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Articles 2791 - 2820 of 8706
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Algorithms And Hardness Results For Computing Cores Of Markov Chains, Ali Ahmadi, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Roodabeh Safavi, Dorde Zikelic
Algorithms And Hardness Results For Computing Cores Of Markov Chains, Ali Ahmadi, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Roodabeh Safavi, Dorde Zikelic
Research Collection School Of Computing and Information Systems
Given a Markov chain M = (V,v0,δ), with state space V and a starting state v0, and a probability threshold ϵ, an ϵ-core is a subset C of states that is left with probability at most ϵ. More formally, C ⊆V is an ϵ-core, iff P reach(V\C) ≤ ϵ. Cores have been applied in a wide variety of verification problems over Markov chains, Markov decision processes, and probabilistic programs, as a means of discarding uninteresting and low-probability parts of a probabilistic system and instead being able to focus on the states that are likely to be encountered in a real-world …
A Study Of Multi-Task And Region-Wise Deep Learning For Food Ingredient Recognition, Jingjing Chen, Bin Zhu, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang
A Study Of Multi-Task And Region-Wise Deep Learning For Food Ingredient Recognition, Jingjing Chen, Bin Zhu, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Food recognition has captured numerous research attention for its importance for health-related applications. The existing approaches mostly focus on the categorization of food according to dish names, while ignoring the underlying ingredient composition. In reality, two dishes with the same name do not necessarily share the exact list of ingredients. Therefore, the dishes under the same food category are not mandatorily equal in nutrition content. Nevertheless, due to limited datasets available with ingredient labels, the problem of ingredient recognition is often overlooked. Furthermore, as the number of ingredients is expected to be much less than the number of food categories, …
Generating Concept Based Api Element Comparison Using A Knowledge Graph, Yang Liu, Mingwei Liu, Xin Peng, Christoph Treude, Zhenchang Xing, Xiaoxin Zhang
Generating Concept Based Api Element Comparison Using A Knowledge Graph, Yang Liu, Mingwei Liu, Xin Peng, Christoph Treude, Zhenchang Xing, Xiaoxin Zhang
Research Collection School Of Computing and Information Systems
Recommender systems are a valuable tool for software engineers. For example, they can provide developers with a ranked list of files likely to contain a bug, or multiple auto-complete suggestions for a given method stub. However, the way these recommender systems interact with developers is often rudimentary—a long list of recommendations only ranked by the model’s confidence. In this vision paper, we lay out our research agenda for re-imagining how recommender systems for software engineering communicate their insights to developers. When issuing recommendations, our aim is to recommend diverse rather than redundant solutions and present them in ways that highlight …
Watch Out! Motion Is Blurring The Vision Of Your Deep Neural Networks, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu
Watch Out! Motion Is Blurring The Vision Of Your Deep Neural Networks, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu
Research Collection School Of Computing and Information Systems
The state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples with additive random noise-like perturbations. While such examples are hardly found in the physical world, the image blurring effect caused by object motion, on the other hand, commonly occurs in practice, making the study of which greatly important especially for the widely adopted real-time image processing tasks (e.g., object detection, tracking). In this paper, we initiate the first step to comprehensively investigate the potential hazards of blur effect for DNN, caused by object motion. We propose a novel adversarial attack method that can generate visually natural motion-blurred adversarial examples, …
Social Media Analytics: A Case Study Of Singapore General Election 2020, Sebastian Zhi Tao Khoo, Leong Hock Ho, Ee Hong Lee, Danston Kheng Boon Goh, Zehao Zhang, Swee Hong Ng, Haodi Qi, Kyong Jin Shim
Social Media Analytics: A Case Study Of Singapore General Election 2020, Sebastian Zhi Tao Khoo, Leong Hock Ho, Ee Hong Lee, Danston Kheng Boon Goh, Zehao Zhang, Swee Hong Ng, Haodi Qi, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
The 2020 Singaporean General Election (GE2020) was a general election held in Singapore on July 10, 2020. In this study, we present an analysis on social conversations about GE2020 during the election period. We analyzed social conversations from popular platforms such as Twitter, HardwareZone, and TR Emeritus.
Secure And Verifiable Inference In Deep Neural Networks, Guowen Xu, Hongwei Li, Hao Ren, Jianfei Sun, Shengmin Xu, Jianting Ning, Haoming Yang, Kan Yang, Robert H. Deng
Secure And Verifiable Inference In Deep Neural Networks, Guowen Xu, Hongwei Li, Hao Ren, Jianfei Sun, Shengmin Xu, Jianting Ning, Haoming Yang, Kan Yang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Outsourced inference service has enormously promoted the popularity of deep learning, and helped users to customize a range of personalized applications. However, it also entails a variety of security and privacy issues brought by untrusted service providers. Particularly, a malicious adversary may violate user privacy during the inference process, or worse, return incorrect results to the client through compromising the integrity of the outsourced model. To address these problems, we propose SecureDL to protect the model’s integrity and user’s privacy in Deep Neural Networks (DNNs) inference process. In SecureDL, we first transform complicated non-linear activation functions of DNNs to low-degree …
Analysis Of Online Posts To Discover Student Learning Challenges And Inform Targeted Curriculum Improvement Actions, Michelle L. F. Cheong, Jean Y. C. Chen, Bingtian Dai
Analysis Of Online Posts To Discover Student Learning Challenges And Inform Targeted Curriculum Improvement Actions, Michelle L. F. Cheong, Jean Y. C. Chen, Bingtian Dai
Research Collection School Of Computing and Information Systems
Past research on analysing end-of-term student feedback tend to result in only high-level course improvement suggestions, and some recent research even argued that student feedback is a poor indicator of teaching effectiveness and student learning. Our intelligent Q&A platform with machine learning prediction and engagement features allow students to ask self-directed questions and provide answers in an out-of-class informal setting. By analysing such high quality and truthful posts which represent the students’ queries and knowledge about the course content, we can better identify the exact course topics which the students face learning challenges. We have implemented our Q&A platform for …
Identifying And Characterizing Alternative News Media On Facebook, Samuel S. Guimaraes, Julia C. S. Reis, Lucas Lima, Filipe N. Ribeiro, Marisa Vasconcelos, Jisun An, Haewoon Kwak, Fabricio Benevenuto
Identifying And Characterizing Alternative News Media On Facebook, Samuel S. Guimaraes, Julia C. S. Reis, Lucas Lima, Filipe N. Ribeiro, Marisa Vasconcelos, Jisun An, Haewoon Kwak, Fabricio Benevenuto
Research Collection School Of Computing and Information Systems
As Internet users increasingly rely on social media sites to receive news, they are faced with a bewildering number of news media choices. For example, thousands of Facebook pages today are registered and categorized as some form of news media outlets. This situation boosted the so-called independent journalism, also known as alternative news media. Identifying and characterizing all the news pages that play an important role in news dissemination is key for understanding the news ecosystems of a country. In this work, we propose a graph-based semi-supervised method to measure the political bias of pages on most countries and show …
Blockchain-Based Public Auditing And Secure Deduplication With Fair Arbitration, Haoran Yuan, Xiaofeng Chen, Jianfeng Wang, Jiaming Yuan, Hongyang Yan, Willy Susilo
Blockchain-Based Public Auditing And Secure Deduplication With Fair Arbitration, Haoran Yuan, Xiaofeng Chen, Jianfeng Wang, Jiaming Yuan, Hongyang Yan, Willy Susilo
Research Collection School Of Computing and Information Systems
Data auditing enables data owners to verify the integrity of their sensitive data stored at an untrusted cloud without retrieving them. This feature has been widely adopted by commercial cloud storage. However, the existing approaches still have some drawbacks. On the one hand, the existing schemes have a defect of fair arbitration, i.e., existing auditing schemes lack an effective method to punish the malicious cloud service provider (CSP) and compensate users whose data integrity is destroyed. On the other hand, a CSP may store redundant and repetitive data. These redundant data inevitably increase management overhead and computational cost during the …
Nearest Centroid: A Bridge Between Statistics And Machine Learning, Manoj Thulasidas
Nearest Centroid: A Bridge Between Statistics And Machine Learning, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
In order to guide our students of machine learning in their statistical thinking, we need conceptually simple and mathematically defensible algorithms. In this paper, we present the Nearest Centroid algorithm (NC) algorithm as a pedagogical tool, combining the key concepts behind two foundational algorithms: K-Means clustering and K Nearest Neighbors (k- NN). In NC, we use the centroid (as defined in the K-Means algorithm) of the observations belonging to each class in our training data set and its distance from a new observation (similar to k-NN) for class prediction. Using this obvious extension, we will illustrate how the concepts of …
Causal Intervention For Weakly-Supervised Semantic Segmentation, Zhang Dong, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun
Causal Intervention For Weakly-Supervised Semantic Segmentation, Zhang Dong, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun
Research Collection School Of Computing and Information Systems
We present a causal inference framework to improve Weakly-Supervised Semantic Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by using only image-level labels --- the most crucial step in WSSS. We attribute the cause of the ambiguous boundaries of pseudo-masks to the confounding context, e.g., the correct image-level classification of "horse'' and "person'' may be not only due to the recognition of each instance, but also their co-occurrence context, making the model inspection (e.g., CAM) hard to distinguish between the boundaries. Inspired by this, we propose a structural causal model to analyze the causalities among images, contexts, and …
Debunking Rumors On Twitter With Tree Transformer, Jing Ma, Wei Gao
Debunking Rumors On Twitter With Tree Transformer, Jing Ma, Wei Gao
Research Collection School Of Computing and Information Systems
Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by "word-of-post" through social media conversations. Conversation tree encodes important information indicative of the credibility of rumor. Existing conversation-based techniques for rumor detection either just strictly follow tree edges or treat all the posts fully-connected during feature learning. In this paper, we propose a novel detection model based on tree transformer to better utilize user interactions in the dialogue where post-level self-attention plays the key role for aggregating the intra-/inter-subtree stances. Experimental results on the TWITTER and PHEME datasets show that the proposed approach consistently improves …
Smartfuzz: An Automated Smart Fuzzing Approach For Testing Smartthings Apps, Lwin Khin Shar, Nguyen Binh Duong Ta, Lingxiao Jiang, David Lo, Wei Minn, Kiah Yong Glenn Yeo, Eugene Kim
Smartfuzz: An Automated Smart Fuzzing Approach For Testing Smartthings Apps, Lwin Khin Shar, Nguyen Binh Duong Ta, Lingxiao Jiang, David Lo, Wei Minn, Kiah Yong Glenn Yeo, Eugene Kim
Research Collection School Of Computing and Information Systems
As IoT ecosystem has been fast-growing recently, there have been various security concerns of this new computing paradigm. Malicious IoT apps gaining access to IoT devices and capabilities to execute sensitive operations (sinks), e.g., controlling door locks and switches, may cause serious security and safety issues. Unlike traditional mobile/web apps, IoT apps highly interact with a wide variety of physical IoT devices and respond to environmental events, in addition to user inputs. It is therefore important to conduct comprehensive testing of IoT apps to identify possible anomalous behaviours. On the other hand, it is also important to optimize the number …
A Hybrid Approach For Detecting Prerequisite Relations In Multi-Modal Food Recipes, Liangming Pan, Jingjing Chen, Shaoteng Liu, Chong-Wah Ngo, Min-Yen Kan, Tat-Seng Chua
A Hybrid Approach For Detecting Prerequisite Relations In Multi-Modal Food Recipes, Liangming Pan, Jingjing Chen, Shaoteng Liu, Chong-Wah Ngo, Min-Yen Kan, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Modeling the structure of culinary recipes is the core of recipe representation learning. Current approaches mostly focus on extracting the workflow graph from recipes based on text descriptions. Process images, which constitute an important part of cooking recipes, has rarely been investigated in recipe structure modeling. We study this recipe structure problem from a multi-modal learning perspective, by proposing a prerequisite tree to represent recipes with cooking images at a step-level granularity. We propose a simple-yet-effective two-stage framework to automatically construct the prerequisite tree for a recipe by (1) utilizing a trained classifier to detect pairwise prerequisite relations that fuses …
Jointly Optimizing Sensing Pipelines For Multimodal Mixed Reality Interaction, Ramesh Darshana Rathnayake Kanatta Gamage, Ashen De Silva, Dasun Puwakdandawa, Lakmal Meegahapola, Archan Misra, Indika Perera
Jointly Optimizing Sensing Pipelines For Multimodal Mixed Reality Interaction, Ramesh Darshana Rathnayake Kanatta Gamage, Ashen De Silva, Dasun Puwakdandawa, Lakmal Meegahapola, Archan Misra, Indika Perera
Research Collection School Of Computing and Information Systems
Natural human interactions for Mixed Reality Applications are overwhelmingly multimodal: humans communicate intent and instructions via a combination of visual, aural and gestural cues. However, supporting low-latency and accurate comprehension of such multimodal instructions (MMI), on resource-constrained wearable devices, remains an open challenge, especially as the state-of-the-art comprehension techniques for each individual modality increasingly utilize complex Deep Neural Network models. We demonstrate the possibility of overcoming the core limitation of latency–vs.–accuracy tradeoff by exploiting cross-modal dependencies–i.e., by compensating for the inferior performance of one model with an increased accuracy of more complex model of a different modality. We present a …
Sadt: Syntax-Aware Differential Testing Of Certificate Validation In Ssl/Tls Implementations, Lili Quan, Qianyu Guo, Hongxu Chen, Xiaofei Xie, Xiaohong Li, Yang Liu, Jing Hu
Sadt: Syntax-Aware Differential Testing Of Certificate Validation In Ssl/Tls Implementations, Lili Quan, Qianyu Guo, Hongxu Chen, Xiaofei Xie, Xiaohong Li, Yang Liu, Jing Hu
Research Collection School Of Computing and Information Systems
The security assurance of SSL/TLS critically depends on the correct validation of X.509 certificates. Therefore, it is important to check whether a certificate is correctly validated by the SSL/TLS implementations. Although differential testing has been proven to be effective in finding semantic bugs, it still suffers from the following limitations: (1) The syntax of test cases cannot be correctly guaranteed. (2) Current test cases are not diverse enough to cover more implementation behaviours. This paper tackles these problems by introducing SADT, a novel syntax-aware differential testing framework for evaluating the certificate validation process in SSL/TLS implementations. We first propose a …
Design Of A Two-Echelon Freight Distribution System In An Urban Area Considering Third-Party Logistics And Loading-Unloading Zones, Vincent F. Yu, Winarno, Shih-Wei Lin, Aldy Gunawan
Design Of A Two-Echelon Freight Distribution System In An Urban Area Considering Third-Party Logistics And Loading-Unloading Zones, Vincent F. Yu, Winarno, Shih-Wei Lin, Aldy Gunawan
Research Collection School Of Computing and Information Systems
This research examines the problem of designing a two-echelon freight distribution system in a dense urban area that considers third-party logistics (TPL) and loading–unloading zones (LUZs). The proposed system takes advantage of outsourcing the last mile deliveries to a TPL provider and utilizing LUZs as temporary intermediate facilities instead of using permanent intermediate facilities to consolidate freight. A mathematical model and a simulated annealing (SA) algorithm are developed to solve the problem. The efficiency and effectiveness of the proposed SA heuristic are verified by testing it on existing benchmark instances. Computational results show that the performance of the proposed SA …
Understanding Continuance Intention Toward Crowdsourcing Games: A Longitudinal Investigation, Xiaohui Wang, Dion Hoe-Lian Goh, Ee-Peng Lim
Understanding Continuance Intention Toward Crowdsourcing Games: A Longitudinal Investigation, Xiaohui Wang, Dion Hoe-Lian Goh, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Given the increasing popularity of gamified crowdsourcing, the study reported here involved examining determinants of users' continuance intention toward crowdsourcing games, both with longitudinal data and reference to a revised unified theory of acceptance and use of technology (UTAUT). At three time points, data were collected from an online survey about playing crowdsourcing games. Time-lagged regression, cross-temporal correlation, and structural equation modeling were performed to examine determinants of the acceptance of crowdsourcing games. Results indicate that the revised UTAUT2 is applicable to explaining the acceptance of crowdsourcing games. Not only did effort expectancy, hedonic motivation, and social influence directly affect …
A Near-Optimal Change-Detection Based Algorithm For Piecewise-Stationary Combinatorial Semi-Bandits, Huozhi Zhou, Lingda Wang, Lav N. Varshney, Ee-Peng Lim
A Near-Optimal Change-Detection Based Algorithm For Piecewise-Stationary Combinatorial Semi-Bandits, Huozhi Zhou, Lingda Wang, Lav N. Varshney, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
We investigate the piecewise-stationary combinatorial semi-bandit problem. Compared to the original combinatorial semi-bandit problem, our setting assumes the reward distributions of base arms may change in a piecewise-stationary manner at unknown time steps. We propose an algorithm, GLR-CUCB, which incorporates an efficient combinatorial semi-bandit algorithm, CUCB, with an almost parameter-free change-point detector, the Generalized Likelihood Ratio Test (GLRT). Our analysis shows that the regret of GLR-CUCB is upper bounded by O(√NKT logT), where N is the number of piecewise-stationary segments, K is the number of base arms, and T is the number of time steps. As a complement, we also …
Agent-Relative Consequentialism And Collective Self-Defeat, Matthew Hammerton
Agent-Relative Consequentialism And Collective Self-Defeat, Matthew Hammerton
Research Collection School of Social Sciences
Andrew Forcehimes and Luke Semrau argue that agent-relative consequentialism is implausible because in some circumstances it classes an act as impermissible yet holds that the outcome of all agents performing that impermissible act is preferable. I argue that their problem is closely related to Derek Parfit's problem of ‘direct collective self-defeat’ and show how Parfit's plausible solution to his problem can be adapted to solve their problem.
Learning Before And During The Covid-19 Outbreak: A Comparative Analysis Of Crisis Learning In South Korea And The Us, Seulki Lee, Jungwon Yeo, Chongmin Na
Learning Before And During The Covid-19 Outbreak: A Comparative Analysis Of Crisis Learning In South Korea And The Us, Seulki Lee, Jungwon Yeo, Chongmin Na
Research Collection School of Social Sciences
Learning is imperative in government responses to crises like the COVID-19 pandemic. This study examines the South Korean and United States governments’ responses to COVID-19 from a comparative perspective. The analysis focuses on crisis learning conducted before and during the COVID-19 outbreak, using the conceptual categories of intercrisis/intracrisis learning and single-/double-loop learning. The findings suggest that double-loop, intercrisis learning allows for more effective crisis management by (re)developing a common operating framework. The efficacy of learning is enhanced when double-loop learning is followed by single-loop learning that embeds new structures and operational procedures. The findings also suggest that intercrisis learning facilitates …
Confidence Is Sexy And It Can Be Trained: Examining Male Social Confidence In Initial, Opposite-Sex Interactions, Norman P. Li, Jose C. Yong, Ming-Hong Tsai, Mark H. C. Lai, Amy J. Y. Lim, Joshua M. Ackerman
Confidence Is Sexy And It Can Be Trained: Examining Male Social Confidence In Initial, Opposite-Sex Interactions, Norman P. Li, Jose C. Yong, Ming-Hong Tsai, Mark H. C. Lai, Amy J. Y. Lim, Joshua M. Ackerman
Research Collection School of Social Sciences
Objective: We investigated whether men's social confidence in an initial, opposite-sex chatting context can be improved through a video tutorial and the extent to which being perceived as socially confident results in being seen as more romantically desirable and worthy of future contact. Method: Women chatted with men who had received or not received a tutorial on how to handle speed-dating chats (Study 1: N = 129; Study 2: N = 60) or with male targets selected for having high versus moderate confidence in handling initial, opposite-sex encounters (Study 3: N = 46). Results: Tutorial-trained men felt more confident going …
Relativized Rankings, Matthew Hammerton
Relativized Rankings, Matthew Hammerton
Research Collection School of Social Sciences
In traditional consequentialism the good is position-neutral. A single evaluative ranking of states of affairs is correct for everyone, everywhere regardless of their positions. Recently, position-relative forms of consequentialism have been developed. These allow for the correct rankings of states to depend on connections that hold between the state being evaluated and the position of the evaluator. For example, perhaps being an agent who acts in a certain state requires me to rank that state differently from someone else who lacks this connection. In this chapter several different kinds of position-relative rankings related to agents, times, physical locations, and possible …
Great Expectations: What Does It Mean To Hold And Make Space For The Arts In Singapore?, Su Fern Hoe
Great Expectations: What Does It Mean To Hold And Make Space For The Arts In Singapore?, Su Fern Hoe
Research Collection School of Social Sciences
The arts and artists need space to thrive. However, as much of the land in Singapore is stateowned, providing space for the arts—literally and figuratively—remains challenging. Today, there is a rich variety of arts infrastructure in Singapore, including performing arts venues, statesubsidised artist studios and co-working spaces for freelancers. However, this stateadministered infrastructure comes with expectations, as these arts spaces have been positioned as expedient policy resources capable of achieving a broad confluence of cultural, urban, economic and social outcomes for Singapore. These “great expectations” on state-initiated arts spaces and the ensuing implications are the foci of this paper. I …
Text As Data: An Overview, Kenneth Benoit
Text As Data: An Overview, Kenneth Benoit
Research Collection School of Social Sciences
When it comes to textual data, the fields of political science and international relations face a genuine embarrassment of riches. Never before has so much text been so readily available on such a wide variety of topics that concern our discipline. Legislative debates, party manifestos, committee transcripts, candidate and other political speeches, lobbying documents, court opinions, laws – not only are all recorded and published today, but in many cases this is in a readily available form that is easily converted into structured data for systematic analysis.
Public Relations Education In Singapore: Educating The Next Generation Of Practitioners On Ethics, Eugene Yong Sheng Woon, Augustine Pang
Public Relations Education In Singapore: Educating The Next Generation Of Practitioners On Ethics, Eugene Yong Sheng Woon, Augustine Pang
Research Collection Lee Kong Chian School Of Business
This study examines if PR education adequately prepares students for the workplace, particularly in the practice of ethics in the context of Singapore, which has been described as one of “Asia’s economic tigers” (BBC, 2018). This study, thus, aims to first, elucidate the state of PR education specifically in relation to how PR ethics is taught in Singapore. Second, it examines how ethics education prepares students for the workplace in Singapore. Data comes from examining the syllabi of 14 universities in Singapore, both local and international, and interviews with 20 academics and practitioners. Findings suggest there are varying degrees in …
Motivation Purity Bias: Expression Of Extrinsic Motivation Undermines Perceived Intrinsic Motivation And Engenders Bias In Selection Decisions, Rellie Derfler-Rozin, Marko Pitesa
Motivation Purity Bias: Expression Of Extrinsic Motivation Undermines Perceived Intrinsic Motivation And Engenders Bias In Selection Decisions, Rellie Derfler-Rozin, Marko Pitesa
Research Collection Lee Kong Chian School Of Business
Organizational selection decisions often involve an exchange of information between candidates and decision makers as to why candidates are motivated to work in the given position. Drawing on popular management myths as our overarching framework, we theorize that candidates’ expressions of extrinsic motivation lead decision makers to infer that the candidate is less intrinsically motivated, leading to bias against such candidates. We term this effect motivation purity bias, and argue that it emerges despite ample evidence, which we review, showing that penalizing expressed extrinsic motivation is not only unfair to candidates but also counterproductive from the standpoint of maximizing future …
Causal Change Detection In Possibly Integrated Systems: Revisiting The Money-Income Relationship, Shuping Shi, Stan Hurn, Peter C. B. Phillips
Causal Change Detection In Possibly Integrated Systems: Revisiting The Money-Income Relationship, Shuping Shi, Stan Hurn, Peter C. B. Phillips
Research Collection School Of Economics
This paper re-examines changes in the causal link between money and income in the United States over the past half century (1959-2014). Three methods for the data-driven discovery of change points in causal relationships are proposed, all of which can be implemented without prior detrending of the data. These methods are a forward recursive algorithm, a rolling window algorithm, and a recursive evolving algorithm all of which utilize subsample tests of Granger causality within a lagaugmented vector autoregressive framework. The limit distributions for these subsample Wald tests are provided. Bootstrap methods are developed to control family-wise size in the implementation …
Testing For Structural Changes In Factor Models Via A Nonparametric Regression, Liangjun Su, Xia Wang
Testing For Structural Changes In Factor Models Via A Nonparametric Regression, Liangjun Su, Xia Wang
Research Collection School Of Economics
We propose a model-free test for structural changes in factor models. The basic idea is to regress the data on commonly estimated factors by local smoothing and compare the fitted values of time-varying factor loadings with those of time-invariant factor loadings estimated via principal component analysis. By construction, the test is designed to be powerful against both smooth structural changes and sudden structural breaks with a possibly unknown number of breaks and unknown break dates in the factor loadings. No restrictions on the form of alternatives or trimming of boundary regions near the beginning or end of the sample period …
Diagnostic Tests For Homoskedasticity In Spatial Cross-Sectional Or Panel Models, Badi K. Baltagi, Alain Pirotte, Zhenlin Yang
Diagnostic Tests For Homoskedasticity In Spatial Cross-Sectional Or Panel Models, Badi K. Baltagi, Alain Pirotte, Zhenlin Yang
Research Collection School Of Economics
We propose an Adjusted Quasi-Score (AQS) method for constructing tests for homoskedasticity in spatial econometric models. We first obtain an AQS function by adjusting the score-type function from the given model to achieve unbiasedness, and then develop an Outer-Product-of-Martingale-Difference (OPMD) estimate of its variance. In standard problems where a genuine (quasi) score vector is available, the AQS-OPMD method leads to finite sample improved tests over the usual methods. More importantly in non-standard problems where a genuine (quasi) score is not available and the usual methods fail, the proposed AQS-OPMD method provides feasible solutions. The AQS tests are formally derived and …