Econometrics in the context of Chemometrics

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.

The Mathematical Sciences are a group of areas of study that includes, in addition to mathematics, those academic disciplines that are primarily mathematical in nature but may not be universally considered subfields of mathematics proper.

Statistics, for example, is mathematical in its methods but grew out of bureaucratic and scientific observations, which merged with inverse probability and then grew through applications in some areas of physics, biometrics, and the social sciences to become its own separate, though closely allied, field. Theoretical astronomy, theoretical physics, theoretical and applied mechanics, continuum mechanics, mathematical chemistry, actuarial science, computer science, computational science, data science, operations research, quantitative biology, control theory, econometrics, geophysics and mathematical geosciences are likewise other fields often considered part of the mathematical sciences.

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.

In statistics and control theory, Kalman filtering (also known as linear quadratic estimation) is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, to produce estimates of unknown variables that tend to be more accurate than those based on a single measurement, by estimating a joint probability distribution over the variables for each time-step. The filter is constructed as a mean squared error minimiser, but an alternative derivation of the filter is also provided showing how the filter relates to maximum likelihood statistics. The filter is named after Rudolf E. Kálmán.

Kalman filtering has numerous technological applications. A common application is for guidance, navigation, and control of vehicles, particularly aircraft, spacecraft and ships positioned dynamically. Furthermore, Kalman filtering is much applied in time series analysis tasks such as signal processing and econometrics. Kalman filtering is also important for robotic motion planning and control, and can be used for trajectory optimization. Kalman filtering also works for modeling the central nervous system's control of movement. Due to the time delay between issuing motor commands and receiving sensory feedback, the use of Kalman filters provides a realistic model for making estimates of the current state of a motor system and issuing updated commands.

In econometrics, endogeneity broadly refers to situations in which an explanatory variable is correlated with the error term.

In simplest terms, endogeneity means that a factor or cause one uses to explain something as an outcome is also being influenced by that same thing. For example, education can affect income, but income can also affect how much education someone gets. When this happens, one's analysis might wrongly estimate cause and effect. The thing one thinks is causing change is also being influenced by the outcome, making the results unreliable.

Economic analysis of climate change uses economic tools and models to calculate the scale and distribution of damages caused by climate change. It can also give guidance for the best policies for mitigation and adaptation to climate change from an economic perspective. There are many economic models and frameworks. For example, in a cost–benefit analysis, the trade offs between climate change impacts, adaptation, and mitigation are made explicit. For this kind of analysis, integrated assessment models (IAMs) are useful. Those models link main features of society and economy with the biosphere and atmosphere into one modelling framework.

In general, climate damages increase the more the global surface temperature increases. Many effects of climate change are linked to market transactions and therefore directly affect metrics like GDP or inflation. For instance, climate change can drive inflation in food via heat and droughts, but also drives up overall inflation. There are also non-market impacts which are harder to translate into economic costs. These include the impacts of climate change on human health, biomes and ecosystem services.

Capital formation is a concept used in macroeconomics, national accounts and financial economics. Occasionally it is also used in corporate accounts. It can be defined in three ways:

• It is a specific statistical concept, also known as net investment, used in national accounts statistics, econometrics and macroeconomics. In that sense, it refers to a measure of the net additions to the (physical) capital stock of a country (or an economic sector) in an accounting interval, or, a measure of the amount by which the total physical capital stock increased during an accounting period. To arrive at this measure, standard valuation principles are used.
• It is used also in economic theory, as a modern general term for capital accumulation, referring to the total "stock of capital" that has been formed, or to the growth of this total capital stock.
• In a much broader or vaguer sense, the term "capital formation" has in more recent times been used in financial economics to refer to savings drives, setting up financial institutions, fiscal measures, public borrowing, development of capital markets, privatization of financial institutions, development of secondary markets. In this usage, it refers to any method for increasing the amount of capital owned or under one's control, or any method in utilising or mobilizing capital resources for investment purposes. Thus, capital could be "formed" in the sense of "being brought together for investment purposes" in many different ways. This broadened meaning is not related to the statistical measurement concept nor to the classical understanding of the concept in economic theory. Instead, it originated in credit-based economic growth during the 1990s and 2000s, which was accompanied by the rapid growth of the financial sector, and consequently the increased use of finance terminology in economic discussions.

Abraham Wald (/wɔːld/; German: [valt]; Hungarian: Wald Ábrahám, Yiddish: אברהם וואַלד; (1902-10-31)31 October 1902 – (1950-12-13)13 December 1950) was a Hungarian and American mathematician and statistician who contributed to decision theory, geometry and econometrics, and founded the field of sequential analysis. One of his well-known statistical works was written during World War II on how to minimize the damage to bomber aircraft and took into account the survivorship bias in his calculations. He spent his research career at Columbia University. He was the grandson of Rabbi Moshe Shmuel Glasner.

Irving Fisher (February 27, 1867 – April 29, 1947) was an American economist, statistician, inventor, eugenicist and progressive social campaigner. He was one of the earliest American neoclassical economists, though his later work on debt deflation has been embraced by the post-Keynesian school. Joseph Schumpeter described him as "the greatest economist the United States has ever produced", an assessment later repeated by James Tobin and Milton Friedman.

Fisher made important contributions to utility theory and general equilibrium. He was also a pioneer in the rigorous study of intertemporal choice in markets, which led him to develop a theory of capital and interest rates. His research on the quantity theory of money inaugurated the school of macroeconomic thought known as "monetarism". Fisher was also a pioneer of econometrics, including the development of index numbers. Some concepts named after him include the Fisher equation, the Fisher hypothesis, the international Fisher effect, the Fisher separation theorem and Fisher market.

In economics and econometrics, the Cobb–Douglas production function is a particular functional form of the production function, widely used to represent the technological relationship between the amounts of two or more inputs (particularly physical capital and labor) and the amount of output that can be produced by those inputs. The Cobb–Douglas form was developed and tested against statistical evidence by Charles Cobb and Paul Douglas between 1927 and 1947; according to Douglas, the functional form itself was developed earlier by Philip Wicksteed.

In an economic model, an exogenous variable is one whose measure is determined outside the model and is imposed on the model, and an exogenous change is a change in an exogenous variable. In contrast, an endogenous variable is a variable whose measure is determined by the model. An endogenous change is a change in an endogenous variable in response to an exogenous change that is imposed upon the model.

The term 'endogeneity' in econometrics has a related but distinct meaning. An endogenous random variable is correlated with the error term in the econometric model, while an exogenous variable is not.