55 research outputs found
A rare case: cerebral air embolism causing stroke after lung cancer ablation
No full textCopyright © 2024 The Author(s). This is an Open Access article published by MA Healthcare Ltd and distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: CC BY-NC 4.0
A 75-year-old male patient was consulted because of a sudden onset of decreased consciousness immediately after radiofrequency ablation (RFA) of lung cancer. Non-contrast axial computed tomography (CT) showed air densities along the vascular trace in the frontoparietal sulci of both cerebral hemispheres, more prominent on the right (Figure 1A, white arrows). Minimum intensity projection (MinIP) reformation from CT showed air images probably of arterial origin more clearly (Figure 1B, white arrowheads). Diffusion-weighted imaging showed restricted diffusion in cortical and subcortical areas and centrum semiovale in both cerebral hemispheres, more markedly on the right, consistent with watershed infarction (Figure 1C, D). The patient was diagnosed as acute watersheed infarction secondary to cerebral arterial air embolism. Treatment was started immediately but unfortunately the patient died.
Figure 1. Imaging findings of a 75-year-old male patient with sudden onset of decreased consciousness. A. Non-contrast CT scan showed air densities along the vascular trace in the frontoparietal sulci of both cerebral hemispheres (white arrows). B. Minimum intensity projection reformation from CT showed air images probably of arterial origin more clearly (white arrowheads). C and D. Diffusion-weighted imaging showed restricted diffusion in bilateral cerebral hemispheres consistent with watershed infarction
Investigating the interaction between oil and macroeconomic indicators in the US, UK, and beyond
Full text linkThis thesis makes contributions towards understanding the relationships between energy, economic growth, and macroeconomics by focussing on the linkages between oil prices and output growth as well as oil sector profits and the real exchange rate. Following an introductory chapter, Chapters 2 and 3 investigate how the oil price–macroeconomy relationship in the United States and the United Kingdom has evolved over time and draw comparisons between the two countries. As a part of this, I estimate time-varying vector autoregression models using a rollingwindow approach. I then used impulse response functions to estimate the size of an oil price shock of a standard magnitude. The findings in these chapters identify differences and similarities between the two countries in question, and suggest that the oil price–macroeconomy relationship is sensitive to variable choice, model specification, and sample period. Chapter 4 studies the existence of resource curse and the Dutch disease on a global scale in oil-exporting countries. Using a unique, large-N, large-T dataset, I find evidence of a long-run relationship between rents in the oil sector and the real exchange rate of oil exporters as well as short-run adjustment towards an equilibrium. Although non-OPEC members exhibit behaviour in line with theory, the impact on OPEC countries’ real exchange rates is the largest
MSFAU Journal of Social Sciences, Issue 5/ Spring 2012
No full textSosyal Ağlar: Türkiye’de Facebook ve Twitter Kullanıcı Profilleri; Salih Akkemik /
Kültigin’in Heykel Başındaki Yırtıcı Kuş Tasvirinin Mahiyeti Hakkında; Beyza Aral /
Sanat ile Teknolojiyi Performansta Birleştiren Sanatçı: Stelarc; Solmaz Bunulday /
Binyılların Ağında Antik Ön Asya Grifonu: Kaynakları, Yayılımı ve Mitolojik Bağlantıları
Hakkında Notlar; Göktürk Ömer Çakır /
Finansal Serbestleşmenin Teorik Temelleri; Hasan Alp Özel /
Rekabet Endeksine İstatistiksel Yaklaşım: İmkb’de İşlem Gören Sanayi Firmaları İçin
Bir Uygulama;
Funda Sezgin, Özlen Erkal /
Üçkonak Köyü’nün (Kayseri/Tomarza) Halk İnanışları Açısından Değerlendirilmesi 93
Gülsüm Tarçın /
ÇEVİRİ:
Bernard Palissy: Modern Seramiğin Peygamberi;
Jerah Jhonson -
Çev. Ersoy Yılmaz /
Wilhelm Barthold’un Makaleleri;
Çev. Janıl Mirza Bapaeva /
KİTAP TANITIMI:
Arkeoloji ve Güzel Sanatların Buluşması: Geçmişten Geleceğe Armağan;
Göktürk Ömer Çakı
High-dimensional Bayesian methods for interpretable nowcasting and risk estimation
Full text linkThis thesis presents new models for nowcasting and macro risk estimation using frontier Bayesian methods that enable incorporating Big Data into policy relevant prediction problems. We propose variable selection algorithms motivated from Bayesian
decision theory to make model outcomes interpretable to the policy maker.
In chapter 2, we propose a Bayesian Structural Time Series (BSTS) model for nowcasting GDP growth. This model jointly estimates latent time trends to capture
slow moving changes in economic conditions along-side a high dimensional mixed
frequency component that is extracted from higher frequency (monthly) cyclical information. We extend on previous implementations of the BSTS with priors and
variable selection methods which facilitate selection over latent time trends as well
as mixed-frequency information that remain tractable to the policy maker. Empirically, we provide a novel nowcast application where we use a large dimensional
set of Internet search terms to gain advance information about supply and demand
sentiment for the US economy before more commonly considered macro information
are available to the nowcaster. We find that our proposed BSTS model offers large
improvements over competing models and that Internet search terms matter for
nowcasts before hard information about the macro economy have been published.
A simulation exercise confirms the good performance of the proposed model.
Chapter 3 presents the T-SV-t-BMIDAS (Bayesian Mixed Data Sampling) model
for nowcasting quarterly GDP growth. The model incorporates a long-run time-varying trend (T) and t-distributed stochastic volatility accounting for outliers (SV-t) into a Bayesian multivariate MIDAS. To address the high-dimensionality of the
model, to account for group-correlation in mixed frequency data, and to make the
model interpretable to the policy maker, we propose a new combination of group-shrinkage prior with sparsification algorithm for variable selection. The prior flexibly
accommodates between-group sparsity and within-group correlation and allows to
communicate the joint importance of predictors over the data release cycle. We
evaluate the model for UK GDP growth nowcasts covering also the time-span of
the Covid-19 recession. The model is competitive prior to the pandemic relative to
various benchmark models, while yielding substantial nowcast improvements during
the pandemic. Contrary to many previous nowcasting approaches, the model reads
in sparse group signals from the data. Simulations show competitive performance
of the variable selection methodology, with particularly good performance to be
expected for highly correlated data as well as dense data-generating-processes.
Chapter 4 presents a new Bayesian Quantile Regression (BQR) model for high dimensional risk estimation. It extends the horseshoe prior to the BQR framework
and provides a fast sampling algorithm for computation that makes it efficient for
high-dimensional problems. A large scale simulation exercise reveals that compared
to alternative shrinkage priors, the proposed methods yield better performance in
coefficient bias and forecast error, especially in sparse data-generating processes
and in estimating extreme quantiles. In a high dimensional Growth-at-Risk forecasting application, we forecast tail risks as well as complete forecast densities using
a database covering over 200 variables related to the U.S. economy. Quantile specific and density calibration score functions show that the horseshoe prior provides
the best performance compared to competing Bayesian quantile regression priors,
especially at short and medium run horizons.
Bayesian quantile regression models with continuous shrinkage priors are known to
predict well but are hard to interpret due to lack of exact posterior sparsity. Chapter 5 bridges this gap by extending the idea of decoupling shrinkage and sparsity.
The proposed procedure follows two steps: First, the quantile regression posterior is
shrunk via state of the art continuous shrinkage priors; then, the posterior is sparsified by taking the Bayes optimal solution to maximising a policy maker’s utility
function with joint preference for predictive accuracy as well as sparsity. For the
sparsification component, we propose a new variant of the signal adaptive variable
selection algorithm that automates the choice of penalization in the integrated tility
through a quantile specific loss-function that works well in high dimensions. Large
scale simulations show that, compared to the un-sparsified regression posterior, the
selection procedure decreases coefficient bias irrespective of the true underlying degree of sparsity in the data, and goodness of variable selection is competitive with
traditional variable selection priors. A high dimensional Growth-at-Risk forecasting
application to the US shows that the method detects varying degrees of sparsity
across the conditional GDP distribution and that the sources to downside risk vary
substantially over time.
Inspired by the work of Giannone et al. (2021) on the “illusion of sparsity” from
sparse modelling techniques, this chapter (6) investigates whether the recently popularised global-local priors, firstly, are implicitly informative about sparsity and,
secondly, whether they are able to communicate the true degree of sparsity from
the data. We consider two methods of analysis: implicit model size distributions
and sparsification techniques which are tested on a host of economic data sets and
simulations. The findings motivate a new horseshoe type model to which we add a
prior that makes it a-priori agnostic about the degree of sparsity and is shown to be
competitive to the spike-and-slab of Giannone et al. (2021) for forecasting as well
as sparsity detection.
Chapter 7 concludes with summaries, limitations of the thesis, as well as directions
for future research
War in Ukraine: The options for Europe's energy supply
No full textEurope is likely to remain reliant on Russian gas in the short term. In the absence of this gas supply, the continent is likely to experience shortages and associated inflation. Immediate solutions include diversification of Europe's natural gas supply and the use of alternative fossil fuels, such as coal, but the latter would come with significant climate costs. Over a longer period, there is more scope for reshaping Europe's energy policy and diversifying its energy supply, but these would come at a cost. Expanding renewable energy capacity within Europe is critical. Fine-tuning policy to balance climate objectives against the security of the energy supply will be difficult. To ensure energy security and emerge from this conflict greener, the answer may be renewable energy produced in Europe. Investment in renewables in developing countries, particularly in the continent's trading partners, also holds promise, as it would allow more natural gas imports into Europe while contributing to Europe's climate commitments.</p
The Crude Oil Market and US Economic Activity: Revisiting the Empirical Evidence
No full textThis paper empirically analyses the relationship between oil prices and real economic activity in the US. We seek to contribute to the literature by reconsidering the measurement of oil prices. We do so by accounting for whether oil price shocks follow periods of quiescence or volatility, since the former oil price changes could be more shocking. This study also accounts for asymmetry of shocks, since both theory and our empirical findings indicate that positive shocks to oil prices have a greater impact on economic activity than negative ones. We implement a rolling window approach in VARs and IRFs to investigate the time-varying nature of the relationship. Based on these, we find no clear evidence of the oil price-macroeconomy relationship weakening over time. There is strong evidence for asymmetry across specifications, proxies, and sample periods. Impulse response analysis suggests that a rise in oil prices is expected to lead to a decline in output growth rate and that this effect is larger in the second half of the dataset
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