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**A**:
Under these conditions, we arrive at our main result: uniform consistency of long term dose response curves**B**: This result appears to be the first rate in supsupremum\suproman_sup norm for a nonlinear, long term dose response**C**: It accommodates general types of short term rewards, actions, and contexts.
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**A**: Columns II-VII present results on the gender wage gap from alternative empirical specifications and alternative sample selections. We discuss these specifications further in Section C.3. Across all specifications, our findings of an increase in the gender wage gap in male majority LLMs, and a decrease in the gender wage gap in female majority LLMs, are statistically significant at conventional levels.**B**:
Table 4 presents our estimates on the effect of EPSW on the gender wage gap. Column I presents our baseline results from (11)**C**: We find that EPSW increases the gender wage gap (in favor of men) by 4.27 percentage points in male majority LLMs, but decreases the gender wage gap (in favor of women) by 6.24 percentage points in female majority LLMs. For reference, men typically outearn women prior to EPSW: the wage gap (controlling for human capital factors, as in (11)) is 8.63% (SE=0.0058) in male-majority LLMs and 0.71% (SE=0.0118) in female-majority LLMs
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**A**: If bargaining is conceptualized as splitting a pie, one player prefers to cede some portion of the pie if it means the entire pie grows to a size that justifies profit-sharing. This phenomenon arises in real-world settings**B**: For instance, Apple allows third party developers to build software on iPhones. Opening up the tasks of application development to third parties improves consumer experience such that consumers are willing to purchase apps or other capabilities within apps. This additional revenue is then shared between Apple and the developer, leaving Apple with higher profits and a better product. Revenue sharing arises, often, because doing so is lucrative.**C**:
The first notable take-away is that players do not necessarily opt to maximize their own proportion of the profit. Even if one player has full control over the bargaining solution, depending on the relative cost of production, they may benefit from a profit-sharing agreement in order to encourage investment by the other player
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**A**: The HR policy is implemented as soft reserves**B**: This means that while members of the protected group receive preferential treatment for HR-protected positions,
these positions are not exclusively set aside for them**C**: Any HR-protected position that remains after all members of the protected category have received a position can then be awarded to other eligible individuals.
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**A**: The tradeoff between the resources that the operator assigns to the S&C functionalities is analyzed from the point of view of the service prices, quantities and profits.**B**:
The present work frames the physical tradeoff between the S&C functionalities within an economic setting, which will contribute to fill a research gap that the authors have identified, as detailed below**C**: We model a service provision by an operator to the users, the utility of which is derived from both S&C functionalities
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**A**:
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**B**: 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.**C**: However, contrary to other industries, cryptocurrency mining firms are different in two ways. First, the exchange rate of cryptocurrencies is highly volatile
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Selection 4
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**A**: Simulations using three real-world datasets [57, 16, 39] consistently show that incorporating response times significantly reduces identification errors, compared to traditional methods that rely solely on choices. To the best of our knowledge, this is the first work to integrate response times into bandits (and RL).
**B**: We specifically integrated our estimator into the Generalized Successive Elimination algorithm [3] for fixed-budget best-arm identification [29, 34]**C**: Our linear-regression-based estimator integrates seamlessly into algorithms for preference-based bandits with linear human utility functions [3, 31], enabling interactive learning systems to leverage response times for faster learning
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**A**:
In the absence of hypothetical full-information choices, no information about utilities is available**B**: Thus, this theory cannot be tested. It is not necessary at this point to delve into the manifold issues with utilitarianism and utilitarian calculation in general (e.g., Kolm, \APACyear1993).**C**: Furthermore, no method for eliciting a prediction of utilities is known
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**A**: Even if a party wishes to choose the option designed for another type that requires less data contribution, they can longer do so as the coordinator can embed the type into the option and easily verify a party’s eligibility at the time of contract signing. Since models are freely replicable, granting the highest rewards to parties incentivizes them to make the highest possible level of contribution while satisfying the IR condition.
**B**: The key is that the full observability of a party’s cost eliminates the possibility of cheating**C**: The detailed proof is deferred to Appendix A, and we provide here a brief intuition on offering the best model to all
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**A**: To calculate differenced outcomes for each decade, we estimate all
measures for the year 2000 based on both schemes.323232Since the 2000 census industry question was based on the NAICS industry**B**: detailed than 10–11 industries used by Fortin et al**C**: (2021, 2022)
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**A**: A strict version of the responsiveness axiom would also characterize the endpoint rules when combined with strategyproofness and anonymity**B**: The endpoint rules are characterized by strategyproofness, anonymity, and translation equivariance.212121Translation equivariance is presented as a simple example of an axiom that eliminates fully bounded and half-bounded intervals; however, it is not the only such axiom**C**: I thank Jianrong Tian for this observation.
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**A**: The data used in this study come from the Panel Study of Income Dynamics (PSID), covering the years between 1977 and 1997**B**: individuals and their households. To ensure consistency across time, we used the version of the PSID data provided by the Cross-National Equivalent File (PSID-CNEF), which harmonizes key variables across survey waves.
**C**: The PSID is a longitudinal survey that began in 1968 and follows a representative sample of U.S
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**A**: National Science Foundation through TESS: Data collected by Time-sharing Experiments for the Social Sciences, NSF Grant 0818839, Jeremy Freese and James Druckman, Principal Investigators. The author has no competing interests to declare that are relevant to the content of this article.
**B**: In-kind support was also received from the U.S**C**: Financial support was received from the Center for Social and Economic Behavior (C-SEB) at the University of Cologne and the German Research Foundation (DFG) under Germany’s Excellence Strategy, EXC 2126/1–390838866
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**A**: A natural question is whether they are also achieved in expectation (since, in principle, realizations with very negative surplus could occur with low probability)**B**: In our setting, it turns out that all the analysis would be unaffected if we added good ex ante expected performance to the definition of robustness.222222This is because the surplus pass-throughs are supported on [0,1]01[0,1][ 0 , 1 ] and all quantities in the proofs are bounded. Thus, convergence in probability is equivalent to convergence in L1superscript𝐿1L^{1}italic_L start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT, and so our proofs extend to show close approximations to the omniscient benchmark in terms of ex ante surplus.
**C**: Our notion of ϵitalic-ϵ\epsilonitalic_ϵ–robustness means that these surplus properties are achieved ex post with high probability
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**A**: The ISCHEMIA trial randomized 5,179 patients with moderate to severe cardiac ischemia to one of two care strategies**B**: Conservative-arm patients were meant to receive medical therapy alone, with possible invasive treatment when medical therapy was deemed inadequate [Maron et al., 2020; Spertus et al., 2020b].
**C**: Patients assigned to the invasive treatment arm were meant to undergo diagnostic coronary angiography and subsequent revascularization when feasible (through PCI or CABG) while also receiving medical therapy
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**A**:
Confirm, through a verification theorem, that v𝑣vitalic_v is indeed the value function, and that the corresponding candidate optimal control is indeed optimal**B**: Once completed, it provides sufficient conditions for optimality. Under suitable conditions, e.g. uniqueness of solutions to the HJB equation, these conditions are also necessary.**C**: This procedure passes through a transversality condition on v𝑣vitalic_v for t→∞→𝑡t\to\inftyitalic_t → ∞ and can be fairly complex when state constraints are present
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**A**: In other words, in the context of system theory, what do we think about the impulse response function of the target variable?
It is worth noting that both the MLP and the 1D CNN build around the assumption of fixed finite support for their target variable, in our case, GDP growth.141414The support of a real-valued function f(.)f(.)italic_f ( **B**: ) is the subset of the function domain containing the elements which are not mapped to zero**C**: In our case, finite support means that the impulse response is bounded in time.
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**A**: This is probably due to the high number of competitive nations in Europe even without England and Germany, which is amplified by the seeding of Argentina and Brazil, the best South American teams. Then—between Seeded sets S1 and S2—the share of UEFA slightly decreases as even more successful nations are placed in the seeded set.
**B**: First (changing from Seeded set S0 to S1), the slots of UEFA grow substantially**C**: On the other hand, the results are not that straightforward if the number of seeded nations increases
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**A**: This behavior is driven by the potential benefits of being waitlisted, specifically the increased probability of admission to more selective daycare centers. These findings support the claim that applicants respond to dynamic incentives, providing evidence against alternative explanations such as declining outside option values or updated preferences.
**B**: I focused on the Japanese daycare system and demonstrated the prevalence of manipulation arising from the additional priority granted to waitlisted applicants**C**: I showed that many waitlisted applicants avoid listing safety options in their initial applications, only to include them when reapplying
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**A**: the return rates on capital depend on the amount of capital a person or household owns. More casually spoken: the richer you are, the faster your wealth grows. Detailed theoretical and empirical information on this phenomenon can be found e.g**B**: in [1, 19, 18, 2, 17]. Diverse explanations for increasing returns can be conceived, like lower risk aversion of the rich due to higher risk-bearing potential. Empirical studies in [18] confirm this idea, but on the other hand increasing returns can even be found within similar asset groups. Hence, risk is not the exclusive driver of increasing returns and other factors like higher skills, political influence, informational advantages, decreasing costs of debt, tax-evasion or lower transaction costs are relevant, too. Moreover, some asset classes are barely available for ordinary people, like private equity fonds, art or other value increasing luxury goods.
**C**: Simple models assuming independent return rates (like the Black-Scholes model) can only account for exponential-tailed wealth distributions, but numerous more complex models have been proposed to find the determinants of the power-law tail, some of which can be found in [19, 21, 4, 15, 7, 39, 25, 5, 13, 26, 40, 6, 27] and references therein. The early model proposed in [36], which was recently taken up in [24], already contains the idea of combining independent and reinforced elements. An essential reason for the power-law tail has been found in so-called increasing returns, i.e
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**A**: Their architectures can be extended and optimized with variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), which improve the model’s ability to retain and utilize information over longer sequences.**B**:
RNNs introduce recurrent connections, allowing information to persist and be shared across different time steps in sequential data, where temporal dependencies are crucial**C**: These models are able to handle variable-length sequences, allowing them to adapt dynamically to different lengths of input data
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**A**: A formal analysis of this claim will be the topic of the next section where we apply a Shapley decomposition of the model predictions.
**B**: 3, where both capital share and inequality seem to remain stable from 2000 onward, we can argue that the fluctuations in inequality are likely driven by a combination of factors, such as rising capital income inequality, decreasing labor income inequality, and the increase in the transmission coefficient, which converts fluctuations in capital share into overall income inequality**C**: Interpreting these results alongside the income inequality and capital share dynamics from Fig
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**A**: However, such agreement is not guaranteed in general; in cases where the two approaches diverge, it becomes essential to assess which underlying assumption - sparsity or the validity of IVs - is more plausible in the specific context.**B**: We emphasize that our Bayesian shrinkage approach does not rely on IVs, and the agreement between the two approaches here validates the BLP results that rely on IVs**C**:
Overall, our Bayesian approach produces similar results to the BLP approach in this case
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**A**: This assumption allows us to isolate the decision-making process regarding the resource allocation and emphasize the role of the Transition Investment Ratio in balancing financial performance and sustainability objectives without introducing additional complexities. Future work could build on this framework by incorporating functional relationships between these parameters—such as production costs and selling prices—and the Transition Investment Ratio.
**B**: However, in the current framework, we assume both the production costs and the selling prices to be constant for simplicity**C**: As previously mentioned, undergoing a low-carbon transition and investing in this transition can decrease production costs and increase selling prices due to improved operational efficiency, stronger brand value, and an alignment with consumer preferences
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**A**:
The main contribution of this work is the endowment of the Granger causality framework with causal reasoning solely on theoretical groundings. This revision birthed an algorithm**B**: Section 3 built on the intuitions from Section 2, and interpreted GC from CBN perspectives as conditional (in)dependence tests, presented our proposed fix as an algorithm and presented results on simulation data. Finally, section 4 concludes the paper.**C**: The paper’s outline is as follows: Section 1 introduces causal discovery and its trends over the years. Section 2 overviews CSL in observational datasets and formalises Granger’s causality and Reichenbach’s common cause principles (RCCPs) and causal Bayesian networks (CBNs)
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**A**: sansserif_crs over$ start_ARG ← end_ARG sansserif_VC **B**: sansserif_Gen ( 1 start_POSTSUPERSCRIPT italic_λ end_POSTSUPERSCRIPT ),
and**C**: 𝖵𝖢.𝖼𝗋𝗌←$𝖵𝖢.𝖦𝖾𝗇(1λ)formulae-sequence𝖵𝖢𝖼𝗋𝗌currency-dollar←𝖵𝖢𝖦𝖾𝗇superscript1𝜆{\sf VC}.{{\sf crs}}{\overset{\$}{\leftarrow}}{\sf VC}.{\sf Gen}(1^{\lambda})sansserif_VC
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**A**: Essentially, it uses mathematics that complicate a simple fact rather than mathematics that simplify complex relations.**B**: This approach makes the theorem, about diversity and social intelligence, inaccessible to individuals outside the mathematical field**C**:
The Hong-Page theorem is, in essence, a misuse of mathematics in the following sense: it employs probability techniques, such as the Borel-Cantelli lemma (as my simpler proof in Appendix A demonstrates, unnecessarily), to prove claims unrelated to diversity
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**A**: The evolution of cluster price standard deviations across the weeks in the scenario years 1989, 1995 and 2009 is illustrated in Appendix B.1**B**: This is an implicit objective of clustering nodes based on prices.**C**: We also observe the price standard deviations corresponding to a single price zone or cluster (Figure 8).
Clearly, the average cluster price standard deviations decrease as we consider configurations with 2, 3 or 4 clusters
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**A**: For instance, suppose that some units already receive the treatment in period 00 before the policy shock that distributes the treatment in period 1111. In this setting, the canonical DID design is infeasible in practice unless these units are discarded ex ante from the data**B**: By contrast, our DID-IV design can be directly applied to this setting, allowing us to identify the LATET. In other words, we can view that DID-IV is DID with noncompliance of the assigned treatment.**C**: Overall, this paper provides a new econometric framework by combining the IV techniques with DID designs.
Our DID-IV design can be applied to various empirical settings. First, it can cope with the general adoption process of the treatment, which the canonical DID designs cannot, when we are interested in the effect of a treatment on an outcome
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**A**: Without sufficient regulatory frameworks and public investments in AI technology, urban mobility systems stagnate, and congestion remains a critical issue**B**: Policymakers must avoid complacency by developing a clear AI strategy.**C**:
Policy Failure Risk: This scenario highlights the risks of inaction
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**A**: 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**B**: For Texas and New York, the model demonstrates relatively strong predictive accuracy between 2014 and 2017. In contrast, for California, the model achieves better results between 2013 and 2015.
**C**: 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
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**A**:
Recall that the EU adopts an administrative classification known as the Nomenclature of territorial units for statistics classification or NUTS [40], which subdivides country members into a multi-layer hierarchical structure having as the lower stratum the local are units or municipalities (LAUs), in turn uniquely nested within provinces (NUTS-3), regions (NUTS-2), macro-regions (NUTS-1), and countries (NUTS-0). In the present work, we use the regional 2010 NUTS-2 classification as it allowed for the largest spatial and temporal coverage of the agricultural industry data**B**: Notice that, despite more recent classification are available, we adopted the NUTS 2010 nomenclature as it is the only one fully covering the 27 countries currently belonging to the European Union within the 2010-2020 period. Also, we preserve only the European regions having complete (i.e., non-missing) information for the entire time span and we removed from the analysis all the non-continental regions and the islands**C**: According to the former, we exclude from the analysis all the extra-European administrative units, such as Guyane, Martinique, and Réunion for France or Ciudad de Melilla for Spain; while according to the latter, we removed data on counties that are islands, that is, Cyprus, Ireland and Malta. We made this choice to have a specific focus on continental Europe. Indeed, while extra continental units would be clustered together due to the dominance of the geographical distance with respect to the features’ distance, the other three major islands would introduce the issue of dealing with spatial units without neighbors or with a few number of neighbors that belong to the same country. The selected dataset includes complete data for 221 NUTS-2 regions across Europe (see Figure 2 or Figure 3 for a graphical representation of the available spatial units).
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**A**: Thus, certain information should be exchanged to induce an efficient trade, and the solution concept therein is a perfect Bayes equilibrium.
In a similar vein, Čopič and Ponsatí (2008) study robust prior-independent mechanisms when the buyer’s and seller’s valuations are discounted over time and hence both agents are eager to have the trade occur as soon as possible**B**: In this setting, the mediator keeps the reported valuations of the buyer and the seller privately while trade is incompatible**C**: Then, after trade becomes compatible, the mediator discloses the agreement and trade occurs at the agreed price.
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**A**: Another potential advantage of the revealing policy is that, when it has been used in a market for years and the market fundamentals (including the distribution of student preferences and school priorities) do not change much over years, the cutoff lottery numbers for schools will tend to stabilize**B**: Although this argument seems to be new for lottery-based school choice, it has been verified in test-based admission systems in countries such as China and Turkey, and the theoretical insight has been conveyed by Azevedo and Leshno (2016) who show that a continuum school choice model generically has a unique stable matching, which is the limit of stable matching in large finite markets.
**C**: Then, students may rely on historical cutoff data and their current lottery numbers to estimate their chances for different schools, significantly simplifying their strategies
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**A**: Tables 1 and 2 show the RMSE and average CRPS statistics for our nowcast/estimate exercise. The top row of each table evaluates the accuracy of the nowcasts for U.S. GDP. Model-based “estimates” of U.S. GDP are not required, since the advance estimate of quarterly U.S. GDP is published at the end of the following month**B**: Looking across the rows of these two tables, we see considerable cross-state variation in accuracy. Looking across the columns, as hoped, we see that accuracy tends to improve as information accumulates – as we move to the right in each table. These gains are summarized in the final row of each table when reporting the RMSE and CRPS statistics averaged across states. These averaged statistics support our main finding that accuracy clearly improves in an absolute sense as more within-quarter and past-quarter information accumulates. They also indicate that the biggest jump in forecast accuracy, both for the point and density nowcasts and estimates, happens when information on the third month in the quarter (see the m3 nowcast) becomes available. Interestingly, this jump in accuracy occurs before the BEA publish their own advance estimate of quarterly U.S**C**: GDP. That is, we find that there is little gain to waiting an extra month and using the advance estimate of state GDP rather than a model-based nowcast, as long as this nowcast conditions on two months of within-quarter information. This is understood when we observe that the accuracy of the nowcasts for U.S. GDP growth also improves dramatically at m3. This means, in effect, that when computing nowcasts of state GDP at m3, the model is conditioning, via the cross-sectional aggregation constraint, on good estimates of U.S. GDP.
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**A**: Although new last-place votes were introduced, the number of last-place votes for A𝐴Aitalic_A remained the same, and hence this candidate still has a majority of last-place votes**B**:
We now have a Borda count election without any partial ballots, and since A𝐴Aitalic_A gained as many or more points than any other candidate, A𝐴Aitalic_A remains the winner of the election**C**: Therefore, this new election with all ballots completed has a majority loser failure, which cannot exist with the Borda count, hence a contradiction. Thus, MBC cannot have such a failure.
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**A**: Sciarra et al. (2020) built upon these insights, placing both methods within a network theory framework and redefining complexity as centrality scores within similarity matrices, which they found useful for exploring relationships in SDG achievement as well [25]. This study applies the SDGs-Generalized Economic Complexity (GENEPY) framework proposed by Sciarra et al.[26] to analyze India’s SDG progress, facilitating a better understanding of state-level contributions and complexities within a broader, networked framework. By identifying these connections, we aim to enhance understanding of state capabilities and support more informed, data-driven policy-making for sustainable development in India.**B**:
Our approach draws on the pioneering work of Hidalgo and Hausmann (2009), who developed the economic complexity index to assess a country’s knowledge base based on its export data. Their method, called the “method of reflection,” uses an iterative approach to link product ubiquity with country diversification, resulting in scores that capture a nation’s growth potential through economic complexity [23]**C**: In a later study, Tacchella et al. (2012) refined this method, suggesting that linear models could not fully capture complexity and advocating for a non-linear approach instead [24]
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**A**:
In terms of the standard errors, we see a ranking among the linear RA estimators**B**: Except in one case, nonlinear SRA standard errors are between 0.5%-1.5% smaller than for the linear SRA ones. Finally, nonlinear PRA standard errors are between 0%-5% smaller than those for the nonlinear SRA estimates. 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**C**: Standard errors for the linear SRA estimator are between 0.3%-3% smaller than those for linear PRA estimates
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**A**:
The main drawback of our approach is its dependence on a selection rule for choosing a candidate algorithm. When we are able to reject the null, as in our application, then the optimality properties of the selection rule (in particular, whether it satisfies improvement convergence as defined in Section 4) do not matter**B**: That is, no matter how naive or heuristic our selection rule is, we can conclude that the status quo algorithm is improvable. But when our test does not allow us to reject the null, there is in general an ambiguity: It may be that there is not sufficient evidence in the data to conclude existence of an improving algorithm; or, alternatively, it may be that the selection rule is not powerful enough to find this algorithm**C**: In the latter case, re-running the same test with a different selection rule could lead us to reject the null. This ambiguity is resolved asymptotically when the selection rule is improvement-convergent, since Theorem 4.2 establishes that our proposed test is consistent under this condition. One interesting direction for future work is thus to provide sufficient conditions for a selection rule to be improvement-convergent in different applications.
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**A**: Accordingly, this is where our formal analysis starts.
**B**: While this example provides a good illustration of our main results, it is too simple to capture all of them**C**: In particular, mixing – which as we show, is severely curtailed, but not necessarily precluded under imprecise discounting – can also play a role under perfect monitoring, albeit a limited one, to the extent that punishing players might require punishers to randomize
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**A**: (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. (2005)**B**: 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).
**C**: Recent studies have recognized the importance of auction speed in the design of mechanisms for real-world markets. Banks et al
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**A**: An important exclusion from this is the particular case of susceptible (in Weber’s sense, i.e., the just-noticeable differences are small) players, where our solution leads to the asymmetric Nash solution with weights expressed via the Weber coefficients of the players. Hence, our results show the Nash solution can have psychophysical foundations. More generally, the Weber strategy of bargaining can be implemented iteratively via an additional natural Axiom 4, and it converges to a unique solution, where the resource is exhausted.
**B**: The first way is to use the symmetry axiom, which postulates that the domains coincide. The second, behavioral way assumes that the players make proposals within the acceptable domain employing the previous proposal of the opponent as her baseline. Our implementation of Weber’s law reduces the set of allowed solutions, but generically does not produce a unique solution, unless new axioms are applied**C**: The solution starts by stating the concepts of (ir)relevance and the just noticeable differences implied by Weber’s law. Next, each player defines the baseline utility and the domain of acceptable utilities on the Pareto line. There are two equivalent ways to make these domains consistent with each other
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**A**: \citeasnounkruse2019comparing had a sample of 4883 observations, so long memory seems a reasonable modelling strategy in their case**B**: However, this approach cannot be applied to moderate sample size, such as the ones typically encountered in macroeconomic forecasting.**C**: On the other hand \citeasnounkruse2019comparing
derived the properties of the DM test in the presence of long memory using standard asymptotics, and memory and autocorrelation consistent standardisation
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Selection 4
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**A**: While the use of an identity matrix restricts information flow to individual nodes, the multi-layer design compensates by enabling the model to capture intricate non-linear dependencies**B**: This approach is particularly effective for datasets where temporal or spatial relationships are not explicitly defined but still contain valuable patterns.
**C**: The GNN processes features for 164164164164 nodes within each temporal window, leveraging the adjacency matrix to perform localized self-looped transformations
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Selection 3
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**A**: Specifically, if one assumes independence across structural errors and imposes a triangular (or acyclic) system, then the Cholesky decomposition of the variance matrix can identify the contemporaneous interaction matrix only up to a permutation and scaling. Concretely:
**B**: Consistent with most studies that leverage higher-cumulant structures for identification, Assumptions A1)–A3) at best ensure the identification of A𝐴Aitalic_A (and hence ΛΛ\Lambdaroman_Λ) only up to a permutation and scaling**C**: A familiar illustration of this concept appears in the Gaussian VAR literature
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**A**: Each task (i.e., responding “yes” or “no”) is repeated 100 times. ChatGPT represents the WEIRD benchmark.
**B**: Note: The top panel of the figure shows the number of Proposer “yes” responses by SCAs to contingent offers ranging from 0% to 100% in the Ultimatum Game**C**: The bottom panel shows the “no” count or rejections to proposed offers by the Responder
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Selection 4
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**A**: These papers cover a diverse range of topics and methodologies, showcasing the versatility of our approach. The visual representations of these knowledge graphs are provided in Figure 6 and 7, which highlight the varying structures and complexities of the narratives in these influential papers.
**B**: To concretely illustrate our graphical framework and the measures derived from it, we examine four landmark economic papers: Chetty et al. (2014), Banerjee et al**C**: (2015), Gabaix (2011), and Goldberg et al. (2010)
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**A**:
The remainder of this paper is structured as follows**B**: We explain our model and experimental setup in Section 3 and Section 4, respectively. Our main results, mechanism, and several robustness checks are presented in Section 5. Section 6 concludes.**C**: We provide an overview of how our work relates to other literature in the next section
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Selection 4
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**A**: Data aggregation and processing is subject to economies of scale and economies of scope**B**: The former are active up to a threshold amount, so that for amounts greater than the threshold diseconomies of scale take place**C**: The latter are in effect when data from both P0 an P1 are aggregated, which implies that economies of scope can only be leveraged by D1.
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Selection 4
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**A**: better performance with respect to the considered benchmark. Different loss functions are reported with different markers, and the horizontal lines indicate the 5% and the 10% critical values using fixed-b𝑏bitalic_b asymptotics.
**B**: A negative value of the test statistic indicates a lower loss for the Bank of England, i.e**C**: Figure 4 reports the test statistic for the null of equal predictive accuracy of the Bank of England and the random walk (first row) or the autoregressive (second row) benchmarks
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Selection 2
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**A**: As ML applications in electricity markets continue to grow, it is reasonable to include more ML users, such as conventional generators, demand, and system operators, in the model**B**: We leave this for future work.
**C**: While the equilibrium model considers wind power producers as the sole users of ML, the private and social benefits are evident even within this narrow setting
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Selection 2
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**A**: The regression-adjusted DTE estimates obtained with logit model are nearly identical to the simple DTE estimates and reduce the standard errors by 0.1%-1.1% across values of**B**:
Figure 4 shows the distributional effect of insurance coverage on the number of primary care visits**C**: The DTE estimates (top left) indicate a statistically significant effect on visits ranging from 0 to 6
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Selection 1
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**A**: This capability is critical for stress-testing and exploring rare, high-severity loss scenarios.
**B**: Latent Space: The latent space encodes each claim as a probabilistic distribution**C**: By sampling from this space, the VAE generates synthetic claims that extrapolate beyond the observed dataset while adhering to its statistical properties
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Selection 4
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**A**: The EMM dataset may not uniformly cover all media sources, and EM-DAT may have incomplete records for less severe disasters or those in underrepresented regions (Jones et al., 2022; Below et al., 2009)**B**:
Our study has several limitations**C**: Further research is needed to clarify the causal mechanisms behind the associations we observe.
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Selection 2
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**A**: with the lowest incidence, even though their mean responses are similar.**B**: “10”, is remarkably different from that of Washington DC,333333The survey also covered Washington DC**C**: It is aggregated here alongside
the states, even though it is entirely urban, unlike any state
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Selection 2
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**A**:
In computer science, Caragiannis et al. (2012) and Ferraioli and Ventre (2018) consider a primitive authentication rate, and they restrict attention to truthful equilibria of direct mechanisms**B**: (2012) allow the principal to use arbitrarily severe punishments to deter any report that is not authenticated with certainty. In our applications (Section 6), the agent can walk away at any time, so punishment is limited to the agent’s outside option.**C**: Our paper shows that the restriction to direct, truthful mechanisms is without loss if α𝛼\alphaitalic_α is most-discerning. Caragiannis et al
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Selection 3
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**A**: As Figure 4 shows, we see that the results are quite general: The industrial complexity of a location and its neighborhood are important for growth, while only the complexity of the neighborhood’s exports matters for a location’s growth**B**: The regression table for all these results can be found in the SI.
**C**: To test the robustness of the thesis of this work, we considered different time intervals to measure the growth of micro-regions
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Selection 3
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**A**: As is standard, we can re-write the total effect as follows:101010Here, we assume that the direct and indirect effects do not vary at different levels of Z**B**: See more discussion by Imai et al**C**: (2011). All results in the paper hold with other decompositions. See additional discussions of general cases in the Appendix C and correlated mechanisms in the Appendix E.
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Selection 4
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**A**: First, there is a small loss in the primary dimension because some marginally beneficial innovations are not implemented. Note that the foregone benefits are small for these innovations**B**: Proposition 2 demonstrates that implementing a positive hurdle rate is strictly beneficial to the firm. The intuition is straightforward: when the firm sets a hurdle rate slightly above zero, there are two conflicting effects**C**: Second, there is a significant gain in the secondary dimension because the firm avoids implementing innovations that would have negative effects (in expectation) there. By setting a positive hurdle rate, the firm screens out potential innovations that might have small positive benefits in the primary dimension but large negative effects in the secondary dimension. This makes a positive hurdle rate beneficial.
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Selection 1
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**A**: manufacturing sector in 2018. Then, we use data on the actual tariff change, the decline in the imports from China, and the rise in input inventories to calibrate the trade war with China that started in 2018, and the rise in delivery delays for inputs.**B**: We start by calibrating the model to match moments of the U.S**C**:
To measure and study the costs of the rise in delays in the aggregate economy, in this section we describe how we choose the parameters for our quantitative analysis
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Selection 4
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**A**: Q={4}𝑄4Q=\{4\}italic_Q = { 4 }**B**: effect of s2tsuperscriptsubscript𝑠2𝑡s_{2}^{t}italic_s start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT on s3tsuperscriptsubscript𝑠3𝑡s_{3}^{t}italic_s start_POSTSUBSCRIPT 3 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT, i.e**C**: The latent
variable utsuperscript𝑢𝑡u^{t}italic_u start_POSTSUPERSCRIPT italic_t end_POSTSUPERSCRIPT obfuscates the causal effect because it influences
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Selection 1
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**A**: Pioneering work by Aumann (1962), Bewley (2002) and Dubra et al**B**: (2004) studied the representation of incomplete preferences under risk and uncertainty. Incomplete preferences in non-deterministic environments have been the object of a growing literature: see, for example,
Nascimento and Riella (2011)**C**: Hope-and-prepare preferences define a partial order on acts
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Selection 1
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**A**: To disambiguate inventor careers, we use a modified version of the algorithm proposed by Trajtenberg et al., (2009)**B**: While other disambiguation methods based on supervised machine learning have been proposed (Li et al.,, 2014; Pezzoni et al.,, 2014), we find them unsuitable for our purposes due to the lack of training data and the black-box nature of these methods. The scoring scheme, described in detail in the supplemental material to the paper, assigns higher scores for matching features that occur less frequently in the population. It also assigns higher scores to less frequent inventor names, as their rarity makes it easier to distinguish between different inventor careers.
**C**: This heuristics-based approach assigns scores based on several matching criteria, such as self-citations, co-inventors, patent applicants, technology classes, addresses, and name frequency
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ABC
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Selection 1
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**A**: The different colors represent the different calibration methods. The charts use 1,000 replications from samples of size 2,000, 4,000 and 8,000 generate from DGP 4 (difficult outcome regression / difficult propensity score).**B**:
Note: The figure depicts the RMSE across replications**C**: From left to right, the charts show the results for gradient boosting, random forest and Lasso
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Selection 2
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**A**: However, it is limited by its use of broad 2-digit sector level of analysis. This high-level aggregation prevents researchers from exploring finer details of sector interactions or product-specific relationships. For instance, in the automotive industry, this limitation makes it difficult to analyze the complexity of specific car parts a country imports to produce its exported vehicles, hindering a more nuanced understanding of the technological sophistication embedded in these component-level trade flows.**B**: The need to bridge these two approaches arises from their respective drawbacks and potential complementarity: EC assumes that ’what you export’ is a proxy of ’what you produce competitively’, and thus of the capabilities you are endowed with, neglecting the fragmentation of production and the import of inputs from abroad. For instance, a country may specialise in exporting final commodities like cars while importing the most technologically advanced and capabilities embedded components essential for their production**C**: This scenario can lead to misinterpretation, where a country’s success in exporting vehicles is mistakenly seen as proof of its ability to manufacture all components, including those embodying cutting-edge technology and capabilities. Recent research stresses the importance of accounting for the import dimension of trade when assessing a country’s economic capabilities in the EC framework (Ferrarini and Scaramozzino,, 2015, 2016; Bam et al.,, 2020; Koch and Schwarzbauer,, 2021; Hernández-Rodríguez et al.,, 2023; Karbevska and Hidalgo,, 2023; Sebestyén et al.,, 2024).
In contrast, Input-Output Analysis provides precise information on how sectors or countries depend on inputs and where their intermediate or final exports go
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Selection 2
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**A**:
Florian Gunsilius is an Assistant Professor in the Department of Economics at Emory University**B**: His research interests are nonparametric approaches for statistical identification, estimation, and inference**C**: His current focus is on statistical optimal transport theory, mean field estimation, causal inference, and free discontinuity problems.
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Selection 2
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**A**: Early work focused primarily on the role of strategy complexity within game theory (Rubinstein,, 1986; Abreu and Rubinstein,, 1988)**B**: There is also interest in formalizing definitions of complexity, e.g., Gabaix and Graeber, (2024) build a general model of production within a cognitive economy in order to operationalize complexity, whereas Oprea, 2024a borrows insights from computer science to introduce a framework within which complexity reflects the cost for handling a task. Others, define it as the signal-to-noise ratio (Goncalves,, 2024), similarly what is often done in psychometrics. The common denominator throughout most of this literature is that definitions of complexity are typically input-based, i.e., they somehow reflect the underlying difficulty to process and handle a task.
**C**: More recently, the focus has shifted towards explaining mistakes and irrationalities, e.g., Oprea, 2024b study the effect of complexity on risk preferences, and Enke et al., 2024a the respective effect on time preferences
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Selection 4
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**A**: The framework is built around a bi-level design, with an inner simulation layer, with agents learning behavioural rules conditioned on an observation of updateable characteristics 𝜽𝜽\boldsymbol{\theta}bold_italic_θ, and an outer layer updating these characteristics 𝜽𝜽\boldsymbol{\theta}bold_italic_θ. As agent behaviour is conditioned on the evolving characteristics, this helps mitigate the Lucas critique, adapting behaviour in response to environmental change, and, due to the bi-level design, simultaneously optimises the environment itself in response to the changing agent behaviours. We formulate the problem as a Stackelberg game, solving a coupled set of non-linear equations, and show how this subsumes several common ABM tasks, such as calibration, scenario analysis, robust behavioural learning, and policy design, under one unified framework.**B**: ADAGE automatically learns behavioural rules that adapt to environmental changes, as well as learning these potential environmental changes based on the task of the designer (e.g., for policy design or calibration)**C**:
In this work, we develop a generic two-layer framework for ADaptive AGEnt-based modelling (ADAGE)
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Selection 1
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**A**: The idea of approximating semiparametric models with growing parametric models is, of course, not new, as there is a well developed literature on sieve estimation. Compared to this broad literature however, the goal of the present paper is much more specific**B**: We aim to find conditions under which we can transfer the classical efficiency results of maximum likelihood estimators of a Euclidean parameter in well-behaved parametric models to the semiparametric case. Whilst similar conclusions are reached in the sieve literature (see, e.g., Theorem 4 in Shen, (1997)), these results typically follow as special cases of more general results permitting, for example, criterion functions which are not log-likelihoods**C**: Our goal in this paper is to show that by focussing on the specific case of maximum likelihood estimation and being more specific about the behaviour of the approximating models permits a simpler and more user-friendly theory of semiparametric estimation. We show asymptotic normality and semiparametric efficiency of maximum likelihood estimators under conditions which are, we believe, simpler to both understand and verify than the often rather technical assumptions made in the sieve literature. In particular, we are able to show that this efficiency result holds in a broad class of models without imposing any empirical process type conditions. As we exemplify with the Cox model in Section 5.2, sieves might also just be used as a theoretical tool (and not for estimation), and allows for simpler efficiency proofs than those currently available.
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Selection 4
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**A**: (2018) extend this framework to quantile regressions.
Our break detection and asymptotic analysis of the breakpoint estimator builds on and extends that of Lee et al**B**: (2018) to a panel setting. A key contribution of our paper is the establishment of the super-consistency of the estimated breakpoint by exploring the cross-sectional variation, which is crucial for the inference of spillover and private effect estimators. The use of cross-sectional information also alleviates the requirement on the length of time series to a large extent.**C**: Gaussian errors, while Lee et al
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Selection 1
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**A**: When the model is misspecified, the parameter of interest is set as the minimizer of the population GMM criterion function, which is referred to as the pseudo-true value**B**:
It is worth emphasizing that this type of moment misspecification can only happen in over-identified moment condition models**C**: We assume that the pseudo-true value is unique. For more detail, see Kang and Lee (2024).
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Selection 3
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**A**: Harrington (2018) discuss the approaches of static checking an algorithm’s source code444Here “source code” refers to any formal representation of an algorithm, which also includes, for instance, the architecture and weights of neural network models**B**: without running it and dynamic testing the algorithm555Here “algorithm” refers to the executable form of the algorithm that a seller is going to deploy**C**: with synthetical inputs to learn its properties related to price collusion and conclude whether it should be prohibited. However, they suggest that to what extent the prohibition comes from a per se rule or rule of reason depends on details: Prohibitions from clear collusion-identifying properties checkable with these approaches can be classified as per se rules. Otherwise, classification into rule of reason is more appropriate.
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Selection 4
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**A**: These selections results in 3,277 observations, for which 26 percent of wage observations are censored**B**: Table LABEL:Table:Lee presents the estimates obtained using the IV Tobit model.**C**:
Following Lee (1995), we proceed with the analysis focusing on the data for married couples with non-negative family total income or “other” household income and where the wife was of working age (18-64) and not self-employed
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Selection 4
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**A**: Compared to the full sample, the restricted sample displays notable differences in both characteristics and outcomes.**B**:
Table I presents summary statistics for the full sample and the restricted sample**C**: The restricted sample includes students within 0.7 grade points of the Dean’s List cutoff, who completed at least two academic years and took a minimum of five units
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Selection 1
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**A**: One-to-many causal relations are highlighted in red**B**:
Figure 1: One and the same action — lifting sanctions to Iran — may lead to quite different outcomes depending on many other factors, such as growing availability of renewable energy sources or shale oil**C**: Loosely inspired by [3].
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Selection 2
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**A**: While the finite type space version of the notion of acceptable bet was used by Morris (2020). The notions of money pump we use in this paper are different in their forms from the ones typically appearing in the literature. However, in spirit, those reflect the very same intuition: arbitrage oppurtinity.**B**: The finite type space version of the notion of agreeable bet was used by Samet (1998a), the notion of agreeable bet we use in this paper was used in Hellman and Pintér (2022)**C**:
Some of the bet notions in this paper are not completely new
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Selection 1
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**A**: (2021) document increases in work experience throughout high school and college, coupled with a rise in time to college degree for the NLSY97 cohort. Additionally, Appendix B documents substantial changes across NLSY cohorts in the types of occupational experience accumulated over ages 17–26**B**: Most notably, experience accumulated in sales positions nearly tripled, while experience in manager and professional positions increased by 23% and 54%, respectively. Increases in management and professional experience were particularly strong at the high end of the AFQT distribution, while increases in sales and service experience were more uniform or concentrated at the low end.**C**: Given modest changes in the distribution of AFQT scores across cohorts (Altonji, Bharadwaj, and Lange, 2012), the “Test Reliability Ratio” term is likely to be very similar across NLSY cohorts.
By contrast, there are good reasons to think that skill dynamics during early-adulthood have changed. For example, Ashworth et al
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Selection 3
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**A**: For continuous features, we use kernel density estimation**B**:
Note: This figure displays the distribution of the features on the training sample by group created from individual XPER values using the K-Medoids methodology**C**: Dark red refers to the first group and light red to the second group.
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Selection 4
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**A**: We first re-establish that a first-price auction, in which the winner pays her own bid, consistently exhibits coordinated bid suppression. In contrast, the second-price auction aligns winning bids more closely with actual valuations, reduces volatility during the learning phase, and often speeds up convergence**B**: how other factors influence the impact of payment rules on seller reserves. Here I employ state of the art machine learning estimators to show that the impact of moving to second-price is magnified by fewer bidders, higher discount factors, and asynchronous updating.
**C**: The second-price design appears robust to further complexity, such as the introduction of partial common values via affiliation or more sophisticated exploration algorithms. We also obtain heterogeneous treatment effects i.e
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Selection 1
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**A**: The standard deviation**B**: The GMMf estimator corrects this, with
the average value of w^gmmf,10=0.989subscript^𝑤𝑔𝑚𝑚𝑓100.989\widehat{w}_{gmmf,10}=0.989over^ start_ARG italic_w end_ARG start_POSTSUBSCRIPT italic_g italic_m italic_m italic_f , 10 end_POSTSUBSCRIPT = 0.989**C**: w^2sls,10=0.003subscript^𝑤2𝑠𝑙𝑠100.003\widehat{w}_{2sls,10}=0.003over^ start_ARG italic_w end_ARG start_POSTSUBSCRIPT 2 italic_s italic_l italic_s , 10 end_POSTSUBSCRIPT = 0.003
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Selection 2
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**A**: Let 𝐕=[𝐯1,…,𝐯R]𝐕subscript𝐯1…subscript𝐯𝑅\mathbf{V}=[\mathbf{v}_{1},\ldots,\mathbf{v}_{R}]bold_V = [ bold_v start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , … , bold_v start_POSTSUBSCRIPT italic_R end_POSTSUBSCRIPT ] and 𝐯j=[𝐯j,1⊤,…,𝐯j,T⊤]⊤subscript𝐯𝑗superscriptsuperscriptsubscript𝐯𝑗1top…superscriptsubscript𝐯𝑗𝑇toptop\mathbf{v}_{j}=[\mathbf{v}_{j,1}^{\top},\ldots,\mathbf{v}_{j,T}^{\top}]^{\top}bold_v start_POSTSUBSCRIPT italic_j end_POSTSUBSCRIPT = [ bold_v start_POSTSUBSCRIPT italic_j , 1 end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ⊤ end_POSTSUPERSCRIPT , … , bold_v start_POSTSUBSCRIPT italic_j , italic_T end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ⊤ end_POSTSUPERSCRIPT ] start_POSTSUPERSCRIPT ⊤ end_POSTSUPERSCRIPT for 1≤j≤R1𝑗𝑅1\leq j\leq R1 ≤ italic_j ≤ italic_R**B**: Note that 𝔽𝐕(−J)𝔽superscript𝐕𝐽\mathbb{F}\mathbf{V}^{(-J)}blackboard_F bold_V start_POSTSUPERSCRIPT ( - italic_J ) end_POSTSUPERSCRIPT is a T×(R−J)𝑇𝑅𝐽T\times(R-J)italic_T × ( italic_R - italic_J ) matrix with the tthsuperscript𝑡𝑡ℎt^{th}italic_t start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT row being [𝐟t⊤𝐯J+1,t,𝐟t⊤𝐯R,t]superscriptsubscript𝐟𝑡topsubscript𝐯𝐽1𝑡superscriptsubscript𝐟𝑡topsubscript𝐯𝑅𝑡[\mathbf{f}_{t}^{\top}\mathbf{v}_{J+1,t},\mathbf{f}_{t}^{\top}\mathbf{v}_{R,t}][ bold_f start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ⊤ end_POSTSUPERSCRIPT bold_v start_POSTSUBSCRIPT italic_J + 1 , italic_t end_POSTSUBSCRIPT , bold_f start_POSTSUBSCRIPT italic_t end_POSTSUBSCRIPT start_POSTSUPERSCRIPT ⊤ end_POSTSUPERSCRIPT bold_v start_POSTSUBSCRIPT italic_R , italic_t end_POSTSUBSCRIPT ].**C**:
We consider I6subscript𝐼6I_{6}italic_I start_POSTSUBSCRIPT 6 end_POSTSUBSCRIPT first
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Selection 2
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**A**: I define treatment cohorts by the period when a state first increased its minimum wage and use the other states that do not increase the minimum wage as control groups (never-treated). Our specifications have two differences. First, to include the confounding event, Medicaid Expansion under the ACA, I use data from 2010 to 2020 instead of from 2001 to 2007**B**: Similar to 2001 to 2007, the federal minimum wage is fixed at $7.25 per hour during this period. Second, CS clustered their standard errors at the county level, whereas I clustered them at the state level, which is the level of the treatment as recommended in Abadie et al. (2023).
**C**: To illustrate the empirical relevance of omitted event bias, I revisit the effect of minimum wage on teen employment with the DiD design using staggered timing of minimum wage increases across the states in the U.S. (Neumark and Wascher, 2006; Dube, Lester and Reich, 2016; Cengiz et al., 2019; Callaway and Sant’Anna, 2021). My specification largely follows Callaway and Sant’Anna (2021)
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Selection 1
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**A**:
MTS quantifies the average cosine similarity between a word’s vector representation across consecutive time frames**B**: For instance, words related to technology or societal changes might have a high temporal distance due to rapid shifts in their contextual usage, reflecting evolving discourse. The MTS for word w, MTS(w), across consecutive years is defined as:**C**: This metric provides insight into the degree of semantic drift, and helps determine whether word meanings evolve gradually or shift abruptly
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ABC
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Selection 4
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**A**: In the context of electronic payments, transactions are typically validated when the payee receives confirmation from the payer’s bank that sufficient funds are available to complete the transaction**B**: However, this process inherently requires revealing the payer’s identity to at least some level of authority, making it incompatible with the desire for full anonymity.**C**:
As previously discussed, the two primary characteristics of physical cash are the preservation of anonymity, particularly the identity of the spender, and the assurance of authenticity, meaning that the note or coin represents a legitimate and recognized value
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Selection 2
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**A**: benchmark**B**: [14] looks at sovereign bond yields and spreads with respect to the U.S**C**: As explanatory variables they consider
vulnerability (country’s exposure, sensitivity, and capacity to adapt to the impacts of climate change) and resilience (country’s capacity to apply economic investments and convert them to adaptation actions).
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Selection 3
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**A**: The independence of item-level treatment effects in this regime explains the superior performance of the IR estimator.
**B**: In contrast, under relaxed capacity constraints where capacity is sufficient to meet desired base-stock levels, the system can be decomposed into independent parallel experiments for each item**C**: In this scenario, the IR estimator becomes naturally unbiased as cross-item interference from capacity sharing is eliminated, enabling each item to achieve its intended base-stock level without scaling
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Selection 2
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**A**: In practice, researchers typically estimate generalized impulse response functions using a two-stage least-squares type estimator**B**: We first analyze this generalized local projection IV
**C**: This is also sometimes called “local projections with an external instrument” (JordaSchularickTaylor(15))
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ACB
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BAC
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BCA
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ABC
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Selection 1
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**A**: Suppose first that s2(b,c)>0subscript𝑠2𝑏𝑐0s_{2}(b,c)>0italic_s start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT ( italic_b , italic_c ) > 0**B**: Then, by (38) and (20)–(22),
s1(b,c)=3subscript𝑠1𝑏𝑐3s_{1}(b,c)=3italic_s start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ( italic_b , italic_c ) = 3, s2(b,c)=1subscript𝑠2𝑏𝑐1s_{2}(b,c)=1italic_s start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT ( italic_b , italic_c ) = 1 and s3(b,c)=2subscript𝑠3𝑏𝑐2s_{3}(b,c)=2italic_s start_POSTSUBSCRIPT 3 end_POSTSUBSCRIPT ( italic_b , italic_c ) = 2**C**: From s1(b,c)=3subscript𝑠1𝑏𝑐3s_{1}(b,c)=3italic_s start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ( italic_b , italic_c ) = 3 and (6) we also get
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ACB
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BCA
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ABC
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BAC
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Selection 3
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**A**: Time series data must not include gaps**B**: Data must be [TS] tsset or [XT] xtset before using xtbreak**C**: Panel data can be unbalanced. In this case, observations with missing data will not be included in the regressions. depvar, indepvars and varlist may contain time-series operators, see [TS] tsvarlist.
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BCA
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BCA
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BAC
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CAB
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Selection 3
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**A**: In other words, LLM simulations pose a unique dilemma to researchers. Improving ecological validity may require leaving unspecified details in the prompt, which runs the risk of introducing confoundedness. On the other hand, including extensive detail may reduce confounding, but at the expense of ecological validity due to focalism.**B**:
While traditional experiments with humans also run the risk of failing to simulate realistic scenarios, the nature of this risk differs. When running experiments with humans, using a blind design with realistic scenarios typically ensures ecological validity without introducing concerns of confoundedness**C**: On the other hand, with LLM simulations, a blind design raises confoundedness concerns. Adressing confoundedness by adding covariates requires adding covariates in the data generation stage of the study, which raises yet another concern, focalism, which in turn reduces ecological validity
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CBA
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CAB
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CBA
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BCA
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Selection 2
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**A**: One immediate application of the previous section is the construction of a canonical way to reduce the dimensionality of discrete DeGroot models.
We start by considering a classic DeGroot model with a large number of agents**B**: Since this new object is a DiKernel, we can discretize it by considering groups of agents.**C**: We can interpret this DeGroot model as a block-constant DiKernel with the uniform partition
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ABC
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BCA
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ABC
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ACB
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Selection 4
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**A**: Kato (2024b) avoid such approximations but focus on the small-gap regime, which may not align well with economic theory.**B**: Adusumilli (2022) rely on local asymptotic normality and diffusion processes, which are approximations that restrict the underlying distributions**C**:
These studies, however, have notable limitations
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ACB
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CBA
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ACB
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BAC
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Selection 2
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**A**: Subsection 3.1 introduces basic facts about spectral graph theory, which are used to derive the equilibrium strategies in Subsection 3.2**B**: This section presents preliminary results for analyzing the optimal public signal**C**: Subsection 3.3 formulates the principal’s optimization problem and presents the optimal public signal based on the existing literature.
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BAC
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ABC
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ABC
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ACB
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Selection 1
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**A**: Thus far, we have assumed that agents suffer from complete correlation neglect (Definition 2)**B**: The main challenge is that different statistical estimation methods handle correlations very differently, so they have to be treated on a case-by-case basis. We illustrate these challenges explicitly by contrasting Bayesian inference and MLE.
**C**: This section illustrates the challenges of allowing agents to have partial correlation neglect in games
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CBA
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ACB
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CAB
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BCA
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Selection 2
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**A**: The second extension—by allowing for any distribution of the signal—partially overlaps with the first extension and also works in combination with the fourth. While it is plausible that additional combinations of these extensions might be feasible, we currently do not see a way to allow all four generalizations simultaneously**B**: Our theorems accommodate combinations of these generalizations. The first, third, and fourth extensions can be applied together**C**: This limitation is inherent to our approach, as the asymptotic concentration in (6), (7), and (8) relies on a form of the law of large numbers. To achieve this we require at least some degree of independence — or, at a minimum, a decay of correlations along the S𝑆Sitalic_S dimension.
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BAC
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ABC
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CBA
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ACB
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Selection 1
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**A**:
While the open-access case provides a useful analytical benchmark, it rarely reflects the complexities of real-world property regimes**B**: As Elinor Ostrom and others have emphasized, communities typically regulate resource use through membership restrictions, social norms, and formal rules that mitigate the inefficiencies of pure open access \citepostrom1990,baland1996,bromley1992**C**: In addition, existing users often actively resist privatization and demand compensation for lost access rights. Our extended model incorporates these critical features - regulated access, political power dynamics, and compensation requirements - enabling analysis of diverse property regimes including customary tenure systems and contested claims.
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CBA
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ACB
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ABC
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BCA
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Selection 3
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**A**: Specifically, we apply the tools developed in Sections 3 and 4 to construct valid confidence intervals for the Marginal Value of Public Funds (MVPF) associated with a menu of government policies**B**: We begin by outlining the MVPF framework for welfare analysis and highlight why our approach is particularly well-suited for valid inference on MVPFs. Then, we use our toolkit to quantify the uncertainty in the estimated MVPF for eight public policies.**C**:
We illustrate our method by conducting inference on a metric that assesses the welfare implications of increasing expenditure on a range of government policies
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BAC
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CBA
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CAB
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BCA
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Selection 4
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**A**: To avoid selection biases, our samples include both commonly selected values from human samples, i.e., 18, 19, and 20, and less common choices, i.e., 11, 12, and 13. These choices correspond to the highest and lowest levels of reasoning depth.**B**:
We next turn our attention to few-shot prompting techniques, i.e., providing a few examples for LLMs to learn from ‘on the fly’, without updating model parameters (Brown et al., 2020). Notably, one recent study has shown that few-shot learning can synthesize more human-like responses for market research (Arora et al., 2024)**C**: We explore this possibility here, specifically employing CoT prompting. We provide the LLMs with three exemplary answers that include response values along with step-by-step reasoning that would rationalize those choices
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ACB
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CAB
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BAC
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ABC
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Selection 2
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**A**: RF’s flexibility in adjusting the weighting of the look-back window over time is evident, particularly around structural breaks**B**: In the two-sided case, we observe a sharp increase in the importance of recent lags just before an abrupt change, while leading observation weights peak right after the break. This pattern reflects RF’s ability, in the two-sided case, to capture breaks by not mixing pre- and post-break data together when computing a moving average around the sudden shift date.**C**:
Figure 3 illustrates the importance of lagged (and, when applicable, leading) observations in AlbaMA, shown for both one-sided and two-sided configurations
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ABC
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ABC
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BCA
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CAB
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Selection 3
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**A**: Our main result is presented in Section 3, while Section 4 outlines a general strategy for verifying the conditions of the main result and demonstrates how to apply it in the examples. Finally, Section 5 compares our results with the known results alluded to in the third paragraph of this section.
**B**: The remainder of the paper proceeds as follows**C**: Section 2 introduces the three above-mentioned estimation examples
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BAC
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CAB
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BAC
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BCA
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Selection 2
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