4 results
Search Results
2. Evaluating merger effects.
- Author
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Genakos, Christos, Lamprinidis, Andreas, and Walker, James
- Subjects
MERGERS & acquisitions ,VALUE (Economics) ,PRICES ,ALGORITHMS - Abstract
This paper proposes a new algorithm to identify the potential effect of mergers by comparing the outcomes of interest in areas of overlap for the merging parties vis‐à‐vis areas where no overlap exists within a difference‐in‐differences estimation framework. Utilizing our proposed algorithm enables researchers and policymakers to perform retrospective merger evaluation studies that look at the effects of mergers on both price and non‐price aspects. We demonstrate the applicability and value of our proposed methodology by examining the effects on price and product variety of four mergers of the late 1980s and the 1990s on the U.K. car market. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Toward a causal link between attachment styles and mental health during the COVID‐19 pandemic.
- Author
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Vowels, Laura M., Vowels, Matthew J., Carnelley, Katherine B., Millings, Abigail, and Gibson‐Miller, Jilly
- Subjects
MENTAL depression risk factors ,CROSS-sectional method ,ATTACHMENT behavior ,RISK assessment ,AVOIDANCE (Psychology) ,LONELINESS ,HEALTH behavior ,RESEARCH funding ,DESCRIPTIVE statistics ,ATTRIBUTION (Social psychology) ,ANXIETY ,SOCIAL distancing ,STAY-at-home orders ,COVID-19 pandemic ,LONGITUDINAL method ,ALGORITHMS - Abstract
Background: Recent research has shown that insecure attachment, especially attachment anxiety, is associated with poor mental health outcomes, especially during the COVID‐19 pandemic. Other research suggests that insecure attachment may be linked to nonadherence to social distancing behaviours during the pandemic. Aims: The present study aims to examine the causal links between attachment styles (secure, anxious, avoidant), mental health outcomes (depression, anxiety, loneliness) and adherence to social distancing behaviours during the first several months of the UK lockdown (between April and August 2020). Materials & Methods: We used a nationally representative UK sample (cross‐sectional n = 1325; longitudinal n = 950). The data were analysed using state‐of‐the‐art causal discovery and targeted learning algorithms to identify causal processes. Results: The results showed that insecure attachment styles were causally linked to poorer mental health outcomes, mediated by loneliness. Only attachment avoidance was causally linked to nonadherence to social distancing guidelines. Discussion: Future interventions to improve mental health outcomes should focus on mitigating feelings of loneliness. Limitations include no access to pre‐pandemic data and the use of categorical attachment measure. Conclusion: Insecure attachment is a risk factor for poorer mental health outcomes. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. TTRISK: Tensor train decomposition algorithm for risk averse optimization.
- Author
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Antil, Harbir, Dolgov, Sergey, and Onwunta, Akwum
- Subjects
STOCHASTIC dominance ,PARTIAL differential equations ,RANDOM fields ,ALGORITHMS ,APPROXIMATION error ,VALUE at risk ,DIFFERENTIAL equations - Abstract
This article develops a new algorithm named TTRISK to solve high‐dimensional risk‐averse optimization problems governed by differential equations (ODEs and/or partial differential equations [PDEs]) under uncertainty. As an example, we focus on the so‐called Conditional Value at Risk (CVaR), but the approach is equally applicable to other coherent risk measures. Both the full and reduced space formulations are considered. The algorithm is based on low rank tensor approximations of random fields discretized using stochastic collocation. To avoid nonsmoothness of the objective function underpinning the CVaR, we propose an adaptive strategy to select the width parameter of the smoothed CVaR to balance the smoothing and tensor approximation errors. Moreover, unbiased Monte Carlo CVaR estimate can be computed by using the smoothed CVaR as a control variate. To accelerate the computations, we introduce an efficient preconditioner for the Karush–Kuhn–Tucker (KKT) system in the full space formulation.The numerical experiments demonstrate that the proposed method enables accurate CVaR optimization constrained by large‐scale discretized systems. In particular, the first example consists of an elliptic PDE with random coefficients as constraints. The second example is motivated by a realistic application to devise a lockdown plan for United Kingdom under COVID‐19. The results indicate that the risk‐averse framework is feasible with the tensor approximations under tens of random variables. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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