Econometrics is the application of statistical methods to economic data in order to give empirical content to economic relationships. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference". An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships". The first known use of the term "econometrics" was by Polish economist Paweł Ciompa in 1910. Jan Tinbergen is one of the two founding fathers of econometrics. The other, Ragnar Frisch, also coined the term in the sense in which it is used today.
Ragnar Anton Kittil Frisch was a Norwegian economist and the co-recipient of the first Nobel Memorial Prize in Economic Sciences in 1969. He is known for being one of the founders of the discipline of econometrics, and for coining the widely used term pair macroeconomics/microeconomics in 1933.
Causality is influence by which one event, process, state or object contributes to the production of another event, process, state or object where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. In general, a process has many causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future. Some writers have held that causality is metaphysically prior to notions of time and space.
Herman Ole Andreas Wold was a Norwegian-born econometrician and statistician who had a long career in Sweden. Wold was known for his work in mathematical economics, in time series analysis, and in econometric statistics.
Yehuda Pearl is an Israeli-American computer scientist and philosopher, best known for championing the probabilistic approach to artificial intelligence and the development of Bayesian networks. He is also credited for developing a theory of causal and counterfactual inference based on structural models. In 2011, the Association for Computing Machinery (ACM) awarded Pearl with the Turing Award, the highest distinction in computer science, "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning". He is the author of several books, including the technical Causality: Models, Reasoning and Inference, and The Book of Why, a book on causality aimed at the general public.
Econometric models are statistical models used in econometrics. An econometric model specifies the statistical relationship that is believed to hold between the various economic quantities pertaining to a particular economic phenomenon. An econometric model can be derived from a deterministic economic model by allowing for uncertainty, or from an economic model which itself is stochastic. However, it is also possible to use econometric models that are not tied to any specific economic theory.
Wesley Charles Salmon was an American philosopher of science renowned for his work on the nature of scientific explanation. He also worked on confirmation theory, trying to explicate how probability theory via inductive logic might help confirm and choose hypotheses. Yet most prominently, Salmon was a realist about causality in scientific explanation, although his realist explanation of causality drew ample criticism. Still, his books on scientific explanation itself were landmarks of the 20th century's philosophy of science, and solidified recognition of causality's important roles in scientific explanation, whereas causality itself has evaded satisfactory elucidation by anyone.
Structural equation modeling (SEM) includes a diverse set of mathematical models, computer algorithms, and statistical methods that fit networks of constructs to data. SEM includes confirmatory factor analysis, confirmatory composite analysis, path analysis, partial least squares path modeling, and latent growth modeling. The concept should not be confused with the related concept of structural models in econometrics, nor with structural models in economics. Structural equation models are often used to assess unobservable 'latent' constructs. They often invoke a measurement model that defines latent variables using one or more observed variables, and a structural model that imputes relationships between latent variables. The links between constructs of a structural equation model may be estimated with independent regression equations or through more involved approaches such as those employed in LISREL.
The Cowles Foundation for Research in Economics is an economic research institute at Yale University. It was created as the Cowles Commission for Research in Economics at Colorado Springs in 1932 by businessman and economist Alfred Cowles. In 1939, the Cowles Commission moved to the University of Chicago under Theodore O. Yntema. Jacob Marschak directed it from 1943 until 1948, when Tjalling C. Koopmans assumed leadership. Increasing opposition to the Cowles Commission from the department of economics of the University of Chicago during the 1950s impelled Koopmans to persuade the Cowles family to move the commission to Yale University in 1955 where it became the Cowles Foundation.
In philosophy of science, a causal model is a conceptual model that describes the causal mechanisms of a system. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for.
Sir David Forbes Hendry, FBA CStat is a British econometrician, currently a professor of economics and from 2001–2007 was head of the Economics Department at the University of Oxford. He is also a professorial fellow at Nuffield College, Oxford.
Probabilistic causation is a concept in a group of philosophical theories that aim to characterize the relationship between cause and effect using the tools of probability theory. The central idea behind these theories is that causes raise the probabilities of their effects, all else being equal.
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four elements: correlation, sequence in time, a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more artificial or natural experiments.
The methodology of econometrics is the study of the range of differing approaches to undertaking econometric analysis.
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The science of why things occur is called etiology. Causal inference is said to provide the evidence of causality theorized by causal reasoning.
Causation in economics has a long history with Adam Smith explicitly acknowledging its importance via his (1776) An Inquiry into the Nature and Causes of the Wealth of Nations and David Hume and John Stuart Mill (1848) both offering important contributions with more philosophical discussions. Hoover (2006) suggests that a useful way of classifying approaches to causation in economics might be to distinguish between approaches that emphasize structure and those that emphasise process and to add to this a distinction between approaches that adopt a priori reasoning and those that seek to infer causation from the evidence provided by data. He represented by this little table which useful identifies key works in each of the four categories.
In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs are probabilistic graphical models used to encode assumptions about the data-generating process. They can also be viewed as a blueprint of the algorithm by which Nature assigns values to the variables in the domain of interest.
Causality: Models, Reasoning and Inference is a book by Judea Pearl. It is an exposition and analysis of causality. It is considered to have been instrumental in laying the foundations of the modern debate on causal inference in several fields including statistics, computer science and epidemiology. In this book, Pearl espouses the Structural Causal Model (SCM) that uses structural equation modeling. This model is a competing viewpoint to the Rubin causal model. Some of the material from the book was reintroduced in the more general-audience targeting The Book of Why.
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Exploratory causal analysis (ECA), also known as data causality or causal discovery is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions. ECA is a type of causal inference distinct from causal modeling and treatment effects in randomized controlled trials. It is exploratory research usually preceding more formal causal research in the same way exploratory data analysis often precedes statistical hypothesis testing in data analysis
The Book of Why: The New Science of Cause and Effect is a 2019 nonfiction book by Judea Pearl and Dana Mackenzie. Pearl is a computer scientist and philosopher and Mackenzie is a science writer. The book explores the subject of causality and causal inference from statistical and philosophical points of view. The book is aimed at a general audience.
