17837 research outputs found
Sort by
Essays on Fiscal Rule Design and its implications
Get PDFThe three chapters of this thesis examine different aspects of the design of fiscal rules and their implications on the effects of policy interventions and how policy reacts to the economy.Chapter 1 focuses on the design of fiscal rules in DSGE models, which has been shown to matter crucially in identifying the effects of policy interventions and analyses two mechanically distinct components of fiscal policy rules: fiscal rule interactions and multimodality.In a first exercise, a set of alternative fiscal rules is considered for the benchmark Leeper, Plante and Traum (2010) model, with the main design feature being across budget block (expenditure vs taxation) interactions. The models are compared using the Bayesian data density. The results show that the Leeper, Plante and Traum (2010) model is competitive in the set but may be improved by including across-budget component interaction with taxes ordered first. Mechanically, the budget component interactions trickle down to how policy interventions are financed, showing increased coordination across blocks. In the benchmark, a government consumption shock raises the federal government's expenditures, and along the path, taxation increases to bring the debt level back to the steady state. In the recursive block models, budget impacts can be temporarily purely expansionary in that expenditure increases and taxation is reduced. Combining both aspects, it seems to reflect a temporary but coordinated approach to raise output across the expenditure and taxation categories.Secondly, I explore the role of multimodality in fiscal rules. Herbst and Schorfheide (2016) showed that fiscal parameters in the aforementioned model can become multimodal, leading to multimodal impulse responses. In essence, what that means is that fiscal policy may have varied impacts depending on the exact posterior parameter draw. For the Leeper, Plante and Traum (2010) model, I argue using graphs and demonstrate that the source of multimodality in the model is likely the structural design of the rules. Furthermore, building on the analysis in Herbst and Schorfheide (2016), I apply bi-modal regions to the highest posterior density regions as intervals tend to overestimate uncertainty of bi-modal distributions. The results show that the effects of consumption taxation shocks not only predict different scenarios depending on the mode but also disjointed impact scenarios. In particular, for consumption taxation shocks, the average effect of a structural shock is not a particularly likely event by itself.In Chapter 2, I explore how fiscal policy decisions relate to the business cycle and, building on that, how the effects of policy interventions may vary depending on when policy is conducted in the business cycle. To assess this, I estimate a small to medium-sized DSGE model with expressive non-linear fiscal and monetary rules using a higher-order approximation.The estimation procedure employed in this chapter combines several existing approaches developed by Herbst and Schorfheide (2016), Jasra et al. (2010), Buchholz, Chopin and Jacob (2021) and Amisano and Tristani (2010) to trade off computation time and inference quality. The model is estimated using Sequential Monte Carlo techniques to estimate the posterior parameter distribution and particle filter techniques to estimate the likelihood. Together, the estimation procedure reduces the estimation from weeks to days by up to 94%, depending on the comparison basis.To assess the behaviour of the effects of fiscal policy interventions, I sample impulse responses conducted along the historical data. The results present time-varying policy rules in which the effects of fiscal shocks go through deep cycles depending on the initial conditions of the economy. Among the set of fiscal instruments, government consumption goes through the most persistent cycles in its effectiveness in stimulating output. In particular, the effects of government consumption stimulus are estimated to be more effective during the financial crisis and, later, the Covid crisis, while being less effective in periods of above steady state output like the early 2000s.Relating the effects of specific stimulating shocks to the initial conditions using regression techniques, I show that fiscal policy is more effective at stimulating output if the interest rate and debt are low. Furthermore, the effects of government consumption are estimated to be increasing in output while tax cuts are decreasing.As a last contribution of Chapter 2, I explore how the behaviour of the central bank and government varies depending on the business cycle by analysing sampled policy rule gradients constructed on historical data. For the central bank, the results show that in phases of high output growth, the central bank puts more emphasis on controlling inflation and less on output. As the economy shifts into crisis, the central bank reduces its focus on inflation and shifts towards bringing output growth back to target. For the fiscal side, the behaviour is heavily governed by the current debt level, and, for example, during the high debt periods of the 1990s, labour taxation became increasingly responsive to debt to stabilize the budget.Chapter 3 applies the model developed in Chapter 2 to a forecasting exercise using the DSGE-VAR framework. The analysis confirms previous results of the literature that the DSGE-VAR framework and, by extension, DSGE models are frequently useful in aiding forecasting performance for output compared to standard models. Furthermore, I show that DSGE-VAR models can help aid forecasting performance of governmental variables like government consumption and debt quite significantly. However, there seems to be no single best methodology across all data series and forecasting settings considered, similar to the results in Gürkaynak, Kısacıkoğlu and Rossi (2014). Rather, the best-performing methodology may depend on factors like sample selection, modelling framework and potentially others.In a novel exercise, I explore the utility of a variation of the Chapter 2 model with a Zero Lower Bound constraint for forecasting. Overall, the model performs well but is not necessarily competitive with the much simpler DSGE-VAR. However, the ZLB model does show some strength in forecasting fiscal variables
To what extent do sea-ice algae affect the modelled transmittance of photosynthetically active radiation (PAR) to the ice-ocean interface?
Get PDFTheorising Bad Faith in International Relations:Climate Change, Deception and the Negotiation of International Order
Get PDFThis study examines how climate-vulnerable states charge major carbonemitters with bad faith behaviors, how those emitters respond in ways thatoften confirm the bad faith charges, and what vulnerable states proposeas policy alternatives. Using an existentialist conceptualization of bad faithand Bassan-Nygate and Heimann’s four response mechanisms – projection,distortion, displacement, and rationalization – we identify how majoremitters try to negate bad faith claims in ways that are deceptive of theself and the other. Major emitters require self-ref lection to identify howthey are not meeting international climate policy agreements and beginto address what they must change (about themselves), but vulnerable statesnote that this ref lection is absent. This study of 399 speeches by nationalleaders at three climate summits opens directions for scholars, activists andpolicymakers to understand how interactions around bad faith illuminatethe politics of bad faith and the potential for change this contains
A 1D Convolutional Neural Network for Spam Classification
Get PDFEmails and short messages are favoured for their communication efficiency. However, a substantial volume of spam is communicated even with recipient consent. To tackle such problems, models like TextCNN are employed for spam detection due to their simple network structures, swift training speed, and commendable performance. While TextCNN excels in spam classification, it has several drawbacks. For example, it utilized multi-channels with limited improvement and reduced explainability. It also lacks the capability to extract long-range features. Therefore, we introduce a novel 1D Convolutional Neural Network, overcoming TextCNN limitations by removing channels and employing an extended kernel. We assess both the proposed and the existing TextCNN models on a large-scale mixed dataset. The empirical results indicate a superior performance of the proposed model over TextCNN with more efficient computational cost, marking a significant advancement in spam classification
Are natural resources a curse, a blessing, or a double-edged sword? Implications for environmental sustainability
Get PDFTeaching the principles of artificial intelligence to Generation Z:The SMaILE-App mobile game application
Get PDFArtificial Intelligence (AI) has increasingly become a significant component of our daily routines. Therefore, it is crucial for society to familiarise young people with AI early on in their lives. We present our educational experience with the SMaILE (Simple Methods for Artificial Intelligence Learning and Education) research project. We focus on the design and development activities of SMaILE-App, a mobile game application, to teach teenagers the fundamental principles of AI. Following a constructionist pedagogical approach, SMaILE-App engages its users to learn and understand AI through suitably designed games. SMaILE-App primarily targets 12- to 18-year-old students but can also appeal to a broader audience of non-experts interested in AI technologies. Formalising the fun activities within SMaILE-App aims to facilitate a deep understanding of AI in its users. We describe SMaILE-App’s city-building game following Schell’s elemental tetrad: story, game mechanics, aesthetics, and technology.<br/
Paranoia in Social Interactions: Exploring the Roles of Adverse Life Events, Attachment Style, and Cognitive Flexibility
No full textPower, Technology and Gambling: An Actor-Network Theory Analysis of Online Sports Betting in Kenya
Get PDFThis thesis investigates how online sports betting is assembled as an industry and as a social practice in Kenya. Using Actor-Network Theory (ANT), it examines the emergence and continual (re)formation of companies, the regulator and consumers, as power moves through the network, conducted and transformed by artifacts, creating the “black box” of the betting industry. An interdisciplinary study of technology and society, it questions the nature of punter autonomy within a highly technologically-mediated consumption environment. Beginning with the notion that gambling consumption and production are inextricably intertwined through artifacts, where punter behaviours are inscribed in marketing technologies and embedded in game design, this study examines both sides as one, continuous, complex, translation of power. This was investigated using a multi-site ethnography in Nairobi, collecting heterogenous data from a wide variety of sources, resulting in insights on company strategies, regulator policies, consumer behaviours and technological features. It was then processed into a series of narratives which explore the design, build, regulation, marketing and consumption of products. The thesis proposes a spatialisation of ANT, a key theoretical contribution, developing an approach to examining space relationally in order to better critique the material world implications of virtual technologies, and the creation of virtual-physical spaces through artifacts. It also provides new knowledge on gambling production, as a rare study of corporate and regulatory actors within the industry. It concludes by advocating for the application of ANT to situations shaped by artificial intelligence, arguing that this vintage lens provides a fresh and relevant approach to critiquing the lively sources of machinic agency that shape the modern world.<br/
Data sources:
Access Repository Dashboard
Do you manage Royal Holloway - Pure? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!