Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Estimation by Homotopy Continuation with an Application to Discount Factor Estimation∗

We develop a method to robustly estimate parameters of structural economic models with potential identification issues. Using homotopy path continuation applied to the MPEC formulation of the estimation problem (Su and Judd, 2012), we trace the parameter estimates and their confidence intervals as a function of a controlled parameter. As the discount factor is commonly assumed to be poorly identified in DDCMs, we trace the parameter estimates of the bus engine replacement model by Rust (1987) as a function of the discount factor β. Applying methods developed for undiscounted dynamic programming, we find that β is well identified and statistically significantly larger than 1. We establish an economically reasonable qualitative link between the decision-maker’s discounting and the real interest rates: in an extended model with an unanticipated structural break in β, the decrease in β qualitatively agrees with the macroeconomic regime change in the real interest rates during the great inflation. These rates were low or even negative, and increased after Paul Volcker took office as chairman of the Fed. In this period of negative ∗We are heavily indebted to Kenneth Judd and Karl Schmedders. We further thank Michel Bierlaire, Øystein Daljord, Robert Erbe, Walter Farkas, Bo Honoré, Diethard Klatte, Felix Kübler, Jasmin Maag, János Mayer, John Rust, Mark Watson, Johannes Willkomm, the participants of the 3rd Conference on Structural Dynamic Models in Chicago and seminar participants at the University of Zurich for helpful comments and discussions. We gratefully acknowledge the support of Kenneth Judd, Senior Fellow at the Hoover Institution. Reich gratefully acknowledges the support of the Fonds zur Förderung des akademischen Nachwuchses (FAN), Zurich. Finally, we thank Dave Brooks for editorial comments.
Spillovers and Asset Allocation

There is a large and growing literature on spillovers but no study that systematically evaluates the importance of spillovers for portfolio management. This paper provides such an analysis and demonstrates that spillovers are fully embedded in estimates of expected returns, variances, and correlations and that estimation of spillovers is not necessary for asset allocation. Simulations of typical empirical spillover settings further show that same-frequency spillovers are often negligible and spurious.
Commodity Markets Intervention: Consequences of Speculation, and Informed Trading

Abstract The Chinese regulatory authority's interventions into the futures market for steel during 2016 provide a rare opportunity to study the unintended consequences of interventions. Although hot rolled coil (HRC) and rebar are products with nearly identical components, their futures contracts trade separately. The regulatory authority intervened into the futures market for rebar but refrained from doing so for HRC. We document impacts of interventions measured by increased volatility, trading volume, and spreads in rebar markets. There is also evidence of informed trading in both markets, reflected by abnormally high trading volumes and low open interests occurring the day before the announcement of interventions.
Direct and indirect effects of voluntary governance codes on company disclosure practices

This study tests for direct and indirect effects of voluntary corporate governance codes on company disclosure practices. Two direct effects of the codes are expected: (1) an overall improvement in company disclosure practices, which is greater when the codes have a greater emphasis on disclosure, and (2) a leveling out of disclosure practices across companies (larger improvements in companies that were previously poorer disclosers) due to the codes new comply-or-explain requirements. The codes are also expected to have an indirect effect on disclosure practices through their effect on company governance practices. The results show that the introduction of codes in eight East Asian countries has been associated with lower analyst forecast error and a leveling out of disclosure practices across companies. The codes are also found to have an indirect effect on company disclosure practices through their effect on board independence. This suggests that voluntary corporate governance codes have both a significant direct effect and a significant indirect effect on company disclosure practices. JEL codes: G14, G15, G34, K22.
NBER WORKING PAPER SERIES REGULATORY EXPLOITATION AND THE MARKET FOR CORPORATE CONTROL

This paper investigates whether managers who fail to exploit regulatory loopholes are vulnerable to replacement. We use the U.S. hospital industry in 1985-1996 as a case study. A 1988 change in Medicare rules widened a pre-existing loophole in the Medicare payment system, presenting hospitals with an opportunity to increase operating margins by five or more percentage points simply by “upcoding” patients to more lucrative codes. We find that “room to upcode” is a statistically and economically significant predictor of whether a hospital replaces its management with a new team of for-profit managers. We also find that hospitals replacing their management subsequently upcode more than a sample of similar hospitals that did not, as identified by propensity scores. Leemore Dafny Department of Management and Strategy Kellogg School of Management Northwestern University 2001 Sheridan Road Evanston, IL 60208-2001 and NBER l-dafny@kellogg.northwestern. edu David Dranove Department of Management and Strategy Kellogg School of Management Northwestern University 2001 Sheridan Road Evanston, IL 60208-2001 d-dranove@kellogg.northwestern. edu
Reversing the Gains of the Civil Rights and Women’s Movements: How Housing Strain and Market Exclusion Led to Wealth Depletion During the Great Recession

Objective: We use feminist standpoint theory to investigate how the intersection of identity influenced wealth loss following the Great Recession among older single adults who benefitted from the social movements and legislative gains of the 1960s and 1970s. Looking back on more than a decade of recession and recovery, this study explores how intersections of race, class, and gender produced different wealth outcomes for the early baby boomer EBB cohort. Method: A sample of older baby boomers from the University of Michigan Health and Retirement Study was selected at two waves, 2004 and 2016. We used a generalized estimation equation with interaction effects to test changes in wealth over time for different race and gender groups. Results: Controlling for income and health, we found that both single Black and white women lost wealth at significantly higher rates than single white men. Poor health was associated with wealth shocks, or substantial wealth loss, for single white women, whereas the intersection of race and gender was associated with wealth loss for single Black women. Black women in this cohort ended the Great Recession with $85,000 less than their peers based on the overlapping identities of race and gender independent of health trouble. Conclusions: The policy history of women’s credit and lending access, as well as predatory targeting during the subprime lending crisis, contextualizes our findings. We discuss policy approaches to prevent future wealth erosion in households headed by Black women.
COMMD5 Inhibits Malignant Behavior of Renal Cancer Cells

Background/Aim: Copper metabolism MURR1 domain-containing 5 (COMMD5) is mainly expressed in renal tubules (RTs), where it facilitates re-differentiation of injured RTs. We reported that COMMD5 regulates the expression of epidermal growth factor receptor by participating in its endocytic membrane trafficking, thus inhibiting tumor growth. Here we aimed to determine the role of COMMD5 in malignant phenotypes of renal cell carcinoma (RCC). Materials and Methods: The associations between COMMD5 levels in RTs adjacent to RCC tumors in patients and their clinicopathologic characteristics were evaluated, and the effects of COMMD5 on cancer stemness in RCC cells were investigated. Results: Low COMMD5 levels in RTs correlated with high tumorigenesis and poor patient outcomes. COMMD5 overexpression in RCC cells reduced the proportion of cancer stem cell-like cells and their malignant phenotypes, including proliferation, invasion and sphere formation. Secreted COMMD5 from RT cells also reduced malignant phenotypes. Conclusion: COMMD5 might suppress malignant phenotypes of RCC, thus inhibiting tumor development and improving patient prognosis.
Ubiquitin-proteasome System Is a Promising Target for Killing Cisplatin-resistant Bladder Cancer Cells

Background/Aim: Activation of the ubiquitin-proteasome system (UPS) has been shown to be associated with drug resistance in cancer. Using bladder cancer cells, we investigated the association between UPS activation and cisplatin resistance and also the efficacy of UPS-targeting drugs. Materials and Methods: We established cisplatin-resistant bladder cancer cells (J82-cisR, T24-cisR) and examined the activation status of the UPS and the efficacy of MLN7243, oprozomib, ixazomib, and RTS-V5. Results: The UPS in cisplatin-resistant bladder cancer cells was activated compared to that in their parental controls. All the UPS-targeting drugs induced apoptosis and inhibited growth more effectively in the cisplatin-resistant bladder cancer cells than they did in the parental controls. Furthermore, these UPS-targeting drugs induced endoplasmic reticulum stress by causing unfolded protein accumulation at lower concentrations in the cisplatin-resistant bladder cancer cells. Conclusion: Targeting the UPS could be an effective strategy for treating cisplatin-resistant bladder cancer.
Development of Uterine Leiomyosarcoma During Follow-up After Caesarean Section in a Woman With Uterine Leiomyoma

Background/Aim: During pregnancy, uterine leiomyomas may cause problems and treatment typically entails uterine conservation. However, for cases of leiomyomas larger than a particular size with some clinical symptoms, enucleation should be performed. In clinical practice, the importance of postpartum follow-up of pregnancies with uterine fibroids must be established. Patients and Methods: A 47-year-old female visited an obstetrics and gynecology clinic with a primary complaint of irregular bleeding. We suspected an 8.4×6.6 cm myoma uteri and recommended immediate surgery. During the next visit, a pregnancy test was positive and the patient requested a follow-up for her myoma uteri diagnosis. Because of a breech presentation, we performed an elective cesarean section (CS) at 38 weeks and 1 day. The patient’s myoma uteri was stable throughout the pregnancy, and after delivery, we continued to follow her up as an outpatient. Results: Two years after the CS, the myoma uterus was 6 cm in size, and 6 months later, it had increased to 10 cm. Magnetic resonance imaging (MRI) supported a diagnosis of uterine leiomyosarcoma and she underwent surgery. Ultimately, she was pathologically diagnosed with uterine leiomyoma, uterine leiomyoma with bizarre nuclei, and uterine leiomyosarcoma following examination of the excised tissue by using molecular pathological examination with anti-cyclin E antibody and anti-Ki-67 antibody. Conclusion: Notably, this case demonstrated the usefulness of cyclin E and Ki-67 as biomarkers for the malignancy of uterine mesenchymal tumors. Presently, she is being monitored for tumor recurrence and metastases on a quarterly basis. In order to detect the rapid increase in uterine mesenchymal tumor, regular follow-up after birth is important.
GuideBoot: Guided Bootstrap for Deep Contextual Banditsin Online Advertising

The exploration/exploitation (E&E) dilemma lies at the core of interactive systems such as online advertising, for which contextual bandit algorithms have been proposed. Bayesian approaches provide guided exploration via uncertainty estimation, but the applicability is often limited due to over-simplified assumptions. Non-Bayesian bootstrap methods, on the other hand, can apply to complex problems by using deep reward models, but lack a clear guidance to the exploration behavior. It still remains largely unsolved to develop a practical method for complex deep contextual bandits. In this paper, we introduce Guided Bootstrap (GuideBoot), combining the best of both worlds. GuideBoot provides explicit guidance to the exploration behavior by training multiple models over both real samples and noisy samples with fake labels, where the noise is added according to the predictive uncertainty. The proposed method is efficient as it can make decisions on-the-fly by utilizing only one randomly chosen model, but is also effective as we show that it can be viewed as a non-Bayesian approximation of Thompson sampling. Moreover, we extend it to an online version that can learn solely from streaming data, which is favored in real applications. Extensive experiments on both synthetic tasks and large-scale advertising environments show that GuideBoot achieves significant improvements against previous state-of-the-art methods.
DGCN: Diversified Recommendation with Graph Convolutional Networks

These years much effort has been devoted to improving the accuracy or relevance of the recommendation system. Diversity, a crucial factor which measures the dissimilarity among the recommended items, received rather little scrutiny. Directly related to user satisfaction, diversification is usually taken into consideration after generating the candidate items. However, this decoupled design of diversification and candidate generation makes the whole system suboptimal. In this paper, we aim at pushing the diversification to the upstream candidate generation stage, with the help of Graph Convolutional Networks (GCN). Although GCN based recommendation algorithms have shown great power in modeling complex collaborative filtering effect to improve the accuracy of recommendation, how diversity changes is ignored in those advanced works. We propose to perform rebalanced neighbor discovering, category-boosted negative sampling and adversarial learning on top of GCN. We conduct extensive experiments on real-world datasets. Experimental results verify the effectiveness of our proposed method on diversification. Further ablation studies validate that our proposed method significantly alleviates the accuracy-diversity dilemma.
Multi-Session Diversity to Improve User Satisfaction in Web Applications

In various Web applications, users consume content in a series of sessions. That is prevalent in online music listening, where a session is a channel and channels are listened to in sequence, or in crowdsourcing, where a session is a set of tasks and task sets are completed in sequence. Content diversity can be defined in more than one way, e.g., based on artists or genres for music, or on requesters or rewards in crowdsourcing. A user may prefer to experience diversity within or across sessions. Naturally, intra-session diversity is set-based, whereas, inter-session diversity is sequence-based. This novel multi-session diversity gives rise to four bi-objective problems with the goal of minimizing or maximizing inter and intra diversities. Given the hardness of those problems, we propose to formulate a constrained optimization problem that optimizes inter diversity, subject to the constraint of intra diversity. We develop an efficient algorithm to solve our problem. Our experiments with human subjects on two real datasets, music and crowdsourcing, show our diversity formulations do serve different user needs, and yield high user satisfaction. Our large data experiments on real and synthetic data empirically demonstrate that our solution satisfy the theoretical bounds and is highly scalable, compared to baselines.
Community Value Prediction in Social E-commerce

The phenomenal success of the newly-emerging social e-commerce has demonstrated that utilizing social relations is becoming a promising approach to promote e-commerce platforms. In this new scenario, one of the most important problems is to predict the value of a community formed by closely connected users in social networks due to its tremendous business value. However, few works have addressed this problem because of 1) its novel setting and 2) its challenging nature that the structure of a community has complex effects on its value. To bridge this gap, we develop a Multi-scale Structure-aware Community value prediction network (MSC) that jointly models the structural information of different scales, including peer relations, community structure, and inter-community connections, to predict the value of given communities. Specifically, we first proposed a Masked Edge Learning Graph Convolutional Network (MEL-GCN) based on a novel masked propagation mechanism to model peer influence. Then, we design a Pair-wise Community Pooling (PCPool) module to capture critical community structures. Finally, we model the inter-community connections by distinguishing intra-community edges from inter-community edges and employing a Multi-aggregator Framework (MAF). Extensive experiments on a large-scale real-world social e-commerce dataset demonstrate our method’s superior performance over state-of-the-art baselines, with a relative performance gain of 11.40%, 10.01%, and 10.97% in MAE, RMSE, and NRMSE, respectively. Further ablation study shows the effectiveness of our designed components. Our code and dataset are available1.
DeepVista: 16K Panoramic Cinema on Your Mobile Device

In this paper, we design, implement, and evaluate , which is to our knowledge the first consumer-class system that streams panoramic videos far beyond the ultra high-definition resolution (up to 16K) to mobile devices, offering truly immersive experiences. Such an immense resolution makes streaming video-on-demand (VoD) content extremely resource-demanding. To tackle this challenge, introduces a novel framework that leverages an edge server to perform efficient, intelligent, and quality-guaranteed content transcoding, by extracting from panoramic frames the viewport stream that will be delivered to the client. To support real-time transcoding of 16K content, employs several key mechanisms such as dual-GPU acceleration, lossless viewport extraction, deep viewport prediction, and a two-layer streaming design. Our extensive evaluations using real users’ viewport movement data indicate that outperforms existing solutions, and can smoothly stream 16K panoramic videos to mobile devices over diverse wireless networks including WiFi, LTE, and mmWave 5G.
Task Offloading Strategy of Internet of Vehicles Based on Stackelberg Game

Moving vehicles generate a large amount of sensor data every second. To ensure automatic driving in a complex driving environment, it needs to fulfill a large amount of data transmission, storage, and processing in a short time. Real-time perception of traffic, target characteristics, and traffic density are important to achieve safe driving and a stable driving experience. However, it is very difficult to adjust the pricing strategy according to the actual demand of the network. In order to analyze the interaction between task vehicle and service vehicle, the Stackelberg game model is introduced. Considering the communication model, calculation model, optimization objectives, and delay constraints, this paper constructs the utility function of service vehicle and task vehicle based on the Stackelberg game model. Based on the utility function, we can obtain the optimal price strategy of service vehicles and the optimal purchase strategy of task vehicles.
An Experimental Study to Understand User Experience and Perception Bias Occurred by Fact-checking Messages

Fact-checking has become the de facto solution for fighting fake news online. This research brings attention to the unexpected and diminished effect of fact-checking due to cognitive biases. We experimented (66,870 decisions) comparing the change in users’ stance toward unproven claims before and after being presented with a hypothetical fact-checked condition. We found that, first, the claims tagged with the ‘Lack of Evidence’ label are recognized similarly as false information unlike other borderline labels, indicating the presence of uncertainty-aversion bias in response to insufficient information. Second, users who initially show disapproval toward a claim are less likely to correct their views later than those who initially approve of the same claim when opposite fact-checking labels are shown — an indication of disapproval bias. Finally, user interviews revealed that users are more likely to share claims with Divided Evidence than those with Lack of Evidence among borderline messages, reaffirming the presence of uncertainty-aversion bias. On average, we confirm that fact-checking helps users correct their views and reduces the circulation of falsehoods by leading them to abandon extreme views. Simultaneously, the presence of two biases reveals that fact-checking does not always elicit the desired user experience and that the outcome varies by the design of fact-checking messages and people’s initial view. These new observations have direct implications for multiple stakeholders, including platforms, policy-makers, and online users.
Unifying Offline Causal Inference and Online Bandit Learning for Data Driven Decision

A fundamental question for companies with large amount of logged data is: How to use such logged data together with incoming streaming data to make good decisions? Many companies currently make decisions via online A/B tests, but wrong decisions during testing hurt users’ experiences and cause irreversible damage. A typical alternative is offline causal inference, which analyzes logged data alone to make decisions. However, these decisions are not adaptive to the new incoming data, and so a wrong decision will continuously hurt users’ experiences. To overcome the aforementioned limitations, we propose a framework to unify offline causal inference algorithms (e.g., weighting, matching) and online learning algorithms (e.g., UCB, LinUCB). We propose novel algorithms and derive bounds on the decision accuracy via the notion of “regret”. We derive the first upper regret bound for forest-based online bandit algorithms. Experiments on two real datasets show that our algorithms outperform other algorithms that use only logged data or online feedbacks, or algorithms that do not use the data properly.