Functional Decomposition Approach Research Articles

Domestic Borrowing and the Nigerian Economy: A Bayesian VAR Analysis

ABSTRACT: The Nigerian government has borrowed for more than three decades to fund the growing fiscal deficit based on a weak tax revenue base. After the repayment of the country's foreign loans in 2006, the borrowing structure has been dominated by domestic loans. Between 2006 and 2020, domestic borrowing has grown by more than 800 per cent. This study examines the effect of domestic borrowing on Nigeria's macroeconomic variables from 1975 to 1980. The variables examined against the effect of domestic borrowing include economic growth, financial development (broad money supply and credit to private sector), external borrowing, interest rate, inflation, industrial production, private investment, trade openness and population growth rate. The study utilised the Bayesian Vector Autoregressive (BVAR) model. The BVAR approach gives more realistic estimations than other VAR models as it considers prior information steady states during analysis. The study also adopts the Cholesky impulse response function and variance decomposition approach in the investigation. The study found a long-run equilibrium relationship between domestic borrowing and other macroeconomic variables. Also, domestic borrowing positively and significantly affected economic growth, industrial production and inflation. However, domestic borrowing negatively and significantly affects trade openness. Although the effect of domestic borrowing on the financial development variables (broad money supply and credit to the private sector) is negative, it is insignificant. The Bayesian VAR impulse response simulation demonstrates that the fiscal policy shock from domestic borrowing impacts the macroeconomic variables in line with the estimated regression results. As a follow-up, the variance decomposition results show that the decomposition of the shocks from government domestic borrowing is borne more by external borrowing, broad money supply and interest rate in the order of magnitude. The study recommends a restriction in monetary policy to benchmark and closely checkmate the aggressive spending from domestic borrowing. Also, the Nigerian government should improve investment in infrastructure to enhance private sector productivity and expand taxes revenue base to mitigate the reliance on borrowing as a source of funding for fiscal deficit. Furthermore, the country's productivity must be coordinated by the government to expand the sources of foreign exchange earnings to enhance stability in the country's external sector.

Efficiency and accuracy of the domain and functional decomposition strategies for the wave-structure interaction problem

Extreme waves can be modelled in a numerical wave tank (NWT) as a focusing wave in order to generate events that are crucial for designing any offshore structures. The focused wave generation implemented in the completely nonlinear potential solver Higher Order Spectral method (HOS-NWT) with the time-reversal technique was used in the present work. The OpenFOAM based solvers foamStar and foamStarSWENSE which use the domain decomposition and the functional decomposition approach, respectively, were used to couple with a HOS-NWT to perform the focusing wave interaction with the structure (in this case, a cylinder). The incident waves from HOS-NWT are blended into a foamStar and foamStarSWENSE subdomain, where the waves will propagate and interact with the structure within their domain. In the SWENSE methodology, the total field in the computational domain is divided into the incident field and a complementary field. foamStar solves for the total flow fields, and foamStarSWENSE solves for the complementary (total minus incident) flow fields. The Volume of Fluid (VoF) method was selected to capture the interface. 2D parametric studies for focused wave generation were carried out with both solvers, and 3D wave interactions for different mesh types (coarse, medium, and fine) were investigated to understand the convergence for such transient type problems. For verification, the case’s uncertainty was quantified using the Richardson extrapolation approach, and validation was then done by comparing the results to the experiment. The computational efficiency of the two coupling techniques was also compared, and recommendations for solver improvements are made. In general, when solving the problem of wave generation and propagation in 2D and 3D conditions, foamStarSWENSE surpassed foamStar in accuracy. On the other hand, when the simulation included a structure in the domain, the accuracy of the solution in foamStar and foamStarSWENSE was nearly the same, but the foamStar performed better in terms of computational cost. Overall, both coupling approaches generate a realistic depiction of focused wave interaction and force over the cylinder at a minimum computational cost.

Machine learning approaches for structural and thermodynamic properties of a Lennard-Jones fluid.

Predicting the functional properties of many molecular systems relies on understanding how atomistic interactions give rise to macroscale observables. However, current attempts to develop predictive models for the structural and thermodynamic properties of condensed-phase systems often rely on extensive parameter fitting to empirically selected functional forms whose effectiveness is limited to a narrow range of physical conditions. In this article, we illustrate how these traditional fitting paradigms can be superseded using machine learning. Specifically, we use the results of molecular dynamics simulations to train machine learning protocols that are able to produce the radial distribution function, pressure, and internal energy of a Lennard-Jones fluid with increased accuracy in comparison to previous theoretical methods. The radial distribution function is determined using a variant of the segmented linear regression with the multivariate function decomposition approach developed by Craven et al. [J. Phys. Chem. Lett. 11, 4372 (2020)]. The pressure and internal energy are determined using expressions containing the learned radial distribution function and also a kernel ridge regression process that is trained directly on thermodynamic properties measured in simulation. The presented results suggest that the structural and thermodynamic properties of fluids may be determined more accurately through machine learning than through human-guided functional forms.

Robust mode-dependent control for discrete-time singular Markovian jump systems with time-varying delay

This study investigates the problem of robust mode-dependent control for a class of discrete-time singular Markovian jump systems with time-varying delay. Using the Lyapunov functional method and delay decomposition approach, Linear matrix inequality (LMI)-based sufficient conditions for the stochastic stability and robust mode-dependent control are developed, which guarantee the considered systems to be regular, causal and stochastically stabilisable. Finally, numerical examples are presented to demonstrate the effectiveness and advantages of the theoretical results.

Evaluating the Gender Wage Gap in Georgia, 2004 – 2011

This paper evaluates the gender wage gap among wage workers along the wage distribution in Georgia between 2004 and 2011, based on the recentered influence function (RIF) decomposition approach developed in Firpo, Fortin, and Lemieux (2009). We find that the gender wage gap decreases along the wage distribution, from 0.64 log points to 0.54 log points. Endowment differences explain between 22 percent and 61 percent of the observed gender wage gap, with the explained proportion declining as we move to the top of the distribution. The primary contributors are the differences in the work hours, industrial composition, and employment in the state sector. A substantial portion of the gap, however, remains unexplained, and can be attributed to the differences in returns, especially in the industrial premia.The gender wage gap consistently declined between 2004 and 2011. However, the gap remains large, with women earning 45 percent less than men in 2011. The reduction in the gender wage gap between 2004 and 2007, and the switch from a glass-ceiling shape for the gender gap distribution to a sticky-floor shape, was driven by the rising returns in the state sector for men at the bottom, and by women at the top of the wage distribution. Between 2009 and 2011, the decline in the gender wage gap can be explained by the decrease in men’s working hours, which was larger than the decrease in women’s working hours. We assess the robustness of our findings using the statistical matching decomposition method developed in Nopo (2008) in order to address the possibility that the high degree of industrial segregation may bias our results. The Nopo decomposition results enrich our understanding of the factors that underlie the gender wage gap but do not alter our key findings, and in fact support their robustness.

Social-Scientific Modeling in Biblical and Related Studies

This article provides background information about the use of the social sciences and introduces the basic themes and factions in the debate about modeling in the field of biblical and related studies. It argues that some of the controversial issues could be clarified by drawing on the current discussion on modeling in the philosophy of science, especially Uskali Mäki's functional decomposition approach. It also presents a model for analyzing early Jewish and Christian movements at the end of the first century, assessing the model's composition, function and truth-value in the framework of Mäki's approach.

A use-case driven approach to systematic functional decomposition: a case of the biopsy procedure in breast cancer treatment

Functional decomposition is a widely used system modelling technique for understanding complex systems. It is based on functional partitioning of the problem domain and requires identification of a system's functionalities. The identification of a system's functionalities is performed informally by the analyst without any systematic guideline. Approaches which employ this technique have, therefore, difficulty in such identification of functionalities. This paper proposes a use-case driven functional decomposition approach. This approach has two major advantages. Firstly, the identification of a system's functionalities is easier than the classical approaches. Secondly, the system requirements and implementation can be easily validated with the users of the system. It also matches well with common functional decomposition approaches like Structured Analysis as it is also a top-down approach. The approach is presented along with an example of its application. We also provide engineering steps, heuristics and guidelines.

Accounting for income distribution trends: A density function decomposition approach

This paper develops methods for decomposing changes in the income distribution using subgroup decompositions of the income density function. Overall changes are related to changes in subgroup shares and changes in subgroup densities, where the latter are broken down further using elementary transformations of individual incomes. These density decompositions are analogous to the widely-used decompositions of inequality indices by population subgroup, except that they summarize multiple features of the income distribution (using graphs), rather than focusing on a specific feature such as dispersion, and are not dependent on the choice of a specific summary index. Nonetheless, since inequality and poverty indices can be expressed as PDF functionals, our density-based methods can also be used to provide numerical decompositions of these. An application of the methods reveals the multi-faceted nature of UK income distribution trends during the 1980s.

Accounting for Income Distribution Trends: A Density Function Decomposition Approach

This paper develops methods for decomposing changes in the income distribution using subgroup decompositions of the income density function. Overall changes are related to changes in subgroup shares and changes in subgroup densities, where the latter are broken down further using elementary transformations of individual incomes. These density decompositions are analogous to the widely-used decompositions of inequality indices by population subgroup, except that they summarize multiple features of the income distribution (using graphs), rather than focusing on a specific feature such as dispersion, and are not dependent on the choice of a specific summary index. Nonetheless, since inequality and poverty indices can be expressed as PDF functionals, our density-based methods can also be used to provide numerical decompositions of these. An application of the methods reveals the multi-faceted nature of UK income distribution trends during the 1980s.

Learning by discovering concept hierarchies

We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less complex problems. The method is inspired by the Boolean function decomposition approach to the design of switching circuits. To cope with high time complexity of finding an optimal decomposition, we propose a suboptimal heuristic algorithm. The method, implemented in program HINT (Hierarchy INduction Tool), is experimentally evaluated using a set of artificial and real-world learning problems. In particular, the evaluation addresses the generalization property of decomposition and its capability to discover meaningful hierarchies. The experiments show that HINT performs well in both respects.

Modeling User Requirements with a Front-End Analysis

In this paper, we propose a front-end analysis approach to specifying vague requirements and identifying implicit user requirements for early validation. First, functional requirements are arranged into a function-subfunction hierarchy based on functional decomposition approach; whereas, nonfunctional requirements are decomposed into two types of hierarchy: a classification hierarchy through IS-A relationship, and an aggregation hierarchy using IS-PART-OF relationship. Second, constraint networks are built from the hierarchies. Propagation rules are then applied to derive implicit requirements. Finally, the derived requirements are represented using Zadeh's canonical forms14.