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176
Bayesian Simultaneous Equations Analysis using Reduced Structures
, 1997
"... Diffuse priors lead to pathological posterior behavior when used in Bayesian analyses of Simultaneous Equation Models (SEMs). This results from the local nonidentication of certain parameters in SEMs. When this, a priori known, feature is not captured appropriately, an a posteriori favor for certain ..."
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Cited by 40 (3 self)
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Diffuse priors lead to pathological posterior behavior when used in Bayesian analyses of Simultaneous Equation Models (SEMs). This results from the local nonidentication of certain parameters in SEMs. When this, a priori known, feature is not captured appropriately, an a posteriori favor for certain specific parameter values results which is not the consequence of strong data information but of local nonidentification. We show that a proper consistent Bayesian analysis of a SEM explicitly has to consider the reduced form of the SEM as a standard linear model on which nonlinear (reduced rank) restrictions are imposed, which result from a singular value decomposition. The priors/posteriors of the parameters of the SEM are therefore proportional to the priors/posteriors of the parameters of the linear model under the condition that the restrictions hold. This leads to a framework for constructing priors and posteriors for the parameters of SEMs. The framework is used to construct priors and pos...
An Extended Class of Instrumental Variables for the Estimation of Causal Effects
 UCSD DEPT. OF ECONOMICS DISCUSSION PAPER
, 1996
"... This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regresso ..."
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Cited by 40 (15 self)
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This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regressors and standard “valid” and “relevant” instrumental variables. We then build on this interpretation to characterize extended instrumental variables (EIV) methods, that is methods that make use of variables that need not be valid instruments in the standard sense, but that are nevertheless instrumental in the recovery of causal effects of interest. After examining special cases of single and double EIV methods, we provide necessary and sufficient conditions for the identification of causal effects by means of EIV and provide consistent and asymptotically normal estimators for the effects of interest.
Using Path Diagrams as a Structural Equation Modelling Tool
, 1997
"... this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling. There are a number of problems associated with structural equation modeling. These problems include: ..."
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Cited by 36 (8 self)
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this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling. There are a number of problems associated with structural equation modeling. These problems include:
Remarks on the Analysis of Causal Relationships in Population Research,” Demography
 Molinari, F. forthcoming. “Missing Treatments.” Journal of Business and Economic Statistics
"... referees for helpful discussions and comments. The problem of determining cause and effect is one of the oldest questions in the social sciences, where laboratory experimentation is generally not possible. This essay provides a perspective on the analysis of causal relationships in population resear ..."
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Cited by 30 (0 self)
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referees for helpful discussions and comments. The problem of determining cause and effect is one of the oldest questions in the social sciences, where laboratory experimentation is generally not possible. This essay provides a perspective on the analysis of causal relationships in population research which draws upon recent discussions of this issue in the field of economics. Within economics, thinking about causal estimation has shifted rather dramatically in the last decade toward a rather more pessimistic reading of what is possible and a retreat in the ambitiousness of claims of causal determination. The framework which underlies this conclusion is presented in this essay, the central identification problem is discussed in some detail, and examples from the field of population research are given. Some of the more important aspects of this framework relate to the problem of the variability of causal effects for different individuals; the relationship between structural forms, reduced forms, and knowledge of mechanisms; the problem of internal vs. external validity and the related issue of extrapolation; and the importance of theory and outside evidence.
Confronting the economic model with the data
 In D. Colander (ed), Post Walrasian Macroeconomics
, 2006
"... Econometrics is about confronting economic models with the data. In doing so it is crucial to choose a statistical model that not only contains the economic model as a submodel, but also contains the data generating process. When this is the case, the statistical model can be analyzed by likelihood ..."
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Cited by 21 (2 self)
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Econometrics is about confronting economic models with the data. In doing so it is crucial to choose a statistical model that not only contains the economic model as a submodel, but also contains the data generating process. When this is the case, the statistical model can be analyzed by likelihood methods. When this is not the case, but likelihood methods are applied nonetheless, the result is incorrect inference. In his paper we illustrate the problem of possible incorrect inference with a recent application of a DSGE model to US data (Ireland, 2004). Specifically, this paper discusses two broad methodological questions. • How should a statistical model be chosen to achieve valid inference for the economic model? • Given a correctly chosen statistical model, can we rely on the asymptotic results found in the statistical literature for the analysis of the data at hand? Using some simple examples, the paper first discusses some unfortunate consequences of applying Gaussian maximum likelihood when the chosen statistical model does not properly describe the data. It also demonstrates that even when the correct statistical model is chosen, asymptotic results derived for stationary processes cannot be used to conduct inference on the steady state value for a highly persistent stationary process.
Complete Identification Methods for the Causal Hierarchy
"... We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from a joint distribution over the observable variabl ..."
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We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from a joint distribution over the observable variables; causeeffect relationships, derived from distributions resulting from external interventions; and counterfactuals, derived from distributions that span multiple “parallel worlds ” and resulting from simultaneous, possibly conflicting observations and interventions. We completely characterize cases where a given causal query can be computed from information lower in the hierarchy, and provide algorithms that accomplish this computation. Specifically, we show when effects of interventions can be computed from observational studies, and when probabilities of counterfactuals can be computed from experimental studies. We also provide a graphical characterization of those queries which cannot be computed (by any method) from queries at a lower layer of the hierarchy.