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Macroeconomic Responses of Emerging Market Economies to Oil Price Shocks: Analysis by Region and Resource Profile
Togonidze, S. ; Kočenda, Evžen
This study employs a vector autoregressive (VAR) model to analyse how oil price shocks affect macroeconomic fundamentals in emerging economies. Findings from existing literature remain inconclusive how macroeconomic variables fare towards shocks, especially in emerging economies. The objective of our study is to uncover if analysis by region (Latin America and the Caribbean, East Asia and the Pacific, Europe, and Central Asia) and resource intensity of economies (oil exporters, oil importers, minerals exporters, and less resource intensive). Our unique approach forms part of our contribution to the literature. We find that Latin America and the Caribbean are least affected by oil price shocks, while in East Asia and the Pacific the response of inflation and interest rate to oil price shocks is positive, and output growth is negative. Our analysis by resource endowment fails to show oil price shocks’ ability to explain huge variations in macroeconomic variables in oil importing economies. Further sensitivity analysis using US interest rates as an alternative source of external shocks to emerging economies establishes a significant response of interest rate responses to US interest rate in Europe and Central Asia, and in inflation in Latin America and the Caribbean. We also find that regardless of resource endowment, the response of output growth and capital to a positive US interest rate shock is negative and significant in EMs. Our results are persuasive that resource intensity and regional factors impact the responsiveness of emerging economies to oil price shocks, thus laying a basis for policy debate.\n
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Probabilistic representation of spatial fuzzy sets
Soukup, Lubomír
Membership function of a given fuzzy set is expressed by probability that a point belongs in the fuzzy set. Such a membership function is derived from probability distribution of points on the boundary of the fuzzy set. Polygonal boundary is considered. Spatial operations (conjunction, disjunction, complement) are defined accordingly. Several application areas are mentioned, namely classification of land cover, cadastral mapping, material quality analysis, interferometric monitoring of bridges.
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On a class of biped underactuated robot models with upper body: Sensitivity analysis of the walking performance
Papáček, Štěpán ; Polach, P. ; Prokýšek, R. ; Anderle, Milan
Biped underactuated robots with an upper body (being a torso) form a subclass of legged robots. This study deals with the walking performance of such class of legged robot models and has been motivated by the need to implement of the previously developed sensor and control algorithms for the real-time movement of the laboratory walking robot, designed and built at the Department of Control Theory of the Institute of Information Theory and Automation (UTIA) of the Czech Academy of Sciences, see Fig. 1 (left). A detailed description of this underactuated walking-like mechanical system (called further UTIA Walking Robot – UWR) is provided in [2] and [5]. The simplest underactuated walking robot hypothetically able to walk is the so-called Compass gait biped walker, alternatively called the Acrobot, see Fig. 1 (right). For a review of underactuated mechanical systems, i.e. systems with fewer actuators than degrees of freedom, which encounter many applications in different fields (e.g., in robotics, in aeronautical and spatial systems, in marine and underwater systems, and in-flexible and mobile systems), see [3]. As follows, we examine the walking performance of parametrized models for different walking regimes and different values of model parameters. More specifically, the sensitivity analysis (i.e., parameter study) of the walking performance with respect to certain design variables (model parameters) is carried out using the software package alaska/MultibodyDynamics. The main attention is attracted to the role of the upper body mass m3 and position lc3, see Fig. 1 (right). Last but not least, having surveyed the mechanics of planar biped robots, our subsequent goal is the analysis of a 3D biped model where lateral balance is either controlled, suppressed or compensated.
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Multimodal data fusion in remote sensing
Greško, Šimon ; Zitová, Barbara
With fast development of remote sensing technologies, demand for remote sensing data started to be increasing. Providing space resolution of these data may not be sufficient for target application. For that reason the methods for fusing remote sensing images were developed. This issue appears in the question of demand for high resolution thermal data. Sentinel-3 provides free remote sensing thermal data with space resolution 1000x1000 m, which may be insufficient for some specialized applications. In this thesis, standard methods of image fusion are presented as well as specialized methods for thermal sharpening, their modifications, implementation and results including validation and comparison.
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Diffusion Kalman filtering under unknown process and measurement noise covariance matrices
Vlk, T. ; Dedecius, Kamil
The state-of-the-art algorithms for Kalman filtering in agent networks with information diffusion impose the requirement of well-defined state-space models. In particular, they assume that both the process and measurement noise covariance matrices are known and properly set. This is a relatively strong assumption in the signal processing domain. By design, the Kalman filters are rather sensitive to its violation, which may potentially lead to their divergence. In this paper, we propose a novel distributed filtering algorithm with increased robustness under unknown process and measurement noise covariance matrices. It is formulated as a Bayesian variational message passing procedure for simultaneous analytically tractable inference of states and measurement noise covariance matrices.
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Analysis of Impact of Covariates Entering Stochastic Optimization Problem
Volf, Petr
In the contribution we study consequences of imperfect information to precision of stochastic optimization solution. In particular, it is assumed that the characteristics of optimization problem are influenced by a set of covariates. This dependence is described via a regression model. Hence, the uncertainty is then caused by statistical estimation of regression parameters. The contribution will analyze several regression model cases, together with their application. Precision of results will be explored, both theoretically as well as with the aid of simulations.
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Modeling COVID Pandemics: Strengths and Weaknesses of Epidemic Models
Šmíd, Martin
We generally discuss modeling the present COVID pandemics. We argue that useful models have to be simple in the first case, yet their uncertainty has to be handled properly. In order to study circumstances of the upcoming wave of infection,\nwe construct a simple stochastic model and present predictions it gives. We conclude that the autumn wave is most likely unavoidable and suggest concentrating to mitigation.
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Modeling the spread of loanwords in South-East Asia using sailing navigation software and Bayesian networks
Kratochvíl, F. ; Kratochvíl, Václav ; Saad, G. ; Vomlel, Jiří
A loanword is a word permanently adopted from one language and incorporated into another language without translation. In this paper, we study loanwords in the South-East Asia Archipelago, home to a large number of languages. Our paper is inspired by the works of Hoffmann et al. (2021) Bayesian methods are applied to probabilistic modeling of family trees representing the history of language families and by Haynie et al. (2014) modeling the diffusion of a special class of loanwords, so-called Wanderw ̈orter in languages of Australia, North America, and South America. We assume that in the South-East Asia Archipelago Wanderwörter spread along specific maritime trade routes whose geographical characteristics can help unravel the history of Wanderwörter diffusion in the area. For millennia trade was conducted using sailing ships which were constrained by the monsoon system and in certain areas also by strong sea currents. Therefore rather than the geographical distances, the travel times of sailing ships should be considered as a major factor determining the intensity of contact among cultures. We use sailing navigation software to estimate travel times between different ports and show that the estimated travel times correspond well to the travel times of a Chinese map of the sea trade routes from the early seventeenth century. We model the spread of loanwords using a probabilistic graphical model - a Bayesian network. We design a novel heuristic Bayesian network structure learning algorithm that learns the structure as a union of spanning trees for graphs of all loanwords in the training dataset. We compare this algorithm with BIC optimal Bayesian networks by measuring how well these models predict the true presence/absence of a loanword. Interestingly, Bayesian networks learned by our heuristic spanning tree-based algorithm provide better results than the BIC optimal Bayesian networks.
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Two Composition Operators for Belief Functions Revisited
Jiroušek, Radim ; Kratochvíl, Václav ; Shenoy, P. P.
In probability theory, compositional models are as powerful as Bayesian networks. However, the relation between belief-function graphical models and the corresponding compositional models is much more complicated due to several reasons. One of them is that there are two composition operators for belief functions. This paper deals with their main properties and presents sufficient conditions under which they yield the same results.
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