Macro level

Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as economic or other resource transfer interactions over a large population. Diagram: section of a large-scale social network Large-scale networks: Large-scale network is a term somewhat synonymous with "macro-level" as used, primarily, in social and behavioral sciences, in economics. Originally, the term was used extensively in the computer sciences (see large-scale network mapping). Complex networks: Most larger social networks display features of social complexity, which involves substantial non-trivial features of network topology, with patterns of complex connections between elements that are neither purely regular nor purely random (see, complexity science, dynamical system and chaos theory), as do biological, and technological networks. Such complex network features include a heavy tail in the degree distribution, a high clustering coefficient, assortativity or disassortativity among vertices, community structure, and hierarchical structure. In the case of agency-directed networks these features also include reciprocity, triad significance profile (TSP, see network motif), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as lattices and random graphs, do not show these features. Following the development of Keynesian economics, applied economics began developing forecasting models based on economic data including national income and product accounting data. In contrast with typical textbook models, these large-scale macroeconometric models used large amounts of data and based forecasts on past correlations instead of theoretical relations. These models estimated the relations between different macroeconomic variables using time series analysis. These models grew to include hundreds or thousands of equations describing the evolution of hundreds

or thousands of prices and quantities over time, making computers essential for their solution. While the choice of which variables to include in each equation was partly guided by economic theory (for example, including past income as a determinant of consumption, as suggested by the theory of adaptive expectations), variable inclusion was mostly determined on purely empirical grounds. Large-scale macroeconometric model consists of systems of dynamic equations of the economy with the estimation of parameters using time-series data on a quarterly to yearly basis. Macroeconometric models have a supply and a demand side for estimation of these parameters. Authors Kydland and Prescott (1991) call it the system of equations approach. Large-scale macroeconometric model can be defined as a set of stochastic equations with definitional and institutional relationships denoting the behaviour of economic agents. The supply side determines the steady state properties of the macroeconometric model. The macroeconometric model designed by the model builder is significantly influenced by his interests, information, purpose behind its construction, time and financial constraints in the research. The size and nature of the model will change because of the above considerations while building the same. According to Pesaran and Smith (1985) the macroeconometric model must have three basic characteristics viz. relevance, adequacy and consistency. Relevance means the model must be according to the requirements of the desired output. Consistency will expect the model to be inline with the existing theory and inner working of the described system. Adequacy explains the model to be better in terms of its predictive performance. The main objective of the model decides its size. In the current scenario there is an increasing interest in the use of these large-scale macroeonometric models for theory evaluation, impact analysis, policy simulation and forecasting purposes.