Does Austerity Pay Off?

2020 ◽  
Vol 102 (2) ◽  
pp. 323-338 ◽  
Author(s):  
Benjamin Born ◽  
Gernot J. Müller ◽  
Johannes Pfeifer
Keyword(s):  
Initial Conditions ◽  
Sovereign Default ◽  
Fiscal Stress ◽  
Government Consumption ◽  
Data Set ◽  
Consumption Growth ◽  
Developed Economies ◽  
Alternative Measures ◽  
Fiscal Shocks

We investigate empirically how fiscal shocks—unanticipated and exogenous changes of government consumption growth—affect the sovereign default premium. For this purpose, we assemble a new data set for 38 emerging and developed economies. It contains approximately 3,000 observations for the sovereign default premium and three alternative measures of fiscal shocks. We condition our estimates on whether shocks are positive or negative and initial conditions in terms of fiscal stress. An increase of government consumption barely affects the default premium. A reduction raises the premium if fiscal stress is severe but decreases it if initial conditions are benign.

Deep Learning Based Classification of Time Series of Chaotic Systems over Graphic Images

2021 ◽  
Author(s):  
Süleyman UZUN ◽  
Sezgin KAÇAR ◽  
Burak ARICIOĞLU
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Transfer Learning ◽  
Chaotic Systems ◽  
Initial Conditions ◽  
Step Size ◽  
Learning Methods ◽  
Data Set ◽  
Graphic Images ◽  
Different Chaotic Systems

Abstract In this study, for the first time in the literature, identification of different chaotic systems by classifying graphic images of their time series with deep learning methods is aimed. For this purpose, a data set is generated that consists of the graphic images of time series of the most known three chaotic systems: Lorenz, Chen, and Rossler systems. The time series are obtained for different parameter values, initial conditions, step size and time lengths. After generating the data set, a high-accuracy classification is performed by using transfer learning method. In the study, the most accepted deep learning models of the transfer learning methods are employed. These models are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet and GoogLeNet. As a result of the study, classification accuracy is found between 96% and 97% depending on the problem. Thus, this study makes association of real time random signals with a mathematical system possible.

Banks’ international assets and sovereign default risk

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Filippo Gori
Keyword(s):  
Credit Risk ◽  
Default Risk ◽  
Sovereign Default ◽  
Content Type ◽  
Sovereign Credit ◽  
Developed Economies ◽  
Sovereign Default Risk ◽  
Sovereign Credit Risk ◽  
Using Data ◽  
International Assets

Purpose This paper aims to investigate the nexus between banks’ foreign assets and sovereign default risk in a panel of 15 developed economies. The empirical evidence suggests that banks’ foreign exposure is an important determinant of sovereign default probability. Design/methodology/approach Using data from the consolidated banking statistics (total foreign claims on ultimate risk basis) by the Bank of International Settlements, the author constructs a measure of bank international exposure to peer countries. This measure is then used as the target variable in a panel regression for sovereign credit default swaps. The model includes 15 European and non-European developed economies. Identification is discussed extensively in the paper. Findings Quantitatively, a 1% increase in banks’ cross-border claims increases sovereign default risk by about 0.19%. The relationship is weaker when banks are more capitalised. On the other hand, governments are more vulnerable to credit risk spillovers from banks’ international portfolios when having higher debt to GDP ratios. Originality/value To the best of the author’s knowledge, this is the first paper that attempts explicitly to establish an empirical connection between banks’ international assets and sovereign default risk. To the author’s opinion, this paper represents a contribution to our understanding of how sovereign credit risk spills over across countries. It also extends significantly the existing literature on the determinants of sovereign risk (that primarily focused on fundamentals, market characteristics – such as liquidity – and global factors). This paper ultimately sheds some new light on the role of intermediaries in the international transmission of credit risk, also adding to today’s discussion about the linkages between banks and sovereigns.

scholarly journals Assessing poverty persistence in households with children

2020 ◽  
Vol 18 (4) ◽  
pp. 551-569
Author(s):  
Enrico Fabrizi ◽  
Chiara Mussida
Keyword(s):  
Initial Conditions ◽  
State Dependence ◽  
Single Measure ◽  
Poverty Measure ◽  
Household Level ◽  
Household Characteristics ◽  
Strong State ◽  
Poverty Persistence ◽  
Alternative Measures ◽  
Households With Children

Abstract The analysis of poverty persistence received considerable attention in recent years. In this paper we explore the role of the adopted poverty measure in the analysis of its persistence. Specifically, we consider three measures: the risk of poverty, the severe material deprivation and subjective poverty, motivated by the understanding of poverty as a complex phenomenon and for which no single measure can effectively capture its several dimensions. The empirical analysis is based on the 2013-2016 longitudinal sample of the EU-SILC survey. We focus on Italian households with dependent children. We apply a correlated random effects probit models with endogenous initial conditions to assess genuine state dependence after controlling for structural household characteristics and variables related to participation in the labour market. A strong state dependence emerges, regardless of the considered poverty measure thus providing evidence of poverty and social exclusion persistence. We also find evidence of relevance of initial conditions for all measures in focus. Nonetheless, structural household characteristics and household level economic variables play roles that are often different in the three parallel models; these differences are consistent with the aims and nature of the alternative measures.

scholarly journals Ensemble daily simulations for elucidating cloud–aerosol interactions under a large spread of realistic environmental conditions

2020 ◽  
Vol 20 (11) ◽  
pp. 6291-6303
Author(s):  
Guy Dagan ◽  
Philip Stier
Keyword(s):  
Initial Conditions ◽  
Cloud Droplet ◽  
Large Data ◽  
Data Set ◽  
Cloud Properties ◽  
Large Spread ◽  
The Difference ◽  
Ice Content ◽  
Aerosol Effects ◽  
Longwave Flux

Abstract. Aerosol effects on cloud properties and the atmospheric energy and radiation budgets are studied through ensemble simulations over two month-long periods during the NARVAL campaigns (Next-generation Aircraft Remote-Sensing for Validation Studies, December 2013 and August 2016). For each day, two simulations are conducted with low and high cloud droplet number concentrations (CDNCs), representing low and high aerosol concentrations, respectively. This large data set, which is based on a large spread of co-varying realistic initial conditions, enables robust identification of the effect of CDNC changes on cloud properties. We show that increases in CDNC drive a reduction in the top-of-atmosphere (TOA) net shortwave flux (more reflection) and a decrease in the lower-tropospheric stability for all cases examined, while the TOA longwave flux and the liquid and ice water path changes are generally positive. However, changes in cloud fraction or precipitation, that could appear significant for a given day, are not as robustly affected, and, at least for the summer month, are not statistically distinguishable from zero. These results highlight the need for using a large sample of initial conditions for cloud–aerosol studies for identifying the significance of the response. In addition, we demonstrate the dependence of the aerosol effects on the season, as it is shown that the TOA net radiative effect is doubled during the winter month as compared to the summer month. By separating the simulations into different dominant cloud regimes, we show that the difference between the different months emerges due to the compensation of the longwave effect induced by an increase in ice content as compared to the shortwave effect of the liquid clouds. The CDNC effect on the longwave flux is stronger in the summer as the clouds are deeper and the atmosphere is more unstable.

Bubble footprints in the Malaysian stock market: are they rational?

2014 ◽  
Vol 22 (3) ◽  
pp. 223-236 ◽  
Author(s):  
Gilbert V. Nartea ◽  
Muhammand A. Cheema
Keyword(s):  
Stock Market ◽  
Emerging Market ◽  
Duration Dependence ◽  
Test Results ◽  
Data Set ◽  
Content Type ◽  
Speculative Bubbles ◽  
Rational Bubbles ◽  
Alternative Measures ◽  
Investor Behaviour

Purpose – The purpose of this paper is to re-examine the presence of rational speculative bubbles in the Malaysian stock market in light of contradictory results presented in previous studies. Design/methodology/approach – The authors use descriptive statistics, explosiveness tests and the duration dependence test. They use an expanded data set that encompasses at least two alleged bubble episodes addressing a significant limitation of previous studies. The authors use both monthly and weekly returns addressing concerns about the sensitivity of duration dependence test results to the use of monthly versus weekly returns, as well as a battery of alternative measures of returns. Findings – The authors detect bubble footprints but they do not appear to be rational. They found no evidence of rational speculative bubbles over the sample period regardless of whether monthly or weekly returns was used. The authors suggest that if there were bubbles in the Malaysian stock market, they might have been caused by irrational investor behaviour. The authors’ results do not support the suggestion that the duration dependence test is sensitive to the use of monthly versus weekly returns. Practical implications – Despite the absence of rational bubbles in the Malaysian stock market, the faint bubble footprints detected still suggest caution for investors, as the authors cannot categorically rule out the presence of irrational bubbles. Originality/value – This paper clarifies conflicting results of previous studies. It also contributes to the literature on bubble testing by presenting new evidence from an emerging market refuting the claim that duration dependence test results are sensitive to the use of either weekly or monthly returns.

scholarly journals Inflation and Economic Growth: a Review of The International Literature

2017 ◽  
Vol 20 (3) ◽  
pp. 41-56 ◽  
Author(s):  
Foluso A. Akinsola ◽  
Nicholas M. Odhiambo
Keyword(s):  
Economic Growth ◽  
Developing Countries ◽  
Negative Relationship ◽  
Threshold Level ◽  
Data Set ◽  
Developed Economies ◽  
The Impact ◽  
The Relationship ◽  
Over Time ◽  
Country Specific

This paper surveys the existing literature on the relationship between inflation and economic growth in developed and developing countries, highlighting the theoretical and empirical indications. The study finds that the impact of inflation on economic growth varies from country to country and over time. The study also finds that the results from these studies depend on country‑specific characteristics, the data set used, and the methodology employed. On balance, the study finds overwhelming support in favour of a negative relationship between inflation and growth, especially in developed economies. However, there is still much controversy about the specific threshold level of inflation that is appropriate for growth. Most previous studies on this subject just assume a unidirectional causal relationsship between inflation and economic growth. To our knowledge, this may be the first review of its kind to survey, in detail, the existing research on the relationship between inflation and economic growth in developed and developing countries.

scholarly journals Foreign Direct Investment in Transition Economies: Is It Related to Export Expansion or Import Substitution?

2014 ◽  
Author(s):  
Cevat Gerni ◽  
Selahattin Sarı ◽  
Ömer Selçuk Emsen ◽  
Burhan Kabadayı
Keyword(s):  
Panel Data Analysis ◽  
Import Substitution ◽  
Developed Countries ◽  
Economic Consequences ◽  
Market Potential ◽  
Foreign Direct Investments ◽  
Data Set ◽  
Foreign Investors ◽  
Explanatory Variables ◽  
Developed Economies

It is propounded that there are two motivations behind foreign direct investments (FDI). One of them is to invest in foreign countries because of trade barrier to export. In this case foreign investors operate in import substitution industries (ISI). The second fact to invest in another country away from homeland is to get benefit from cost advantages such as cheap labor and inputs, positioning closed to developed countries. With this aspect foreign investors operate in export oriented sectors (EOS). The economic consequences were discussed lighting on study’s aim examining the FDI to Transitions Countries whether are ISI or EOS. The foreign direct investments to Transitions Countries were investigated by panel data analysis. First and second generation unit root tests and cross section dependency tests were applied. Long and short term regressions were realized. The data set were obtained from Word Bank Data Base and annually data were collected between 1993 and 2012. Theoretically and statistically expected coefficients and coefficient’s sign for explanatory variables have been obtained. It is as a result observed that the countries have higher internal market potential to take foreign direct investments to import substitution industries. The countries close to developed economies have been drawing foreign direct investments to export oriented sectors.

scholarly journals Ensemble daily simulations for elucidating cloud–aerosol interactions under a large spread of realistic environmental conditions

2019 ◽  
Author(s):  
Guy Dagan ◽  
Philip Stier
Keyword(s):  
Initial Conditions ◽  
Cloud Droplet ◽  
Large Data ◽  
Data Set ◽  
Cloud Properties ◽  
Large Spread ◽  
The Difference ◽  
Ice Content ◽  
Aerosol Effects ◽  
Ice Water

Abstract. Aerosol effects on cloud properties and the atmospheric energy and radiation budgets are studied through ensemble simulations over two month-long periods during the NARVAL campaigns (December 2013 and August 2016). For each day, two simulations are conducted with low and high cloud droplet number concentrations (CDNC), representing low and high aerosol concentrations, respectively. This large data-set, which is based on a large spread of co-varying realistic initial conditions, enables robust identification of the effect of CDNC changes on cloud properties. We show that increases in CDNC drive a reduction in the top of atmosphere (TOA) net shortwave flux (more reflection) and a decrease in the lower tropospheric stability for all cases examined, while the TOA longwave flux and the liquid and ice water path changes are generally positive. However, changes in cloud fraction or precipitation, that could appear significant for a given day, are not as robustly affected, and, at least for the summer month, are not statistically distinguishable from zero. These results highlight the need for using large statistics of initial conditions for cloud–aerosol studies for identifying the significance of the response. In addition, we demonstrate the dependence of the aerosol effects on the season, as it is shown that the TOA net radiative effect is doubled during the winter month as compared to the summer month. By separating the simulations into different dominant cloud regimes, we show that the difference between the different months emerge due to the compensation of the longwave effect induced by an increase in ice content as compared to the shortwave effect of the liquid clouds. The CDNC effect on the longwave is stronger in the summer as the clouds are deeper and the atmosphere is more unstable.

scholarly journals How crucial is it to account for the Antecedent Moisture Conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments

2009 ◽  
Vol 6 (2) ◽  
pp. 1707-1736 ◽  
Author(s):  
L. Berthet ◽  
V. Andréassian ◽  
C. Perrin ◽  
P. Javelle
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Flood Forecasting ◽  
Hydrologic Model ◽  
Data Availability ◽  
Data Set ◽  
Soil Moisture Accounting ◽  
Antecedent Moisture ◽  
Rainfall Runoff Model ◽  
Event Based

Abstract. This paper compares event-based and continuous hydrological modelling approaches for real-time forecasting of river flows. Both approaches are compared using a lumped hydrologic model (whose structure includes a soil moisture accounting (SMA) store and a routing store) on a data set of 178 French catchments. The main focus of this study was to investigate the actual impact of soil moisture initial conditions on the performance of flood forecasting models and the possible compensations with updating techniques. The rainfall runoff model assimilation technique we used does not impact the SMA component of the model but only its routing part. Tests were made by running the SMA store continuously or on event basis, everything else being equal. The results show that the continuous approach remains the reference to ensure good forecasting performances. We show, however, that the possibility to assimilate the last observed flow considerably reduces the differences in performance. Last, we present a robust alternative to initialize the SMA store where continuous approaches are impossible because of data availability problems.

scholarly journals Exploiting Generative Adversarial Networks as an Oversampling Method for Fault Diagnosis of an Industrial Robotic Manipulator

2020 ◽  
Vol 10 (21) ◽  
pp. 7712
Author(s):  
Ziqiang Pu ◽  
Diego Cabrera ◽  
René-Vinicio Sánchez ◽  
Mariela Cerrada ◽  
Chuan Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Initial Conditions ◽  
Robotic Manipulator ◽  
Learning Curves ◽  
Fault Classification ◽  
Generative Adversarial Networks ◽  
Unbalanced Data ◽  
Data Set ◽  
Adversarial Networks ◽  
Test Of Hypotheses

Data-driven machine learning techniques play an important role in fault diagnosis, safety, and maintenance of the industrial robotic manipulator. However, these methods require data that, more often that not, are hard to obtain, especially data collected from fault condition states and, without enough and appropriated (balanced) data, no acceptable performance should be expected. Generative adversarial networks (GAN) are receiving a significant interest, especially in the image analysis field due to their outstanding generative capabilities. This paper investigates whether or not GAN can be used as an oversampling tool to compensate for an unbalanced data set in an industrial manipulator fault diagnosis task. A comprehensive empirical analysis is performed taking into account six different scenarios for mitigating the unbalanced data, including classical under and oversampling (SMOTE) methods. In all of these, a wavelet packet transform is used for feature generation while a random forest is used for fault classification. Aspects such as loss functions, learning curves, random input distributions, data shuffling, and initial conditions were also considered. A non-parametric statistical test of hypotheses reveals that all GAN based fault-diagnosis outperforms both under and oversampling classical methods while, within GAN based methods, an average accuracy difference as high as 1.68% can be achieved.

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