Econometrics in the context of "Mathematical science"

An economist is a professional and practitioner in the social science discipline of economics.

The individual may also study, develop, and apply theories and concepts from economics and write about economic policy. Within this field there are many sub-fields, ranging from the broad philosophical theories to the focused study of minutiae within specific markets, macroeconomic analysis, microeconomic analysis or financial statement analysis, involving analytical methods and tools such as econometrics, statistics, economics computational models, financial economics, regulatory impact analysis and mathematical economics.

In statistics and econometrics, panel data and longitudinal data are both multi-dimensional data involving measurements over time. Panel data is a subset of longitudinal data where observations are for the same subjects each time.

Time series and cross-sectional data can be thought of as special cases of panel data that are in one dimension only (one panel member or individual for the former, one time point for the latter). A literature search often involves time series, cross-sectional, or panel data.

Agricultural economics is an applied field of economics concerned with the application of economic theory in optimizing the production and distribution of food and fiber products. Agricultural economics began as a branch of economics that specifically dealt with land usage. It focused on maximizing the crop yield while maintaining a good soil ecosystem. Throughout the 20th century the discipline expanded and the current scope of the discipline is much broader. Agricultural economics today includes a variety of applied areas, having considerable overlap with conventional economics. Agricultural economists have made substantial contributions to research in economics, econometrics, development economics, and environmental economics. Agricultural economics influences food policy, agricultural policy, and environmental policy.

In economics, the credibility revolution was the movement towards more rigorous empirical analysis. The movement sought to test economic theory and focused on causative econometric modeling and the use of experimental and quasi experimental methods. These more advanced statistical methods gave economists the ability to make causal claims, as the discipline shifted towards a potential outcome framework.

The revolution began in the 1960s when governments began to ask economists to use their skills in economic modeling, econometrics and research design to collect and analyze government data to improve policy making and enforcement of laws. A good example is research on discrimination carried out by the Equal Employment Opportunity Commission (EEOC). Grounded in legally required data from all US employers with 100 or more employees, economists, led by Phyllis Wallace, showed systematic discrimination in employment by race and sex. Their work led to successful discrimination cases in the utility, pharmaceutical and textile industries. Francine Blau and others continued to use EEOC and other data to more rigorously test for wage differentials and occupational segregation by race and sex.

Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or contexts. Typically, it attempts to use discrete choices (A over B; B over A, B & C) in order to infer positions of the items (A, B and C) on some relevant latent scale (typically "utility" in economics and various related fields). Indeed many alternative models exist in econometrics, marketing, sociometrics and other fields, including utility maximization, optimization applied to consumer theory, and a plethora of other identification strategies which may be more or less accurate depending on the data, sample, hypothesis and the particular decision being modelled. In addition, choice modelling is regarded as the most suitable method for estimating consumers' willingness to pay for quality improvements in multiple dimensions.

In economic theory and econometrics, the term heterogeneity refers to differences across the units being studied. For example, a macroeconomic model in which consumers are assumed to differ from one another is said to have heterogeneous agents.

In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.

A time series is very frequently plotted via a run chart (which is a temporal line chart). Time series are used in statistics, actuarial science, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.