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<|MaskedSetence|> <|MaskedSetence|> A higher value of b𝑏bitalic_b corresponds to a lower effective dimension, better control of the variance of our estimator, and hence a faster rate. <|MaskedSetence|> The empirical eigenvalues are simple to compute, so it is simple to validate this assumption with a diagnostic plot. Figure 2 verifies polynomial decay of the empirical eigenvalues in the real world application of Section 6; the Project STAR data have a low effective dimension as required by Assumptions 5.2 and 5.3.222Specifically, we divide each empirical eigenvalue by the trace of the corresponding matrix, to convey the fraction of variation explained.
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**A**: (3)
The eigenvalues decay at least polynomially.
**B**: Any bounded kernel satisfies (3) [Fischer and Steinwart, 2020, Lemma 10].
**C**: The limit b→∞→𝑏b\rightarrow\inftyitalic_b → ∞ gives an RKHS with finite dimension [Caponnetto and De Vito, 2007].
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Therefore, a key force in our model is firms’ equilibrium behavior to segregate their workforce by group identity. Indeed, we show empirically that the Chilean EPSW leads to an increase in gender segregation across firms. One may suspect that such segregation is less likely to occur in other localities that enact EPSW.999For example, recent research shows that gender-based occupational segregation may be especially likely when the local language has gendered nouns, as firms can target their hiring to workers of specific genders (Kuhn et al., 2020; Kuhn and Shen, 2023; Card et al., 2024). <|MaskedSetence|> <|MaskedSetence|> Our search model includes this feature, by allowing workers to direct their search based on the segregation status of firms. <|MaskedSetence|> Therefore, it seems plausible that EPSW could further affect segregation in a wide variety of labor markets.
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**A**: Speaking to this point, however, group-based segregation across firms has been noted in the US (Blau, 1977; Neumark et al., 1996; Hellerstein and Neumark, 2008; Goldin, 1990), and recent research (Ferguson and Koning, 2018) argues that this segregation has increased over time.
**B**: This may explain the high baseline level of gender segregation in Chile, where Spanish is the official language.
**C**: Gendered nouns and targeted hiring may also play a role in the ability of Chilean firms to further segregate once EPSW is enacted.
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Approaches to fine-tuning. <|MaskedSetence|> As a result, a variety of broad and flexible base models have been developed (‘pretrained’) for downstream adaptation to particular tasks. These include large language models (Brown et al., 2020; Howard and Ruder, 2018; Dai and Le, 2015) and visual models (Radford et al., 2021; Yuan et al., 2021). Fine-tuning is an approach where new data and training methods are applied to a pretrained base model to improve performance on a domain-specific task (Dodge et al., 2020). Fine-tuning often consists of several steps: (1) gathering, processing and labeling domain-specific data, (2) choosing and adjusting the base model’s architecture (including number of layers (Wang et al., 2017) and parameters (Sanh et al., 2020)) and the appropriate objective function (Gunel et al., 2020), (3) Updating the model parameters using techniques like gradient descent, and (4) evaluating the resulting model and refining if necessary.
Economic models of general-purpose technology production. Several lines of work in growth economics address the development and diffusion of general-purpose technologies (or GPTs). Bresnahan (2010) provides a general survey of this concept. <|MaskedSetence|> Scholars have examined the effects of factors such as knowledge accumulation, entrepreneurial activity, network effects, and sectoral interactions on the creation of GPTs (Helpman, 1998). <|MaskedSetence|>
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**A**: New applications of ML often involve leveraging an existing model to a specific task, in a process known as transfer learning (Zhuang et al., 2020).
**B**: Jovanovic and Rousseau (2005) offers a historic account of technologies such as electricity and information technology as GPTs with major impacts on the United States economy.
**C**: The model presented here abstracts away the forces giving rise to the invention of general-purpose technologies, and instead focuses on the later-stage decision of when (or at what performance level) to release the GPT to market for domain-specialization.
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<|MaskedSetence|> State of U.P. (2020),123123123As the basis for my opinion, see Mamta Bisht vs. <|MaskedSetence|> <|MaskedSetence|> as an economist, I cannot assert that this point is clear-cut under reservation jurisprudence.
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**A**: State of Uttaranchal (2005) at the Uttarakhand High Court,
where the plaintiff successfully challenged an individual’s assignment in the open category indirectly.
**B**: She argued that a third party who had been awarded an SC position should instead be assigned the open category position,
thereby allowing the SC position to be reassigned to the plaintiff.
**C**: While, in my opinion, such a challenge aligns with Saurav Yadav vs.
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Griffiths, 2007). Low cost is mentioned as one of the most interesting features that would push the replacement of traditional active radars by passive radars (Judice
et al., 2023). <|MaskedSetence|> (2016); Wang
et al. <|MaskedSetence|> (2019), where the optimal placement of passive radars to achieve belt barrier coverage is sought after; however, the cost of the radar systems involved is considered fixed and is not a decision variable. <|MaskedSetence|>
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**A**: A similar optimization task, where the placement of fixed cost devices is considered for WiFi-based passive bistatic radars, is investigated in Ivashko.
**B**: (2015); Xu
et al.
**C**: Examples of economics-aware approaches to the design of passive radar systems are shown in Chang
et al.
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2.2.3 Price responses
If we ignore a few price peaks, historically in the ERCOT market—as shown in Fig. 2(a,b)—day-ahead prices are statistically higher than real-time prices and have a comparatively narrower standard deviation. This implies that day-ahead prices remain elevated for longer periods. Therefore, cryptocurrency miners’ response to day-ahead prices will be stronger than their response to real-time prices, especially during the summer. Prices tend to be statistically lower at night, suggesting that cryptocurrency miners may not be incentivized to respond to either day-ahead or real-time prices during both summer and non-summer months during late-night hours. During the summer, prices remain higher than during non-summer months, as shown in Fig. 7. <|MaskedSetence|> <|MaskedSetence|> 6. <|MaskedSetence|> The correlation coefficient for day-ahead prices during non-summer times increases to -0.29 (p-value 0.00), and during summer times to -0.42 (p-value 0.00). However, selecting a narrower window for real-time prices did not significantly increase the correlation coefficients.
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**A**: We observe that cryptocurrency miners respond more vigorously to both day-ahead and real-time prices during the summer months.
**B**: While not shown for brevity, cryptocurrency miners respond further vigorously during peak demand hours (3 PM-7 PM).
**C**: These price-responsive behaviors are depicted in Fig.
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<|MaskedSetence|> Unlike explicit feedback, response time is unobtrusive and effortless to measure [17], offering valuable information that complements binary choices [16, 2]. For instance, consider an online retailer that repeatedly presents users with a binary query, whether to purchase or skip a recommended product [35]. <|MaskedSetence|> <|MaskedSetence|> Response time can help overcome this limitation. Psychological research shows an inverse relationship between response time and preference strength [17]: users who strongly prefer to skip a product tend to do so quickly, while longer response times can indicate weaker preferences. Thus, even when choices appear similar, response time can uncover subtle differences in preference strength, helping to accelerate preference learning.
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**A**: Since most users skip products most of the time [33], the probability of skipping becomes nearly 1 for most items.
**B**:
In this paper, we propose leveraging implicit human feedback, specifically response times, to provide additional insights into preference strength.
**C**: This lack of variation in choices makes it difficult to assess how much a user likes or dislikes any specific product, limiting the system’s ability to accurately infer their preferences.
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<|MaskedSetence|> Indeed, Bartling \BOthers. (\APACyear2023) found experimentally that a vast majority of CAs transmit true information to Choosers. However, information can play a strategic role for self-interested policymakers.
First, if (a lack of) information leads to Chooser mistakes in the CA’s subjectively preferred direction, information may be withheld from Choosers. Corollary 1 illustrates this point formally. <|MaskedSetence|> <|MaskedSetence|>
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**A**:
It has long been recognized in economics (Blackwell, \APACyear1953) that an expected-utility decision-maker can better his position by relying on more accurate information.
**B**: By not correcting these misconceptions, policymakers engage in “paternalism by omission.”
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**C**: This argument hints at why policymakers do not correct the widespread pessimistic misconceptions about health risks to smoking (Viscusi, \APACyear1990): these misconceptions go in policymakers’ subjectively preferred direction (fewer people smoke).
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Prior to our work, there has been a line of research that resorts to contract theory to address the incentive issue in collaborative machine learning (Kang et al. <|MaskedSetence|> 2023), but most of them focus on using money as the reward for the collaboration. Karimireddy, Guo, and Jordan (2022) attends to the administration of models with different accuracy levels as rewards, while their primary focus is on the case where the scheme coordinator can directly observe each party’s data collection costs. However, in reality, the cost of contribution is typically private information known only to the contributing party. For instance, consider a CML scheme where private computing firms pull together their GPUs for the training of a language model for code generation. Each firm could face a different vendor price and incur dissimilar maintenance cost of the chips. <|MaskedSetence|> To gather the data, each firm needs to recruit analysts, the overheads of which are usually determined by conditions of the local labor market and the firm’s own incentive policies. The differences in the operating environments cause the parties of a CML scheme to have a wide range of per-unit contribution costs. While the scheme coordinator can be an expert in the domain field, thereby possessing some general information about the process, it remains challenging for them to gauge the exact costs borne by the parties. <|MaskedSetence|> Worse still, a rent-seeking party may cheat by misreporting their cost if it leads to higher profits being gained from the scheme. This information asymmetry results in what is known as a principal-agency problem in economic literature (see Mas-Colell, Whinston, and Green 1995; Laffont and Martimort 2002; Bolton and Dewatripont 2004 for a comprehensive treatment of the subject).
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**A**: As another example, consider the CML scheme where investment firms join their privately curated data for the training of an investment model.
**B**: 2019; Ding, Fang, and Huang 2020; Karimireddy, Guo, and Jordan 2022; Liu et al.
**C**: Even if the parties willingly inform the coordinator of their costs, the coordinator cannot verify the truthfulness of these reports without incurring significant auditing expenses.
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The second reason is that some parties may have unequal bargaining power or unequal legal rights.
For example, consider the case of treaty negotiations.
In principle, all states are equal.222222“The Organization is based on the principle of the sovereign equality of all its Members.” U.N. Charter art. 2, para. 1.
Nonetheless, this principle has exceptions,232323The UN security council is a notable case, as it has five permanent members (United States, United Kingdom, France, Russia, and China) with veto powers of security council decisions. U.N. <|MaskedSetence|> 23, para 1; art. <|MaskedSetence|> <|MaskedSetence|> and even countries with equal rights may agree to a non-anonymous rule due to the secure the participation of a more powerful state in treaty negotiations.
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**A**: 27, para 3.
**B**: Charter art.
**C**: However, the security council does not negotiate treaties.
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The study of behavioral responses to tax changes has long been a central area of economic research. Historically, much of the focus was on labor supply, with the primary question being how labor supply responds to tax reforms. In a series of influential papers, Feldstein (1995, 1999) argued that individuals respond to tax changes on multiple margins beyond hours worked. These include exerting more effort in current employment, switching to higher-paying jobs that require more effort, or relocating geographically to better-paying positions. Other margins include choosing different compensation mixes (e.g., cash vs. <|MaskedSetence|> <|MaskedSetence|> Feldstein’s empirical work found a taxable income elasticity of 3. Following Feldstein’s work, a large body of literature emerged, producing a wide range of elasticity estimates, from -1.3 (Goolsbee, 1999) to 3 (Feldstein, 1995). <|MaskedSetence|> Subsequent studies by Weber (2014), Burns and Ziliak (2017), and Kumar and Liang (2020) have reported higher elasticity estimates ranging from 0.6 to 1.4. While these conventional elasticity estimates are useful for understanding how taxable income responds to marginal changes in a linear budget constraint, they are less effective in predicting the impact of tax reforms on taxable income. In reality, tax systems are non-linear, and reforms often result in changes to both kink points and marginal tax rates across different income brackets.
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**A**: More recent studies, including Saez (2003), Gruber and Saez (2002), Kopczuk (2005), and Giertz (2007), produced estimates closer to 0.5, with Saez, Slemrod, and Giertz (2012) providing a comprehensive review of the literature up to 2012.
**B**: By estimating how taxable income responds to changes in the marginal net-of-tax rate, one can capture a broader set of these relevant margins.
**C**: fringe benefits) and engaging in tax avoidance or evasion.
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Second, we recognize that waiting periods and caps are two rules that, in practice, may be deployed simultaneously. For example, political scientists and environmental economists have studied “policy mixes” (e.g., Bouma \BOthers., \APACyear2019). <|MaskedSetence|> <|MaskedSetence|> Our experiment contains one treatment to test for the relationship between the waiting period and the cap. <|MaskedSetence|> This is to test for substitution between rules: do those CAs who implement a waiting period relax the cap? If deliberation is thought to move behavior in a preferred direction, hard-nosed restrictions may become less attractive if CAs generally respect autonomous decision-making.
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**A**: By employing multiple instruments in a single policy area, policymakers’ goals are thought to be attained more effectively.
**B**: In this treatment, CAs can implement both the cap and a waiting period.
**C**: This term is also used by macroeconomists to refer to the joint application of monetary and fiscal policy.
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<|MaskedSetence|> In a marketplace with numerous and changing goods, the demand system is high-dimensional and a priori unstructured. <|MaskedSetence|> <|MaskedSetence|> In particular, the authority will have nothing close to a precise estimate of the entire demand system. This raises the question is whether there are policies that robustly improve surplus despite this uncertainty.
Our main result is that if demand satisfies a property that we call recoverable structure, then there are feasible intervention rules that robustly (i.e., with high probability) increase equilibrium total surplus despite large errors in observing every detail of the system. Moreover, within a natural class of interventions—those that do not reduce consumer surplus222This constraint is natural for a platform that could lose customers to other marketplaces or a government that can lose political support.—our feasible interventions achieve the largest gain in surplus that is possible for a given level of subsidy expenditure. Hence, within this class, these interventions are as good as those that could be designed by an authority with perfect information..
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**A**: As any firm’s cost changes, the number of potential effects to consider is equal to the number of products; thus, the number of interactions quadratically in this number.
**B**: Realistic signals will leave substantial uncertainty about many aspects of the structure of the game among the firms (see Section 7.1 for a detailed discussion).
**C**:
What makes the problem challenging is that, once we broaden our perspective beyond one traditionally defined market (e.g., smartphones) and consider spillovers to a variety of other complements and substitutes, there is a kind of curse of dimensionality.
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Notes: This table compares 2SLS and OLS estimates of the effects of 1-5 years of revascularization exposure computed using equation (6), stacking data from all waves. Columns 3 and 6 report Hausman [1978]-type t-tests for the difference between 2SLS and OLS estimates, where standard errors are computed using the variance of the difference of estimates. <|MaskedSetence|> <|MaskedSetence|> Estimates were computed with controls for baseline angina frequency scores and enrollment regions. Standard errors, clustered on person, are reported in parentheses. <|MaskedSetence|>
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**A**: P-values for joint tests appear in brackets in the last row.
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**B**: Chi-square statistics at the bottom of the table test 2SLS-OLS joint equality.
**C**: This statistic has a χ2(5)superscript𝜒25\chi^{2}(5)italic_χ start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT ( 5 ) distribution under the null.
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<|MaskedSetence|> Following the general approach in Nordhaus and Boyer, (2003) and Nordhaus, (2018), the model accounts for climate damages created by economic activity.444See, for example, Weyant, (2017) and the references therein for a review and Traeger, (2023) for a more recent discussion. <|MaskedSetence|> We will impose a utility damage specification that is linear in the stock of greenhouse gas emissions (GHG), which will be the key state variable in the model. In order to investigate strategic interactions, we introduce a two-country extension, which we interpret as a stylized model of interactions between a “global north” and a “global south.” We incorporate a general nonlinear abatement technology that allows for a reduction of GHG in the atmosphere. This captures, for example, investing in reforestation, as well as in carbon-capture technologies that can directly affect the GHG stock. We then investigate the role of a variety of transfer schemes, including technological transfers. We assume that input use in production creates a flow of GHG emissions, and the accumulated GHG emissions damage each country’s payoff function. <|MaskedSetence|> Each country chooses an abatement effort towards reducing the stock of
GHG emissions. As with the climate sensitivity parameter, the abatement technology can capture reforestation efforts, carbon capture and storage systems, etc. We model the interactions between North and South through transfers and standard nonlinear catching-up equations. We consider different transfers between the two countries, including transfers that can improve the abatement technology. The model can accommodate several kinds of heterogeneity, including in preferences, time discount rates, and damages resulting from the stock of accumulated GHG.
When applied to this framework, our solution method allows us to reduce the computation of the Nash equilibria of the dynamic game to the solution of temporary games indexed by time. Open-loop Nash equilibria computed by our method are also MPE. The uniqueness of MPE in the class of affine feedbacks is also discussed. To obtain comparisons between equilibrium outcomes and the efficient frontier, we distinguish between a social planner problem without country sovereignty constraints, where a “global planner” can relocate production from one country to another, as well as the more realistic case of a “restricted planner,” who is subject to a resource constraint for each country. In the special case of logarithmic utility, linear production function, and a non-linear abatement function, we derive various comparisons between the equilibrium and the efficient values of variables of interest, such as consumption, abatement effort, and transfers between the two countries. We then use a numerical example to illustrate the role of heterogeneity in time-discounting and climate vulnerability between the two countries, as well as the role of the intertemporal elasticity of substitution. Finally, we demonstrate how the ITM can be applied in a robust control framework; see, for example, Hansen and Sargent, (2008), as well as in a game-theoretic framework in order to investigate the effects of uncertainty on various non-cooperative equilibrium outcomes. We find that when the marginal abatement efficiency gains are small relative to the marginal emissions created by production, it is not efficient to subsidize abatement in the global south. Under logarithmic payoffs we find that in the Nash equilibrium there is over-consumption both in the global north and (provided that technological differences between the two are not too large) in the global south. The global south receives lower abatement-technology transfers and under-invests in abatement relative to the social optimum. Both global emissions and welfare are lower as a result. Our numerical example points to some interesting implications of heterogeneous climate vulnerability. If the global south is more vulnerable to climate-related damages, then the.
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**A**: A country-specific climate sensitivity parameter is used to capture factors that can make it more vulnerable to climate change due to, for example, geography, or the ability to engage in adaptation.
**B**: More precisely, we build on the simplified version of Nordhaus’s three-reservoir model developed in Golosov et al., (2014).
**C**: We illustrate the ITM in the context of a dynamic analytical integrated assessment model.
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To summarise, choosing a fairer, more transparent slot allocation method would be a socially responsible obligation of FIFA. Hopefully, at least some berths will be determined by well-defined rules in the future. <|MaskedSetence|> As mentioned by Krumer and Moreno-Ternero, (2023), the most straightforward case seems to be the European club competitions organised by the Union of European Football Associations (UEFA). Here, the number of slots provided for each association depends on the UEFA country ranking, the average number of points collected by the participating clubs over the last five seasons (Csató, 2022b, ). However, the interaction of these international contests and the national leagues (Güner and Hamidi Sahneh,, 2023; Rappai and Fűrész,, 2024) can justify different allocation rules. <|MaskedSetence|> <|MaskedSetence|> Note that this consideration is much less relevant for national teams as they cannot increase their strength by buying high-ability players on the market..
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**A**: The recent improvement of the FIFA World Ranking (FIFA,, 2018), which has eliminated the main weaknesses of the previous formula (Cea et al.,, 2020; Csató,, 2021; Kaminski,, 2022; Lasek et al.,, 2016), shows that FIFA is open to suggestions from the academic community.
The proposed approach of rating sets of teams based on historical matches between them can be used in other contexts than the FIFA World Cup.
**B**: Since improving competitive balance is an important aim of the UEFA (Gyimesi,, 2024), it would be unfavourable to apply a strictly performance-based rule without any compensation mechanism.
**C**: For instance, there is robust evidence that the substantial income from the UEFA club competitions reduces the intensity of competition in domestic leagues and leads to the dominance of some teams over the long term (Pawlowski et al.,, 2010; Peeters,, 2011).
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Welfare analysis reveals that waitlist priority benefits applicants who arrive early to the market at age 0, especially those with lower initial scores, by increasing their chances of admission in subsequent periods. However, it disadvantages applicants who arrive lately at age 1, whose utility declines due to heightened competition from waitlisted cohorts. These findings demonstrate that waitlist priority, while designed to support unassigned applicants, acts as a redistributive mechanism, favoring early starters at the expense of late starters..
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**A**: Conversely, for age 1 and age 2 applicants, reduced priority shifts cutoff distributions significantly, lowering the barriers to admission.
**B**: For age 0 applicants, reduced waitlist priority leads to more daycare centers having cutoffs rather than being under-enrolled, but with little shift in their distribution’s peak.
**C**: Given the high cardinality of the choice set, I propose a heuristic algorithm to approximate the optimal pair of ROLs for a given preference profile.
Given my structural estimates I simulate a counterfactual scenario in which no additional priority is granted for being waitlisted.
The counterfactual experiments highlight the redistributive effects of waitlist prioritization in daycare admissions, showing how changes in priority impact cutoff distributions, welfare outcomes, and application behaviors.
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Figure 10 (a) shows these RoR for all agents in the Pareto case, but corresponding plots for the other simulations look essentially the same. For the bottom 10%, we oberserve an enormous variance of the RoR. Whereas many agents had no capital returns at all, some others won one or two steps of the process by luck, leading to large RoRs due to their low level of wealth. This also explains the observable stratified shape of the wealth-RoR-plot for the bottom 20%percent2020\%20 %. For the broad middle class in our simulation, we observe a moderate variance of RoR and only a slight dependence on wealth. Hence, increasing returns mainly concern the rich. <|MaskedSetence|> A significant increase of RoR can only be detected for the top 1% of the agents. <|MaskedSetence|> Using data from Norway, [18] empirically investigates the dependence of return rates and wealth, which reveals a similar shape of the wealth-return-curve. <|MaskedSetence|> Almost constant returns for the majority of the population and strongly increasing return rates for the top induce the two-tailed wealth distribution mentioned in the introduction.
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**A**: Figure 10 (b) shows the RoR of the top 10% in detail.
**B**: This is consistent with the observation from Figure 8 that the wealth distribution of the bottom 99% is almost equivalent to the ”scaled wages”-distribution.
**C**: Moreover, they emphasize that this shape is persistent in time apart from extreme events like the financial crisis, where even decreasing returns could be observed.
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<|MaskedSetence|> Since RNNs only consider the most recent state, “the cellular state of LSTM determines which states should be left behind and which states should be forgotten. <|MaskedSetence|> <|MaskedSetence|> specifically LSTMs, they noticed that the LSTM model outperformed traditional econometric approaches such as ARIMA, MA, vanilla neural networks, etc. Some of the drawbacks they mention for RNNs are, “there are many parameters that need to be trained, which are prone to gradient dissipation or gradient explosion; [and they are] without feature learning ability” (Zhang et al., 2022). The authors highlight the superiority of LSTMs over traditional econometric methods, despite noted challenges like parameter tuning and feature explainability.
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**A**: Hence, LSTM plays an important role in many fields of economic research” (Zhang et al., 2022).
**B**: With the rise in deep learning, the authors explore various architectures and determine which models are preferred and successful in economics.
**C**: In a list of papers that utilize RNNs.
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<|MaskedSetence|> <|MaskedSetence|> Both approaches also provide significant evidence of dispersion in the random coefficients for these variables. Additionally, well-known brands, such as Chobani, Fage Total, and Stonyfield Organic Oikos, exhibit relatively larger brand fixed effects in the consumer utility function. <|MaskedSetence|> This difference is primarily driven by the dispersion in the random coefficients on price, which captures heterogeneity in consumer sensitivity to price changes.
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**A**: 5.1.3 Estimation Results
The estimation results for the preference parameters (β𝛽\betaitalic_β and ΣΣ\Sigmaroman_Σ) are presented in Table 4.
**B**: The estimated slope coefficients for price and the organic indicator in the proposed approach have reasonable signs and magnitudes, aligning closely with the results from the BLP approach.
**C**: Finally, the random coefficient model (either Bayesian or BLP estimates) implies a more elastic demand compared to the model without random coefficients, as indicated by the last two rows of the table.
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<|MaskedSetence|> While embracing low-carbon transformation holds the promise of environmental sustainability and enhanced competitiveness, it also entails a substantial amount of financial investments and operational adjustments. <|MaskedSetence|> These costs may include investments in renewable energy infrastructure, efficiency upgrades, and emission reduction measures, all of which require a substantial amount of capital expenditure and major operational restructuring in order to embark on the transition path.
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. This reluctant proclivity to adopt green practices could undermine a firm’s progress towards sustainability goals and exacerbate environmental challenges. <|MaskedSetence|>
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**A**: For many enterprises, particularly those operating in carbon-intensive industries, the costs associated with transitioning to cleaner technologies and sustainable practices can be daunting.
**B**:
The transition towards a low-carbon economy presents a complex challenge for enterprises, acting as a double-edged sword with respect to both arising opportunities and costs.
**C**: In such cases, the role of government intervention becomes relevant in incentivizing and regulating enterprises’ low-carbon transformation efforts..
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4 Conclusions
In this work, we presented a new method of estimating Granger causality to solve one of its significant criticisms of not being causal but rather a predictive tool. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> However, we present a method to enhance the GC inferred connectivity matrix to be considered fully causal. Our simulation experiments indicated that this method is efficient with its ability to unravel cycles in structures.
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**A**: Taking the logical-and operation on both BVGC and MVGC results will solve the criticism of GC being only predictive.
**B**: A notable point, however, is that this framework has not solved the latent confounder problems of GC.
**C**: Our approach leveraged the causal Bayesian network and interpreted GC as conditional independence tests.
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𝖵𝖢.𝖼𝗋𝗌←$𝖵𝖢.𝖦𝖾𝗇(1λ)formulae-sequence𝖵𝖢𝖼𝗋𝗌currency-dollar←𝖵𝖢𝖦𝖾𝗇superscript1𝜆{\sf VC}.{{\sf crs}}{\overset{\$}{\leftarrow}}{\sf VC}.{\sf Gen}(1^{\lambda})sansserif_VC . sansserif_crs over$ start_ARG ← end_ARG sansserif_VC . <|MaskedSetence|> sansserif_crs over$ start_ARG ← end_ARG sansserif_AoK . <|MaskedSetence|> sansserif_crs, 𝖵𝖢.𝖼𝗋𝗌formulae-sequence𝖵𝖢𝖼𝗋𝗌{\sf VC}.{{\sf crs}}sansserif_VC . <|MaskedSetence|> sansserif_crs.
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**A**: sansserif_crs, and 𝖠𝗈𝖪.𝖼𝗋𝗌formulae-sequence𝖠𝗈𝖪𝖼𝗋𝗌{\sf AoK}.{{\sf crs}}sansserif_AoK .
**B**: sansserif_Gen ( 1 start_POSTSUPERSCRIPT italic_λ end_POSTSUPERSCRIPT ),
and
𝖠𝗈𝖪.𝖼𝗋𝗌←$𝖠𝗈𝖪.𝖦𝖾𝗇(1λ)formulae-sequence𝖠𝗈𝖪𝖼𝗋𝗌currency-dollar←𝖠𝗈𝖪𝖦𝖾𝗇superscript1𝜆{\sf AoK}.{{\sf crs}}{\overset{\$}{\leftarrow}}{\sf AoK}.{\sf Gen}(1^{\lambda})sansserif_AoK .
**C**: sansserif_Gen ( 1 start_POSTSUPERSCRIPT italic_λ end_POSTSUPERSCRIPT ).
Publish 𝖭𝖨𝖳𝖢.𝖼𝗋𝗌formulae-sequence𝖭𝖨𝖳𝖢𝖼𝗋𝗌{\sf NITC}.{{\sf crs}}sansserif_NITC .
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Selection 1
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<|MaskedSetence|> These include the portrayal of the theorem as an unnecessary mathematical formalization of a simple fact, effectively a mere reiteration of the hypothesis, overlooking certain assumptions, and the application of mathematical principles in ways that may not fully align with their desirable use in sociopolitical contexts. <|MaskedSetence|> Through the use of mathematical formalism, aspects that might be considered basic are portrayed with a level of complexity that suggests deeper insights, potentially leading to a misunderstanding of the theorem’s implications. <|MaskedSetence|> Despite its wide recognition and numerous citations, the Hong-Page theorem, as well as the related “Diversity Prediction Theorem,” should be critically evaluated for their contributions to the understanding of collective decision-making. This careful scrutiny is also applicable to Page’s book [Pag08], where the foundational claims merit a reexamination in light of these considerations. From181818Quotes are included here to demonstrate how the main contributions, according to the authors (hence the use of quotes), of their respective books are significantly impacted by the analysis presented in this paper, essentially indicating that their validity is considerably compromised. the preface:
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**A**: Our thorough analysis of the Hong-Page Theorems has revealed several critical issues.
**B**: It is crucial to approach the application of mathematics with careful consideration and precision, particularly when it informs the basis for decisions with significant societal impact.
**C**: We have concluded that what the theorem requires is not “diversity”, but the existence of a more “able” problem solver who can improve upon areas where others fall short.
The application of mathematics by Hong and Page in their theorem presents its findings in a manner that may hide the straightforward nature of its conclusions.
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ACB
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ACB
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ACB
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Selection 4
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We want to understand if the clustering algorithms compute the same clusters when nodal prices of different time periods are considered. <|MaskedSetence|> <|MaskedSetence|> If we recompute the clusters and take only prices into account666This means that we do not include constraints in the clustering computation such that we obtain balanced clusters in terms of either the number of nodes or geographical coverage., we can reduce the price standard deviation within clusters further. However, the resulting clusters are not of equal size and are not geographically coherent. <|MaskedSetence|> Depending on the time frame taken into account, different clusterings would result.
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**A**: How different are clusters resulting from different clustering algorithms? Also, how similar are the clusters of a particular configuration with respect to prices, and how large are the price standard deviations within clusters?
We find that the configurations proposed by ACER are not stable across time and the algorithm used.
**B**: Moreover, the configurations do not reduce the price standard deviations within zones considerably, and the average prices between zones are similar.
**C**: Importantly, neither the configurations proposed by ACER nor the clusters that we computed are stable across time.
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ABC
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ABC
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ABC
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ABC
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Selection 3
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6.1 Setting
Oreopoulos (2006) estimates returns to schooling using a major education reform in the UK that increased the years of compulsory schooling from 14 to 15. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The data are a sample of individuals in Britain and Northern Ireland, who were aged 14141414 between 1936193619361936 and 1965196519651965, constructed from combining the series of U.K. General Household Surveys between 1984198419841984 and 2006200620062006; see Oreopoulos (2006), Oreopoulos (2008) for details.
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**A**: Due to the imprecision of their standard errors, Oreopoulos (2006) then moves to “a difference-in-differences and instrumental-variables analysis by combining the two sets of U.K.
**B**: data”.
**C**: Specifically, Oreopoulos (2006) exploits variation resulting from the different timing of implementation of school reforms between Britain (England, Scotland, and Wales) and Northern Ireland as an instrument for education attainment: the school-leaving age increased in Britain in 1947, but was not implemented until 1957 in Northern Ireland.191919In the first part of his analysis, Oreopoulos (2006) adopts regression discontinuity designs (RDD) and analyzes the data sets in Britain and Northern Ireland separately.
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ACB
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CAB
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CAB
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Selection 3
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Traditional policy interventions like congestion pricing have been implemented to reduce traffic congestion but possess inherent limitations. <|MaskedSetence|> <|MaskedSetence|> While cities like London, Stockholm, and
Singapore have reported significant traffic reductions when congestion pricing is paired with public transport improvements, these policies can have unintended consequences. Traffic may shift to non-charged periods or alternative routes outside the charging zone, and wealthier drivers may continue to use the roads while lower-income individuals face restricted access. <|MaskedSetence|>
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**A**: Cheng et al.
**B**: The success of congestion pricing largely depends on complementary measures, such as enhanced public transit options, to sustain long-term benefits [1].
In response to these challenges, AI technologies have emerged as.
**C**: [1] discuss how congestion pricing, which involves charging drivers for road usage during peak times, aims to decrease the number of vehicles on the road and alleviate congestion.
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ACB
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ACB
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ACB
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ACB
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Selection 2
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<|MaskedSetence|> Figure 5 displays the predicted densities for weeks 5, 24, and 48, and compare these predictions with the realized values of the target variable. Following Adrian et al. <|MaskedSetence|> Specific weeks are chosen to examine how predictions for the current year improve as more information becomes available over the calendar. Results are presented for the AR-W-M-Q model and focus on California, Texas, and New York, states that annually report the highest CO2 emissions records.
Our analysis indicates that in most cases, the actual values of CO2 emissions growth (represented by black dots) fall within the range of the predicted densities. Improvements in predictive accuracy across the calendar are evident, particularly if we compare density predictions in week 5 versus week 24. As additional data become available, the median of the predicted density moves closer to the actual observed value, providing a visual confirmation of the decreasing CRPS highlighted earlier in this paper. The performance of our approach exhibits considerable variability across different states and years. <|MaskedSetence|> In contrast, for California, the model achieves better results between 2013 and 2015.
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**A**: (2019), full continuous conditional densities are constructed by fitting a generalized skewed Student’s distribution to the discrete conditional quantiles predicted at each nowcasting point.
**B**: For Texas and New York, the model demonstrates relatively strong predictive accuracy between 2014 and 2017.
**C**: This section illustrates the behavior of our timely quantile predictions of per-capita CO2 emissions growth throughout the calendar year.
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ABC
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Selection 4
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First, we consider the effect of spatial autocorrelation, that is, the ρ𝜌\rhoitalic_ρ coefficient. For the pooled model we find it to be weakly significant in both 2010 and 2020, meaning that the presence of neighbors with high level of market concentration is associated with higher concentration levels in Europe. <|MaskedSetence|> <|MaskedSetence|> Therefore, the pooled model provides an averaged-like evidence and does not highlight such a difference for the clusters.
In terms of covariates impact, we also highlight that the clusters are characterized by different relationships with the dependent variable. Table 3 shows the estimates of the coefficients in the pooled regression and clusterwise for the two years, allowing for a direct comparison across space and time. Generally speaking, the coefficients suggest a prominent spatial heterogeneity across the European regions, but also a marked temporal dynamic, leading to an overall favourable attitude toward the clustered strategy. Indeed, in many cases, the coefficients are clearly unstable regarding the magnitudes and the signs (i.e., for several variables, both pooling and groupwise, the signs move from positive to negative or the other way around). A glaring example of this situation is the regional wealth. <|MaskedSetence|> A close pattern is found only for for Cluster 3 (i.e., France and Spain), in which the coefficients are strongly significant across time but the sign moves from negative to positive.
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**A**: Globally, we find that the estimated coefficients are poorly statistically significant and that regional wealth was negatively associated with market concentration in 2010, meaning that richer regions were associated with lower market concentration, becoming positive (but not significant) in recent years.
**B**: However, while analyzing the effects at the cluster level, we find our that this spatial effect is statistically significant for Cluster 1 (i.e., Scandinavia and Eastern Europe, Balkans and Italy) but not for the other two clusters.
**C**: Moreover, considering the results for 2020, we notice a stronger spatial autocorrelation effect for the Cluster 1, which grow from 1% up to 2%.
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BCA
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BAC
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BCA
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BCA
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Selection 1
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<|MaskedSetence|> subject to the constraints of IC, IR, and BB, and even subject to the loosened notion of Bayes-Nash IC (BNIC), IR, and weakly budget-balancedness (WBB).
Correspondingly, a long line of work (McAfee, 2008; Blumrosen and Dobzinski, 2014, 2016; Blumrosen and Mizrahi, 2016) has studied the best possible approximate efficiency, in particular for the notion of gains-from-trade (GFT),222Gains-from-trade is defined by the expected marginal increase of the social welfare. Thus, it is typically harder to approximate than social welfare. We refer to Section 2 for more details. <|MaskedSetence|> <|MaskedSetence|>
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**A**: (2022)..
**B**: with respect to those desideratum.
It was a long-standing open problem whether a constant-factor approximation to the first-best GFT was possible, until it was recently answered in the affirmative by Deng et al.
**C**:
The seminal result by Myerson and Satterthwaite (1983), however, reveals that it is impossible to achieve ex-post efficiency111A mechanism is ex-post efficient if it achieves the optimal ex-post social welfare, i.e., if trade occurs whenever the buyer’s valuation is at least the seller’s valuation.
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CBA
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CBA
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Selection 1
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It is noteworthy that during the preparation of this paper, NYC adopted the revealing policy for the 2022-2023 season. Initially, NYC refused to reveal lottery numbers to parents. However, following a parent-led campaign under the New York State’s Freedom of Information Law, NYC first agreed to reveal lotteries upon request after admissions and finally decided to disclose lotteries before applications. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Using a crowdsourced survey, Marian (2023) estimates school cutoffs and demonstrates their positive impact on reducing the unmatch rate in subsequent years among the survey participants.
Related literature
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**A**: For instance, it still withholds historical cutoff data.
**B**: The empirical implications of this policy shift represent an exciting area for future research.
**C**: NYC continues to progress toward providing more comprehensive information about lotteries.
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Selection 4
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<|MaskedSetence|> Section 2 sets out the proposed cross-state MF-VAR model and explains the Bayesian estimator. <|MaskedSetence|> We illustrate the use of these new data for state business cycle analysis and the study of state connectedness. Then we show how the MF-VAR model can be used to produce accurate nowcasts of state GDP that are available at least three months ahead of BEA data. The production and dissemination of timely higher-frequency estimates of state GDP will be useful for decision makers tracking the evolution of state economies. <|MaskedSetence|> Online appendices comprise a data Appendix (Appendix A.1), a technical Appendix (Appendix A.2) detailing the estimation algorithm, and an empirical Appendix (Appendix A.3) containing supplementary empirical results.
2 Econometric Methods.
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**A**: Section 4 concludes.
**B**: The plan for the remainder of this paper is as follows.
**C**: Section 3 considers the application that uses the MF-VAR to produce historical monthly estimates of state GDP from 1964 through 2024.
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CBA
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BCA
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BCA
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BCA
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Selection 3
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A primary criticism of the Borda count is its susceptibility to majority winner failures, but note how rare these failures actually are. BCU and MBC’s 1.3%percent\%% correspond to just 3 elections with majority winner failures, with ABC exhibiting this failure only once. Meanwhile, EBC and QBC never have this failure, although Example 3.1 showed one is theoretically possible. <|MaskedSetence|> BCU, on the other hand, is the only variation with majority and Condorcet loser failures, all of which occurred in two-candidate elections as in the previously described 2018 San Leandro election.
When comparing the manipulation failure rates, the BCU percentage for truncation failures illustrates the obvious incentive voters have with this method to submit ballots that only include their top choice, known as bullet voting. Nearly half of the elections using this variation are vulnerable to truncation failures, with the three variations featuring the averaged approach for partial ballots performing much better. <|MaskedSetence|> <|MaskedSetence|> The lower truncation failure rate for ABC compared to BCU mirrors the computation results by Kamwa (2022), which were referred to in that paper as the averaged and pessimistic models, respectively..
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**A**: This occurs since truncating a ballot in those cases is a zero-sum strategy, meaning that your favored candidate will gain in points compared to other highly ranked candidates, but will instead lose ground over the lowest ranked candidates.
**B**: BCU would instead cause your top choice to gain an advantage over all candidates who are removed from the ballot.
**C**: These two methods also avoid majority loser failures and exhibit the lowest rates of Condorcet winner failures, clearly showing the strongest results within the verifiable failure category.
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CAB
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CAB
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ACB
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CAB
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Selection 1
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COVID-19 Pandemic: The global pandemic, which began in early 2020, had far-reaching consequences on health, economies, education, and social systems. It led to widespread job losses, disruptions in healthcare services, school closures, and increased poverty. The economic setbacks caused by COVID-19 reversed years of progress, particularly on goals related to health (SDG 3), poverty (SDG 1), and education (SDG 4).
War in Ukraine: The ongoing conflict between Russia and Ukraine, which began in early 2022, has created major disruptions in global food and energy supply chains. Ukraine and Russia are significant producers of grains, fertilizers, and energy. <|MaskedSetence|> Climate change has impacted efforts to achieve goals related to clean water (SDG 6), climate action (SDG 13), and life on land and below water (SDGs 14 and 15).
Global Economic Slowdowns: Various economic crises, driven by the pandemic, inflation, energy price shocks, and geopolitical tensions, have led to slower global growth and deepened inequalities. This has hindered progress in reducing inequality (SDG 10), eradicating poverty (SDG 1), and ensuring decent work and economic growth (SDG 8) [15]. <|MaskedSetence|> As a result, policymakers have shifted their focus from the long- and medium-term goals of the SDGs to addressing immediate, pressing challenges. <|MaskedSetence|>
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**A**: The war has caused food insecurity, higher energy prices, and economic instability worldwide, negatively affecting progress on SDGs related to food security (SDG 2), energy (SDG 7), and peace (SDG 16).
Climate Change: The accelerating impacts of climate change, including extreme weather events such as floods, droughts, and wildfires, have worsened environmental degradation and increased vulnerability for many communities.
**B**: The need for fundamental SDG cooperation and clean energy has become more critical than ever in the current global context [17, 18].
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**C**: These crises have significantly affected the advancement of member countries [16].
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ACB
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ACB
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ABC
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ACB
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Selection 1
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<|MaskedSetence|> Standard errors for the linear SRA estimator are between 0.3%-3% smaller than those for linear PRA estimates. <|MaskedSetence|> <|MaskedSetence|> In this application, using pooled logistic regression produces the most efficiency gains over the usual SM estimator, also known as ABERS estimator introduced by Ayer
et al. (1955) in contingent valuation studies–see the online appendix for reference.
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**A**: Except in one case, nonlinear SRA standard errors are between 0.5%-1.5% smaller than for the linear SRA ones.
**B**: Finally, nonlinear PRA standard errors are between 0%-5% smaller than those for the nonlinear SRA estimates.
**C**: In terms of the standard errors, we see a ranking among the linear RA estimators.
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Selection 4
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To do this, we formalize the property of fairness-improvability as a null hypothesis. We propose a test for this null, and show that (under suitable conditions) it is valid and consistent. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> (2020), Wasserman et al. (2020), and Ritzwoller and Romano (2023), among others).
In Section 5, we provide a theoretical argument that repeated sample-splitting makes our test more robust to potential manipulation by the analyst. This section is related to papers that draw conclusions about optimal research procedures based on models of a strategic researcher (Andrews and Kasy, 2019; Frankel and Kasy, 2022; Andrews and Shapiro, 2021; Kitagawa and Vu, 2023; Spiess, 2024). In particular, our focus on selective reporting of a p𝑝pitalic_p-value is similar to the concern with p𝑝pitalic_p-hacking in Jagadeesan and Viviano (2024) and Kasy and Spiess (2024). Different from these papers, we focus specifically on the question of whether manipulation is reduced when p𝑝pitalic_p-values are aggregated over repeated sample splits. That is, our model does not broadly study the optimal statistical procedure, but rather provides a theoretical justification for the common use of repeated sample-splitting, which is not always justified by more standard econometric properties (e.g., power)..
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**A**: Our test involves sample-splitting, an approach with a long history in statistics going back at least to the work of Moran (1973) and Cox (1975).
**B**: (2009), DiCiccio et al.
**C**: To mitigate the uncertainty introduced by sample splitting, we further recommend that the analyst performs repeated sample-splitting (as in Meinshausen et al.
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ACB
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ACB
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ACB
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Selection 3
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<|MaskedSetence|> The construction involves the same ingredients as the proof of FM, and can be achieved using a variation of simple strategies (Abreu, 1988). Not too surprisingly, this folk theorem can be extended to imperfect public monitoring, in the special case in which monitoring satisfies product structure, individual full rank, and a public randomization device is available.777Admittedly, the product structure is very special, but it applies to important classes of games, such as games with one-sided imperfect monitoring, e.g. principal-agent games, and games with adverse selection and independent types.
In general, under imperfect public monitoring, it is known that unpredictable behavior serves another purpose. Mixed actions enlarge the set of detectable deviations, and hence affect the sufficient conditions under which the folk theorem usually holds (Fudenberg, Levine, and Maskin, 1994, hereafter FLM). Yet, the impossibility of fine-tuning continuation payoffs in order to compensate players for mixing in a way that would be independent of the discount rate further restricts the action profiles that can be implemented. Absent a public randomization device, only stage-game Nash equilibria and pure action profiles can be played in a (perfect public) Blackwell equilibrium (generically, see Proposition 2). <|MaskedSetence|> <|MaskedSetence|>
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**A**: This is because, unless the action profile is a Nash equilibrium of the stage game, the continuation play must depend on the realized signal, which makes it impossible for players to be indifferent over multiple actions (as they generically induce distinct distributions over public signals), even if they are myopically indifferent over those..
**B**: That is, the only mixed actions that can be played are stage-game Nash equilibria: it no longer suffices that players be myopically indifferent over the support of their mixed action.
**C**:
We show that this is the only adjustment that must be made to the “standard” statement of the folk theorem – indeed, mixed actions play no other role in the usual proofs under perfect monitoring.
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CBA
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CBA
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CBA
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Selection 2
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Recent studies have recognized the importance of auction speed in the design of mechanisms for real-world markets. <|MaskedSetence|> <|MaskedSetence|> (2005). Andersson and Erlanson (2013) numerically show that a hybrid Vickrey-English-Dutch algorithm is faster than the Vickrey-English or Vickrey-Dutch auctions. The role of speed in auctions has also been highlighted in experimental studies, where participants’ impatience was linked to their enjoyment of participation (Cox et al., 1983) or intrinsic costs of time (Katok and Kwasnica, 2008).
However, none of these models formally considers “time discounting” in values due to auction duration, except for Hafalir et al. <|MaskedSetence|>
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**A**: Banks et al.
**B**: (2003) document the trade-off between efficiency and speed in the Federal Communication Commission spectrum auctions, where speed and revenue can be enhanced through the improved design proposed by Kwasnica et al.
**C**: (2023), which predates our work..
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ABC
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ABC
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ABC
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ABC
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Selection 3
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<|MaskedSetence|> The main problem is that direct experiments are lacking on the validity of Weber’s law in the perception of utility changes. It is despite the close attention already paid to Weber’s law in economics [27, 28, 29, 30, 20, 31, 21, 32]. <|MaskedSetence|> Hence we hope that our results will motivate direct experiments for checking Weber’s law in economic experiments. <|MaskedSetence|> Also, such setups can be instrumental in suggesting generalizations of Weber’s law.
Acknowledgements
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**A**: Another interesting open problem is multi-player bargaining setups, where Weber’s law can be employed to find new solutions.
**B**: We close by mentioning pertinent open problems.
**C**: As illustrated by our analysis of the ultimatum game, Weber’s law can be used to study two-player bargaining, and more generally partially cooperative behavior.
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BCA
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Selection 4
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Finally, we also present the autocorrelation structure and the ADF tests for the forecast errors (we estimated the model with the intercept, with lags selected by means of the BIC). <|MaskedSetence|> Despite this fact, the ADF test fails to reject the null hypothesis in all the cases, except for the one period horizon. We interpret this as a situation of low power of the ADF test, and therefore as evidence of persistence, but possibly not a unit root. We verify this interpretation by
analysing the properties of the realised forecast losses reported in Table 4. The average realised losses associated to the AR(1) forecast are lower, at least for forecasts up to six quarters, and less dispersed than the ones of the two benchmarks, so they are, in this sense, more precise. Moreover, the losses from the AR(1) predictions are not very correlated for short forecasting horizons. As we increase the forecasting horizon the dependence increases, but the autocorrelations still decay reasonably quickly. On the other hand, the two benchmarks display large and persistent autocorrelations in their realised forecast losses at all forecasting horizons. <|MaskedSetence|> <|MaskedSetence|>
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**A**: We further investigate the dependence in the realised losses using the ADF test: the difference in the persistence that we observed in the sample autocorrelations of the realised losses is confirmed by the outcome of the ADF test, where the unit root hypothesis is rejected only for the forecasts from the AR(1) model (and only for short horizons).
Overall, these results suggest that the AR(1) model should be more precise for short-term forecasts, but this superiority could be masked empirically by the excessive dependence in the benchmarks.
**B**: On the other hand, the AR(1) does not seem to produce more precise forecasts than the benchmarks at longer horizons, and the outcomes of the unit roots tests should be interpreted as a warning that any potential statistical significant difference may be spurious..
**C**: Especially at the lowest horizons, the autocorrelations of the errors from the AR(1) forecasts declines fairly quickly, in comparison with the autocorrelations of the benchmarks.
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ACB
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CAB
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Selection 4
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This study demonstrates the potential of Graph Neural Networks (GNNs) in addressing critical challenges in supply chain analytics, particularly in demand forecasting for individual nodes. By employing a model based on fully connected layers and leveraging temporal features, the GNN effectively captures meaningful patterns within time-series data, even in the absence of explicit inter-node connections. <|MaskedSetence|> This approach provides a robust framework for handling datasets with fixed node counts and time-dependent characteristics.
The findings highlight the scalability and flexibility of the proposed GNN architecture. <|MaskedSetence|> <|MaskedSetence|> These results establish a strong foundation for exploring more advanced GNN methodologies tailored to supply chain management tasks..
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**A**: The identity matrix as the adjacency matrix simplifies computations while enabling the model to focus on self-looped transformations, which are crucial for extracting node-specific temporal insights.
**B**: Its ability to adapt to diverse graph structures makes it suitable for a wide range of applications, including anomaly detection, resource optimization, and production planning.
**C**: Despite the use of a simplified adjacency matrix, the model effectively learns non-linear relationships, demonstrating its capability to uncover hidden dependencies in complex supply chain systems.
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ABC
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ABC
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ABC
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ABC
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Selection 1
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This paper’s first contribution is to demonstrate that zero-covariance need not be imposed for structural parameter identification when one can rely on higher-order orthogonality conditions. <|MaskedSetence|> This new argument is simpler than most previous approaches and lends itself naturally to a straightforward sample-analogue estimator. <|MaskedSetence|> In Section 6, we establish that the sample-analogue estimator derived from our identification argument is both consistent and asymptotically normal. We also show how the identification framework substantially reduces complexity in deriving asymptotic properties and in implementing the estimation algorithm. <|MaskedSetence|>
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**A**: In Section 5, we show that under a diagonal higher-cumulant assumption, the identification problem reduces to an eigenvector problem.
**B**: Section 7 then evaluates the estimator’s finite-sample performance in simulations, and we conclude by illustrating its practical usefulness through two empirical applications.
.
**C**: Moreover, even in contexts such as VAR models—where relaxing uncorrelatedness is perhaps less critical—our framework still provides a transparent way to test for uncorrelatedness within the same orthogonal higher-cumulant setting used in earlier work.
Our second contribution is to propose a conceptually and computationally simple estimator with desirable statistical properties.
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ACB
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BAC
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ACB
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ACB
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Selection 3
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Finally, for the endowment effect, we took a different approach. <|MaskedSetence|> Indeed, in one of their experiments, Apicella et al. [Apicella2014] placed endowed/exchange items on the ground in front of the Hadza for them to see and choose. <|MaskedSetence|> First, we generated two items as objects via text-based chat. <|MaskedSetence|> See Appendix A.2.3. Our interactive platform can be accessed through this link:https://multimodal-egame.streamlit.app/, and code and documentation can be found through our GitHub repository.555https://tinyurl.com/4khsxdky
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**A**: Next, we tested for the endowment effect by displaying images of the two items to the chatbot, thereby integrating both textual and visual elements into the experimental framework.
**B**: In line with this, when developing the endowment effect experiment with SCAs, we employed a two-step multimodal design.
**C**: As implemented in the field with tribesmen, the endowment effect has a visual component.
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CBA
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CBA
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CBA
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CBA
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Selection 1
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2.3 Matching Variables to Standardized Economic Concepts
To facilitate systematic network analysis and aggregation, we standardized the free-text descriptions of the source and sink variables by mapping them to official Journal of Economic Literature (JEL) codes. <|MaskedSetence|> <|MaskedSetence|> By generating vector embeddings of the variable descriptions and comparing them to the vector embeddings of JEL code descriptions using cosine similarity, we identified the most relevant codes for each variable.222222Fetzer et al. <|MaskedSetence|> This process situates each causal claim within the broader context of economic research and allows us to construct a knowledge graph of economic research, mapping and documenting the frontier in causal evidence over time. This process is visually summarized in Figure 2, which illustrates our AI-driven approach to analyzing and mapping causal linkages between JEL codes. Full details are available in Appendix Section B.
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**A**: We created semantic embeddings for each JEL code’s overall description, which concatenates the JEL description, guidelines, and keywords.212121The JEL guidelines (available at https://www.aeaweb.org/jel/guide/jel.php) provide detailed descriptions of each code and are typically a paragraph long.
**B**: Including keywords enhances the semantic specificity of the JEL codes.
**C**: (2024) employs a similar vector embeddings methodology in comparing descriptions of product descriptions with Harmonized System (HS) codes to allow for a structured production network that can be mapped to existing economic data.
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ABC
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ABC
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ABC
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ACB
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Selection 2
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<|MaskedSetence|> The same is true for the alternating monopoly. <|MaskedSetence|> Thus, these bargaining solutions do not only provide a best fit in terms of the overall (level) prediction accuracy but also a reasonably good fit for the relative change.
For total profits, equal relative gains and Kalai-Smorodinsky perfectly capture the comparative statics of asymmetry perfectly. <|MaskedSetence|> However, the good fit is masked by slight over- and underestimation of consumer surplus and total profits, respectively. In particular, the Nash prediction underestimates the effect on consumer surplus and overestimates the impact on total profits, while it is the opposite for the bargaining solutions.
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**A**: Similarly, the effect of asymmetry on total welfare is captured extremely well by the Nash prediction, as well as the bargaining concepts of equal relative gains and Kalai-Smorodinsky (with Nash).
**B**: Note that we designed the simulations so that the total quantities under Nash remain constant across all degrees of (a)symmetry, resulting in a horizontal line at value 1111 in Figure 2, panel (a), for the Nash equilibrium.
**C**: Again, the trajectory of the simulation results is predicted best by equal relative gains and Kalai-Smorodinsky (with Nash deviation profits).
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BCA
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ACB
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BCA
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BCA
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Selection 3
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