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Aggregate data are applied in statistics, data warehouses, and in economics. There is a distinction between aggregate data and individual data. Aggregate data refers to individual data that are averaged by geographic area, by year, by service agency, or by other means. [ 2 ]
Input–output economics has been used to study regional economies within a nation, and as a tool for national and regional economic planning. A main use of input–output analysis is to measure the economic impacts of events as well as public investments or programs as shown by IMPLAN and Regional Input–Output Modeling System. It is also ...
Data aggregation is the compiling of information from databases with intent to prepare combined datasets for data processing. [1] Description
Research in microfoundations explores the link between macroeconomic and microeconomic principles in order to explore the aggregate relationships in macroeconomic models. During recent decades, macroeconomists have attempted to combine microeconomic models of individual behaviour to derive the relationships between macroeconomic variables.
Economic data are data describing an actual economy, past or present. These are typically found in time-series form, that is, covering more than one time period (say the monthly unemployment rate for the last five years) or in cross-sectional data in one time period (say for consumption and income levels for sample households).
Data envelopment analysis (DEA) is a nonparametric method in operations research and economics for the estimation of production frontiers. [1] DEA has been applied in a large range of fields including international banking, economic sustainability, police department operations, and logistical applications [2] [3] [4] Additionally, DEA has been used to assess the performance of natural language ...
The aggregation problem is the difficult problem of finding a valid way to treat an empirical or theoretical aggregate as if it reacted like a less-aggregated measure, say, about behavior of an individual agent as described in general microeconomic theory [1] (see representative agent and heterogeneity in economics). The second meaning of ...
Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of data wrangling is to assure quality and useful data.