at the peripheral level [18, 19]**C**: More recently, the law was observed and validated on systems that are “cognitive” rather than “sensory”, e.g., the perception of temporal durations [24], or cognitive representation of number in humans and animals [25, 26]. These findings are supported both by behavioral and neurophysiological evidence [26].
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Selection 2
**A**: In Table 3, we report summary statistics for the forecast errors (defined as the realised value minus the prediction) for the AR(1) and the two benchmark predictions**B**: The forecast errors are all negative on average, implying that in this period inflation in the Euro Area has been lower than predicted by the AR(1) and the benchmarks**C**: This result is not generated by a few large negative errors, as all the median forecast errors are also negative.
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Selection 1
**A**: Extending the approach to dynamic graphs, where nodes and edges evolve over time, could further improve the model’s applicability to real-world scenarios**B**: Additionally, benchmarking the performance of this GNN architecture against state-of-the-art models on more complex datasets would provide deeper insights into its strengths and limitations. Such advancements have the potential to significantly contribute to the development of predictive tools for supply chain analytics and beyond.**C**: Future work could focus on enhancing the model by incorporating domain-specific or learned adjacency matrices to capture explicit inter-node relationships and dependencies
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Selection 4
**A**: In Table 2, we present the coverage rates of 95% confidence intervals for two resampling methods under k=0.5𝑘0.5\smash{k=0.5}italic_k = 0.5**B**: Specifically, we report results for the bias-corrected and accelerated (BCa) bootstrap(Efron (1987); Efron and Narasimhan (2020)) and the leave-one-out jackknife**C**: These outcomes provide positive evidence for resampling-based approaches as a tool for inference in this model setup.
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Selection 3
**A**: Our tribal profiles are based on articles, stories, and accounts written mostly by WEIRD people, which represent the majority of available text data – common sources included Wikipedia, National Geographic, and Nature articles. To create more authentic tribal profiles moving forward, we could use original tribal sources, such as songs and stories collected by ethnographers, or collect primary non-choice data from these tribes to use as input data for our model. This would allow us to build profiles that more accurately represent the knowledge and perspectives of the tribal groups themselves.**B**: There are constraints related to both the knowledge base we use to build the profiles and how we validate the resulting agents**C**: Our current approach to creating profiles of tribal groups and validating SCAs has some limitations
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Selection 1
**A**: The relationship between theory and empirics is a central concern in economics**B**: Critics argue that the focus on empirical identification has led to a neglect of theoretical development (Keane, 2010; Heckman, 2001)**C**: Without a solid theoretical foundation, empirical findings may lack coherence. Sims (2010) emphasizes that economics is not purely an experimental science and that theoretical models are essential for interpreting empirical results. Furthermore, Andre and Falk (2021) highlight that economists see value in research that is multidisciplinary and addresses diverse topics, suggesting a need to balance empirical rigor with theoretical and interdisciplinary approaches.
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ABC
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Selection 2
**A**: As asymmetry increases, bargaining solutions align increasingly well with simulated output, particularly equal relative gains (for both disagreement profits)**B**: However, this averaging masks an important tendency: The monopoly prediction rises steeper in asymmetry than our simulation results. That is, the monopoly solution provides predictions significantly below the actual level for the symmetric and low asymmetry cases. However, its predictions rise well above the simulation results for higher degrees of asymmetry. **C**: Interestingly, the simulation results in terms of quantity are closest, on average, to those predicted by a monopoly (joint profit maximization)
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Selection 3
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