The Statistical Implications of a System of Simultaneous Equations

By T. Haavelmo. 1943

Subfields: Statistics and Probability, Economics & Operations Research

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Citations in the corpus

Causal Inference through a Witness Protection Program (2014, methodology): “ot; that removes incoming edges to X. Spirtes et al. (2000) provide an account of the rst graphical methods applying this idea, which are related to the overriding of structural equations proposed by Haavelmo (1943). Notice that if Uis observed in the dataset, then we can obtain the distribution P(Y”
Learning explanations that are hard to vary (2020, background): “This postulate has appeared in various forms in the literature [Haavelmo, 1943, Simon, 1953, Hurwicz, 1962, Pearl, 2009, Schölkopf et al., 2012].14 Based on this insight, it was proposed that regression based on causal features should presents desirable invariance and robustness properties [Mooij…”
External Validity: From Do-Calculus to Transportability Across Populations (2014, methodology): “This is done through a mathematical operator called do( x ), which simulates physical interventions by deleting certain functions from the model, replacing them with a constant X = x , while keeping the rest of the model unchanged (Haavelmo (1943); Strotz and Wold (1960); Pearl (2014)).”
Causal inference in statistics: An overview (2009, neutral): “Such an extension, based on simulating hypothetical interventions in the model, was proposed in (Haavelmo, 1943; Strotz and Wold, 1960; Spirtes et al., 1993; Pearl, 1993a, 2000a; Lindley, 2002) and has led to new ways of defining and estimating causal effects in nonlinear and nonparametric models…”
Better Late than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009) (2010, methodology): “…causal questions as comparisons of unit-level potential outcomes.9 Although this framework is a substantial departure from the Cowles Commission general set up of simultaneous equations models, it is closely related to the interpretation of structural equations in, for example, Haavelmo (1943).”
TRYGVE HAAVELMO AND THE EMERGENCE OF CAUSAL CALCULUS (2014, unknown): “Econometric Theory, 31, 2015, 152–179. doi:10.1017/S0266466614000231 TRYGVE HAAVELMO AND THE EMERGENCE”
Multivariate Statistical Analysis (2011, background): “The result obtained by T. Haavelmo (1943) gave an important impetus.”
Post Walrasian Macroeconomics: Confronting the Economic Model with the Data (2006, methodology): “When taking an economic model to the data in order to conduct inference ‘we need a stochastic formulation to make simplified relations elastic enough for applications’, to quote Haavelmo (1943).”
1. The Scientific Model of Causality (2005, methodology): “An example of fixing versus conditioning is most easily illu- strated in a linear regression model of the type analyzed by Haavelmo (1943).”
The Life-Cycle Benefits of an Influential Early Childhood Program (2016, unknown): “(11)See Heckman (1992), Heckman et al. (2000), and Kline and Walters (2016).”
The Causal Foundations of Structural Equation Modeling (2012, background): “Encounters The founding fathers of SEM, from Sewall Wright (1923) and the early econometricians (Haavelmo, 1943; Simon, 1953; Marschak, 1950; Koopmans, 1953), to Blalock (1964) and Duncan (1975) have all considered SEM a mathematical tool for drawing causal conclusions from a combination of…”
An Extended Class of Instrumental Variables for the Estimation of Causal Effects (2011, methodology): “As we show, the rst case accords with the classical indirect least squares (ILS) interpretation for IV (Haavelmo, 1943, 1944).”
On the Testability of Causal Models With Latent and Instrumental Variables (1995, background): “The two defining attributes of structural equations which set them apart from ordinary algebraic equa tions are autonomy [Haavelmo 1943] and asymmetry [Simon 1953].”