PRISTUP «VREMENSKE SERIJE» REŽIMU KINESKOG VALUTNOG TEČAJA

This paper deals with the issue of the exchange rate regime that China has established since 2005, when it announced a move away from the US dollar peg. In fact, from that date, the RMB was managed with reference to a basket of currencies rather than being pegged to the dollar; the exchange rate, therefore, became more flexible. But, though in the presence of basket peg, early econometric analysis (Shan-2005, Frankel & Wei- 2006, Ogawa-2006, Yamazaky-2006) found that the assigned basket gave overwhelming weight to the dollar, and that the degree of flexibility had hardly increased at all. Almost all those studies used a technique introduced by Frankel in 1994 to estimate the weights in a currency basket: on one side regressed changes in the value of the local currency, in this case the RMB, while on the other one changes in the values of the dollar, the euro, the yen and other currencies that may be in the basket. Though there are numerous econometric techniques for estimating the exchange rate system, the technique proposed by Frankel is still the most widely used. However, in our opinion, this model has an error of autocorrelation among the variables, a factor that could lead the analysis to different results. Therefore, this work proposes a study on the Chinese exchange rate regime through an alternative econometric technique.Ovaj se rad bavi pitanjem režima valutnog tečaja kojeg je Kina uvela 2005. kad je najavila odmak od veze s američkim dolarom. Doista, otada se RMB (CNY) veže za košaricu valuta osim za dolar te je tako tečaj postao fleksibilniji. No usprkos tome, prve ekonometrijske analize (Shan-2005, Frankel & Wei-2006, Ogawa-2006, Yamazaky-2006) pokazale su da je odlučujuća valuta u košarici još uvijek dolar i da je stupanj fleksibilnosti jedva povećan. Gotovo su sve te analize koristile tehniku koju je uveo Frankel 1994. kako bi procijenio važnost pojedinih valuta u košarici: s jedne strane RMB a s druge promjene u vrijednosti dolara, eura, jena i ostalih valuta koje se mogu naći u košarici valuta. Iako postoje brojne ekonometrijske tehnike za procjenu sustava valutnog tečaja, još uvijek je najraširenija Frankelova tehnika. Ipak, po našem mišljenju taj model ima grešku u autokorelaciji varijabli što je faktor koji bi analizu mogao odvesti do drugačijih rezultata. Stoga ovaj rad predlaže proučavanje režima kineskog valutnog tečaja pomoću alternativne ekonometrijske tehnike

PRISTUP «VREMENSKE SERIJE» REŽIMU KINESKOG VALUTNOG TEČAJA

This paper deals with the issue of the exchange rate regime that China has established since 2005, when it announced a move away from the US dollar peg. In fact, from that date, the RMB was managed with reference to a basket of currencies rather than being pegged to the dollar; the exchange rate, therefore, became more flexible. But, though in the presence of basket peg, early econometric analysis (Shan-2005, Frankel & Wei- 2006, Ogawa-2006, Yamazaky-2006) found that the assigned basket gave overwhelming weight to the dollar, and that the degree of flexibility had hardly increased at all. Almost all those studies used a technique introduced by Frankel in 1994 to estimate the weights in a currency basket: on one side regressed changes in the value of the local currency, in this case the RMB, while on the other one changes in the values of the dollar, the euro, the yen and other currencies that may be in the basket. Though there are numerous econometric techniques for estimating the exchange rate system, the technique proposed by Frankel is still the most widely used. However, in our opinion, this model has an error of autocorrelation among the variables, a factor that could lead the analysis to different results. Therefore, this work proposes a study on the Chinese exchange rate regime through an alternative econometric technique.Ovaj se rad bavi pitanjem režima valutnog tečaja kojeg je Kina uvela 2005. kad je najavila odmak od veze s američkim dolarom. Doista, otada se RMB (CNY) veže za košaricu valuta osim za dolar te je tako tečaj postao fleksibilniji. No usprkos tome, prve ekonometrijske analize (Shan-2005, Frankel & Wei-2006, Ogawa-2006, Yamazaky-2006) pokazale su da je odlučujuća valuta u košarici još uvijek dolar i da je stupanj fleksibilnosti jedva povećan. Gotovo su sve te analize koristile tehniku koju je uveo Frankel 1994. kako bi procijenio važnost pojedinih valuta u košarici: s jedne strane RMB a s druge promjene u vrijednosti dolara, eura, jena i ostalih valuta koje se mogu naći u košarici valuta. Iako postoje brojne ekonometrijske tehnike za procjenu sustava valutnog tečaja, još uvijek je najraširenija Frankelova tehnika. Ipak, po našem mišljenju taj model ima grešku u autokorelaciji varijabli što je faktor koji bi analizu mogao odvesti do drugačijih rezultata. Stoga ovaj rad predlaže proučavanje režima kineskog valutnog tečaja pomoću alternativne ekonometrijske tehnike

Use of ANTARES and IceCube data to constrain a single power-law neutrino flux

We perform the first statistical combined analysis of the diffuse neutrino flux observed by ANTARES (nine-year) and IceCube (six-year) by assuming a single astrophysical power-law flux. The combined analysis reduces by a few percent the best-fit values for the flux normalization and the spectral index. Both data samples show an excess in the same energy range (40--200 TeV), suggesting the presence of a second component. We perform a goodness-of-fit test to scrutinize the null assumption of a single power-law, scanning different values for the spectral index. The addition of the ANTARES data reduces the $p$-value by a factor 2$\div$3. In particular, a single power-law component in the neutrino flux with the spectral index deduced by the six-year up-going muon neutrinos of IceCube is disfavored with a $p$-value smaller than $10^{-2}$.Comment: 6 pages, 4 figures. Version published in AP

A Neural Network Evidence of the Nexus Among Air Pollution, Economic Growth, and COVID-19 Deaths in the Hubei Area

In this study, we used an image neural network model to assess the relationship between economic growth, pollution (PM2.5, PM10, and CO2), and deaths from COVID-19 in the Hubei area (China). Data analysis, neural network analysis, and deep learning experiments were carried out to assess the relationship among COVID-19 deaths, air pollution, and economic growth in China (Hubei province, the epicenter of the COVID-19 pandemic). We collected daily data at a city level from January 20 to July 31, 2020. We used main cities in the Hubei province, with data on confirmed COVID-19 deaths, air pollution (expressed in µg/m3 as PM2.5, PM10, and CO2), and per capita economic growth. Following the most recent contributions on the relationship among air pollution, GDP, and diffusion of COVID-19, we generated an algorithm capable of identifying a neural connection among these variables. The results confirmed a strong predictive relationship for the Hubei area between changes in the economic growth, fine particles, and deaths from COVID-19. These results would recommend adequate environmental reforms to policymakers to contain the spread and adverse effects of the virus. Therefore, there is a requirement to reconsider the system of transport and return to production by combining it with economic growth to protect the planet

Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM2.5, PM10 and CO2) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM2.5, PM10 and CO2 which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results

Renewable Energy Consumption: the Effects on Economic Growth in Mexico

This study will demonstrate, through an econometric approach, the renewable energy consumption-economic growth effects in Mexico over the period 1990-2017. After a premise where we describe the situation of energy demand and consumption in Mexico and a summary of the economic literature, we have applied various econometric tests. Results about unit root tests describe a situation with all variables that aren't stationary except that in first differences. The Toda and Yamamoto approach is very important in our analysis: it highlights the existence of a unidirectional causal flow, running from renewable energy consumption to aggregate income. This situation respects the theory and hypothesis of economic growth. Keywords: Renewable Energy Consumption, Economic Growth, Causality, Mexico JEL Classifications: B22, C32, N54, Q43 DOI: https://doi.org/10.32479/ijeep.746

The nexus between COVID-19 deaths, air pollution and economic growth in New York state : Evidence from Deep Machine Learning

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A Pedagogical Intrinsic Approach to Relative Entropies as Potential Functions of Quantum Metrics: the qq-zz Family

The so-called $q$-z-\textit{R\'enyi Relative Entropies} provide a huge two-parameter family of relative entropies which includes almost all well-known examples of quantum relative entropies for suitable values of the parameters. In this paper we consider a log-regularized version of this family and use it as a family of potential functions to generate covariant $(0,2)$ symmetric tensors on the space of invertible quantum states in finite dimensions. The geometric formalism developed here allows us to obtain the explicit expressions of such tensor fields in terms of a basis of globally defined differential forms on a suitable unfolding space without the need to introduce a specific set of coordinates. To make the reader acquainted with the intrinsic formalism introduced, we first perform the computation for the qubit case, and then, we extend the computation of the metric-like tensors to a generic $n$-level system. By suitably varying the parameters $q$ and $z$, we are able to recover well-known examples of quantum metric tensors that, in our treatment, appear written in terms of globally defined geometrical objects that do not depend on the coordinates system used. In particular, we obtain a coordinate-free expression for the von Neumann-Umegaki metric, for the Bures metric and for the Wigner-Yanase metric in the arbitrary $n$-level case.Comment: 50 pages, 1 figur

Revisiting the Dynamic Interactions between Economic Growth and Environmental Pollution in Italy: Evidence from a Gradient Descent Algorithm

Although the literature on the relationship between economic growth and CO2 emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960–2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided

Waste generation, wealth and GHG emissions from the waste sector : Is Denmark on the path towards circular economy?

Municipal solid waste (MSW) is one of the most urgent issues associated with economic growth and urban population. When untreated, it generates harmful and toxic substances spreading out into the soils. When treated, they produce an important amount of Greenhouse Gas (GHG) emissions directly contributing to global warming. With its promising path to sustainability, the Danish case is of high interest since estimated results are thought to bring useful information for policy purposes. Here, we exploit the most recent and available data period (1994–2017) and investigate the causal relationship between MSW generation per capita, income level, urbanization, and GHG emissions from the waste sector in Denmark. We use an experiment based on Artificial Neural Networks and the Breitung-Candelon Spectral Granger-causality test to understand how the variables, object of the study, manage to interact within a complex ecosystem such as the environment and waste. Through numerous tests in Machine Learning, we arrive at results that imply how economic growth, identifiable by changes in per capita GDP, affects the acceleration and the velocity of the neural signal with waste emissions. We observe a periodical shift from the traditional linear economy to a circular economy that has important policy implications.publishedVersionUnit Licence Agreemen