Economics
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Showing new listings for Monday, 25 November 2024
- [1] arXiv:2411.14763 [pdf, html, other]
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Title: From Replications to Revelations: Heteroskedasticity-Robust InferenceSubjects: Econometrics (econ.EM)
We compare heteroskedasticity-robust inference methods with a large-scale Monte Carlo study based on regressions from 155 reproduction packages of leading economic journals. The results confirm established wisdom and uncover new insights. Among well established methods HC2 standard errors with the degree of freedom specification proposed by Bell and McCaffrey (2002) perform best. To further improve the accuracy of t-tests, we propose a novel degree-of-freedom specification based on partial leverages. We also show how HC2 to HC4 standard errors can be refined by more effectively addressing the 15.6% of cases where at least one observation exhibits a leverage of one.
- [2] arXiv:2411.15092 [pdf, html, other]
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Title: Trade Wars with Trade DeficitsComments: 58 pages, 9 figures, 13 tablesSubjects: General Economics (econ.GN)
Trade imbalances significantly alter the welfare implications of tariffs. Using an illustrative model, we show that trade deficits enhance a country's ability to alter its terms of trade, and thereby benefit from tariffs. Greater trade deficits imply higher optimal, or welfare maximizing, tariffs. We compute optimal unilateral and Nash equilibrium tariffs between the United States and China $\unicode{x2014}$ the countries with the largest bilateral trade imbalance $\unicode{x2014}$ using a multi-region, multi-sector applied general equilibrium model with service sectors and input-output linkages, a computationally complex task. We find the United States gains from such a trade war with China, albeit minimally.
New submissions (showing 2 of 2 entries)
- [3] arXiv:2411.14463 (cross-list from cs.CL) [pdf, html, other]
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Title: Leveraging AI and NLP for Bank Marketing: A Systematic Review and Gap AnalysisSubjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); General Economics (econ.GN)
This paper explores the growing impact of AI and NLP in bank marketing, highlighting their evolving roles in enhancing marketing strategies, improving customer engagement, and creating value within this sector. While AI and NLP have been widely studied in general marketing, there is a notable gap in understanding their specific applications and potential within the banking sector. This research addresses this specific gap by providing a systematic review and strategic analysis of AI and NLP applications in bank marketing, focusing on their integration across the customer journey and operational excellence. Employing the PRISMA methodology, this study systematically reviews existing literature to assess the current landscape of AI and NLP in bank marketing. Additionally, it incorporates semantic mapping using Sentence Transformers and UMAP for strategic gap analysis to identify underexplored areas and opportunities for future research.
The systematic review reveals limited research specifically focused on NLP applications in bank marketing. The strategic gap analysis identifies key areas where NLP can further enhance marketing strategies, including customer-centric applications like acquisition, retention, and personalized engagement, offering valuable insights for both academic research and practical implementation. This research contributes to the field of bank marketing by mapping the current state of AI and NLP applications and identifying strategic gaps. The findings provide actionable insights for developing NLP-driven growth and innovation frameworks and highlight the role of NLP in improving operational efficiency and regulatory compliance. This work has broader implications for enhancing customer experience, profitability, and innovation in the banking industry.
Cross submissions (showing 1 of 1 entries)
- [4] arXiv:2304.01921 (replaced) [pdf, html, other]
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Title: Individual Welfare Analysis: Random Quasilinear Utility, Independence, and Confidence BoundsComments: 57 pages, 3 figuresSubjects: Econometrics (econ.EM)
We introduce a novel framework for individual-level welfare analysis. It builds on a parametric model for continuous demand with a quasilinear utility function, allowing for heterogeneous coefficients and unobserved individual-good-level preference shocks. We obtain bounds on the individual-level consumer welfare loss at any confidence level due to a hypothetical price increase, solving a scalable optimization problem constrained by a novel confidence set under an independence restriction. This confidence set is computationally simple and robust to weak instruments, nonlinearity, and partial identification. The validity of the confidence set is guaranteed by our new results on the joint limiting distribution of the independence test by Chatterjee (2021). These results together with the confidence set may have applications beyond welfare analysis. Monte Carlo simulations and two empirical applications on gasoline and food demand demonstrate the effectiveness of our method.
- [5] arXiv:2305.03134 (replaced) [pdf, html, other]
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Title: Debiased Inference for Dynamic Nonlinear Panels with Multi-dimensional HeterogeneitiesSubjects: Econometrics (econ.EM)
We introduce a generic class of dynamic nonlinear heterogeneous parameter models that incorporate individual and time effects in both the intercept and slope. To address the incidental parameter problem inherent in this class of models, we develop an analytical bias correction procedure to construct a bias-corrected likelihood. The resulting maximum likelihood estimators are automatically bias-corrected. Moreover, likelihood-based tests statistics -- including likelihood-ratio, Lagrange-multiplier, and Wald tests -- follow the limiting chi-square distribution under the null hypothesis. Simulations demonstrate the effectiveness of the proposed correction method, and an empirical application on the labor force participation of single mothers underscores its practical importance.
- [6] arXiv:2411.12412 (replaced) [pdf, html, other]
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Title: Procompetitive effects of vertical takeovers. Evidence from the European UnionSubjects: General Economics (econ.GN)
Rising market power threatens competition and decreases consumers' welfare. To date, a few works have shown how global firm-level markups increase, but there is scant evidence about the channels of such a change. This study investigates the causal impact of takeovers on markups and related firm-level outcomes on European manufacturing in 2007- 2021. Interestingly, findings suggest that takeovers aimed at vertical integration strategies are procompetitive because they result in lower markups (0.7%) and more sales (2.9%). The effects are higher as time passes from the takeover event, and they increase with the parents' number of already integrated subsidiaries. Notably, we do not find a significant impact on markups in horizontal integration strategies after we control for cherry-picking by acquirers. Eventually, we emphasize that our results on vertical takeovers point to strategies aimed at eliminating double profit margins on the input markets; thus, lower markups increase sales, spreading fixed costs and benefiting from economies of scale. Several checks on methods and sample composition effects confirm our central tenets. Finally, we reconnect with the debate initiated by the U.S. Vertical Merger Guidelines (2020; 2023), where the presumption of harm after vertical deals has been softened, thus considering procompetitive effects, but the discussion of potential foreclosure risks has been expanded.