Trygve Magnus Haavelmo | |
---|---|

Born | |

Died | 26 July 1999 87) | (aged

Nationality | Norwegian |

Academic career | |

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 |

Contributions | Probability 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.

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.^{[ citation needed ]}

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.^{[ citation needed ]}

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.^{[ citation needed ]}

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] }

**Econometrics** is an 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". 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 an influential Norwegian economist known for being one of the major contributors to establishing economics as a quantitative and statistically informed science in the early 20th century. He coined the term econometrics in 1926 for utilising statistical methods to describe economic systems, as well as the terms microeconomics and macroeconomics in 1933, for describing individual and aggregate economic systems, respectively. He was the first to develop a statistically informed model of business cycles in 1933. Later work on the model together with Jan Tinbergen won the two the first Nobel Memorial Prize in Economic Sciences in 1969.

**Causality** (also called **causation**, or **cause and effect**) is influence by which one event, process, state, or object (*a**cause*) contributes to the production of another event, process, state, or object (an *effect*) 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.

In statistics, **path analysis** is used to describe the directed dependencies among a set of variables. This includes models equivalent to any form of multiple regression analysis, factor analysis, canonical correlation analysis, discriminant analysis, as well as more general families of models in the multivariate analysis of variance and covariance analyses.

**Judea 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.

**Structural equation modeling** (**SEM**) is a label for a diverse set of methods used by scientists doing both observational and experimental research. SEM is used mostly in the social and behavioral sciences but it is also used in epidemiology, business, and other fields. A definition of SEM is difficult without reference to technical language, but a good starting place is the name itself.

In the 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.

**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.

**James M. Robins** is an epidemiologist and biostatistician best known for advancing methods for drawing causal inferences from complex observational studies and randomized trials, particularly those in which the treatment varies with time. He is the 2013 recipient of the Nathan Mantel Award for lifetime achievement in statistics and epidemiology, and a recipient of the 2022 Rousseeuw Prize in Statistics, jointly with Miguel Hernán, Eric Tchetgen-Tchetgen, Andrea Rotnitzky and Thomas Richardson.

**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 emphasize 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.

**Guido Wilhelmus Imbens** is a Dutch-American economist whose research concerns econometrics and statistics. He holds the Applied Econometrics Professorship in Economics at the Stanford Graduate School of Business at Stanford University, where he has taught since 2012.

In statistics, econometrics, epidemiology, genetics and related disciplines, **causal graphs** are probabilistic graphical models used to encode assumptions about the data-generating process.

* 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 2018 nonfiction book by computer scientist Judea Pearl and writer Dana Mackenzie. The book explores the subject of causality and causal inference from statistical and philosophical points of view for a general audience.

- ↑ "Oslo Katedralskole - en skole med tradisjoner".
*Om skolen*(in Norwegian). Oslo katedralskole. Archived from the original on 19 April 2014. Retrieved 19 April 2014. - ↑ "Trygve Haavelmo". University of Oslo. Retrieved 4 January 2016.
- ↑ Prokesch, Steven (12 October 1989). "Norwegian Wins Nobel For His Work in Economics".
*The New York Times*. - ↑ "Sky Nobelpros-vinner stakk fra pressen" (in Norwegian). Norwegian News Agency. 11 October 1989.
- ↑ 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. - ↑ Pearl, Judea (2015). "Trygve Haavelmo and the Emergence of Causal Calculus".
*Econometric Theory*.**31**: 152–179. CiteSeerX 10.1.1.687.1860 . doi:10.1017/S0266466614000231. S2CID 232151859. - ↑ 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. - ↑ 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. - ↑ Pearl, Judea (1993). "Comment: Graphical models, causality, and intervention".
*Statistical Science*.**8**(3): 266–269. doi: 10.1214/ss/1177010894 . - ↑ Spirtes, P.; Glymour, C. N.; Scheines, R. (1993).
*Causation, prediction, and search*. New York, NY: Springer-Verlag. - ↑ 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 . - ↑ Pearl, Judea (2000).
*Causality: Models, Reasoning, and Inference*(2nd (2009) ed.). New York, NY: Cambridge University Press. - ↑ Balke, Alex; Pearl, Judea (1995). Besnard, P.; Hanks, S. (eds.). "Counterfactuals and policy analysis in structural models".
*Uncertainty in Artificial Intelligence 11*: 11–18. - ↑ Pearl, Judea (2009).
*Causality: Models, Reasoning, and Inference*. Chapter 7 (2nd ed.). New York, NY: Cambridge University Press. - ↑ Chen, Bryant; Pearl, Judea (2013). "Regression and Causation: A Critical Examination of Six Econometrics Textbooks" (PDF).
*Real-World Economics Review*.**65**: 2–20.

Wikiquote has quotations related to ** Trygve Haavelmo **.

- List of publications
- Trygve Haavelmo on Nobelprize.org including the Nobel Lecture on 7 December 1989
*Econometrics and the Welfare State* - Model Discovery and Trygve Haavelmo’s Legacy by David F. Hendry and Søren Johansen.]
- Trygve Haavelmo Growth Model by Elmer G. Wiens
- "Trygve Haavelmo (1911–1999)".
*The Concise Encyclopedia of Economics*. Library of Economics and Liberty (2nd ed.). Liberty Fund. 2008. - Pearl, Judea (2014). "Trygve Haavelmo and the Emergence of Causal Calculus" (PDF).
*Forthcoming, Econometric Theory, special issue on Haavelmo Centennial*. UCLA Computer Science Department, Technical Report R-391. - Chen, Bryant; Pearl, Judea (2013). "Regression and Causation: A Critical Examination of Six Econometrics Textbooks" (PDF).
*Real-World Economics Review*.**65**: 2–20.

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