Trygve Haavelmo

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Trygve Magnus Haavelmo
Trygve Haavelmo.jpg
Born(1911-12-13)13 December 1911
Died26 July 1999(1999-07-26) (aged 87)
Nationality Norway
Institution University of Aarhus
University of Chicago
University of Oslo
University College London
Field Macroeconomics, econometrics
School or
tradition
Neo-Keynesian economics
Alma mater University of Oslo
Influences John Maynard Keynes
Ragnar Frisch
Jan Tinbergen
ContributionsProbability approach in econometrics
Balanced budget multiplier
Awards Nobel Memorial Prize in Economic Sciences (1989)
Information at IDEAS / RePEc

Trygve Magnus Haavelmo (13 December 1911 – 28 July 1999), born in Skedsmo, Norway, was an economist whose research interests centered on econometrics. He received the Nobel Memorial Prize in Economic Sciences in 1989.

Contents

Biography

After attending Oslo Cathedral School, [1] Haavelmo received a degree in economics from the University of Oslo in 1930 and eventually joined the Institute of Economics with the recommendation of Ragnar Frisch. Haavelmo was Frisch's assistant for a period of time until he was appointed as head of computations for the institute. In 1936, Haavelmo studied statistics at University College London while he subsequently traveled to Berlin, Geneva, and Oxford for additional studies. [2] Haavelmo assumed a lecturing position at the University of Aarhus in 1938 for one year and then in the subsequent year was offered an academic scholarship to travel abroad and study in the United States. During World War II he worked with Nortraship in the Statistical Department in New York City. He received his PhD in 1946 for his work on The Probability Approach in Econometrics.

He was a Professor of economics and statistics at the University of Oslo between 1948–79 and was the trade department head of division from 1947–48. Haavelmo acquired a prominent position in modern economics through his logical critique of a series of custom conceptions in mathematical analysis.

In 1989, Haavelmo was awarded the Nobel Prize in Economics "for his clarification of the probability theory foundations of econometrics and his analyses of simultaneous economic structures." [3]

Haavelmo resided at Østerås in Bærum. [4] He died on 28 July 1999 in Oslo.

Legacy

Judea Pearl wrote "Haavelmo was the first to recognize the capacity of economic models to guide policies" and "presented a mathematical procedure that takes an arbitrary model and produces quantitative answers to policy questions". According to Pearl, "Haavelmo's paper, 'The Statistical Implications of a System of Simultaneous Equations', [5] marks a pivotal turning point, not in the statistical implications of econometric models, as historians typically presume, but in their causal counterparts." [6] Haavelmo's idea that an economic model depicts a series of hypothetical experiments and that policies can be simulated by modifying equations in the model became the basis of all currently used formalisms of econometric causal inference.[ citation needed ] (The biostatistics and epidemiology literature on causal inference draws from different sources. [7] ) It was first operationalized by Robert H. Strotz and Herman Wold (1960) [8] who advocated "wiping out" selected equations, and then translated into graphical models as "wiping out" incoming arrows. [9] [10] This operation has subsequently led to Pearl's "do"-calculus [11] [12] and to a mathematical theory of counterfactuals in econometric models. [13] [14] Pearl further speculates that the reason economists do not generally appreciate these revolutionary contributions of Haavelmo is because economists themselves have still not reached consensus of what an economic model stands for, as attested by profound disagreements among econometric textbooks. [15]

Related Research Articles

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 Frisch Norwegian economist

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.

Judea Pearl Computer scientist

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 Form of causal modeling that fit networks of constructs to data

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.

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Causal model

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.

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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.

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<i>Causality</i> (book)

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.

References

  1. "Oslo Katedralskole - en skole med tradisjoner". Om skolen (in Norwegian). Oslo katedralskole. Archived from the original on 19 April 2014. Retrieved 19 April 2014.
  2. "Trygve Haavelmo". University of Oslo. Retrieved 4 January 2016.
  3. Prokesch, Steven (12 October 1989). "Norwegian Wins Nobel For His Work in Economics". The New York Times.
  4. "Sky Nobelpros-vinner stakk fra pressen" (in Norwegian). Norwegian News Agency. 11 October 1989.
  5. Haavelmo, T. (1943). "The statistical implications of a system of simultaneous equations". Econometrica. Reprinted in D.F. Hendry and M.S. Morgan (Eds.), The Foundations of Econometric Analysis, Cambridge University Press, New York, 440–453, 1995. 11 (1): 1–43. doi:10.2307/1905714. JSTOR   1905714.
  6. Pearl, Judea (2015). "Trygve Haavelmo and the Emergence of Causal Calculus". Econometric Theory . Forthcoming: 152–179. CiteSeerX   10.1.1.687.1860 . doi:10.1017/S0266466614000231. S2CID   232151859.
  7. Rubin, Donald (2005). "Causal inference using potential outcomes: Design, modeling, decisions". Journal of the American Statistical Association . 100 (469): 322–331. doi:10.1198/016214504000001880. S2CID   842793.
  8. Strotz, R.H.; Wold, H.O.A. (1960). "Recursive versus nonrecursive systems: An attempt at synthesis". Econometrica. 28 (2): 417–427. doi:10.2307/1907731. JSTOR   1907731. S2CID   6584147.
  9. Pearl, Judea (1993). "Comment: Graphical models, causality, and intervention". Statistical Science. 8 (3): 266–269. doi: 10.1214/ss/1177010894 .
  10. Spirtes, P.; Glymour, C. N.; Scheines, R. (1993). Causation, prediction, and search. New York, NY: Springer-Verlag.
  11. Pearl, Judea (1994). Lopez de Mantaras, R.; Poole, D. (eds.). "A probabilistic calculus of actions". Uncertainty in Artificial Intelligence 10: 454–462. arXiv: 1302.6835 .
  12. Pearl, Judea (2000). Causality: Models, Reasoning, and Inference (2nd (2009) ed.). New York, NY: Cambridge University Press.
  13. Balke, Alex; Pearl, Judea (1995). Besnard, P.; Hanks, S. (eds.). "Counterfactuals and policy analysis in structural models". Uncertainty in Artificial Intelligence 11: 11–18.
  14. Pearl, Judea (2009). Causality: Models, Reasoning, and Inference. Chapter 7 (2nd ed.). New York, NY: Cambridge University Press.
  15. Chen, Bryant; Pearl, Judea (2013). "Regression and Causation: A Critical Examination of Six Econometrics Textbooks" (PDF). Real-World Economics Review. 65: 2–20.
Awards
Preceded by
Maurice Allais
Laureate of the Nobel Memorial Prize in Economics
1989
Succeeded by
Harry M. Markowitz
Merton H. Miller
William F. Sharpe