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Principles and practice in reporting structural equation analyses
 PSYCHOLOGICAL METHODS
, 2002
"... Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of ident ..."
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Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodnessoffit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here.
The TETRAD Project: Constraint Based Aids to Causal Model Specification
 MULTIVARIATE BEHAVIORAL RESEARCH
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Identifying causal effects with computer algebra, in
 Proceedings of the Twenty Sixth Annual Conference on Uncertainty in Artificial Intelligence (UAI2010
, 2010
"... The longstanding identification problem for causal effects in graphical models has many partial results but lacks a systematic study. We show how computer algebra can be used to either prove that a causal effect can be identified, generically identified, or show that the effect is not generically i ..."
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The longstanding identification problem for causal effects in graphical models has many partial results but lacks a systematic study. We show how computer algebra can be used to either prove that a causal effect can be identified, generically identified, or show that the effect is not generically identifiable. We report on the results of our computations for linear structural equation models, where we determine precisely which causal effects are generically identifiable for all graphs on three and four vertices. 1
On the identification of a class of linear models
 In Proceedings of the AAAI
, 2007
"... This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. The paper provides a procedure for solving the identification problem in a special class of models. ..."
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This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. The paper provides a procedure for solving the identification problem in a special class of models.
USING FIRM OPTIMIZATION TO EVALUATE AND ESTIMATE PRODUCTIVITY AND RETURNS TO SCALE
, 2010
"... At the firm level, revenue and costs are well measured but prices and quantities are not. This paper shows that because of these data limitations estimates of returns to scale at the firm level are for the revenue function, not production function. Given this observation, the paper argues that, unde ..."
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At the firm level, revenue and costs are well measured but prices and quantities are not. This paper shows that because of these data limitations estimates of returns to scale at the firm level are for the revenue function, not production function. Given this observation, the paper argues that, under weak assumptions, microlevel estimates of returns to scale are often inconsistent with profit maximization or imply implausibly large profits. The puzzle arises because popular estimators ignore heterogeneity and endogeneity in factor/product prices, assume perfect elasticity of factor supply curves or neglect the restrictions imposed by profit maximization (cost minimization) so that estimators are inconsistent or poorly identified. The paper argues that simple structural estimators can address these problems. Specifically, the paper proposes a fullinformation estimator that models the cost and the revenue functions simultaneously and accounts for unobserved heterogeneity in productivity and factor prices symmetrically. The strength of the proposed estimator is illustrated by Monte Carlo simulations and an empirical application. Finally, the paper discusses a number of implications of estimating revenue functions rather than production functions and demonstrates that the profit share in revenue is a robust nonparametric economic diagnostic for estimates of returns to scale.
REDUCEDRANK IDENTIFICATION OF STRUCTURAL SHOCKS IN VARS
, 2006
"... This paper integrates imposing a factor structure on residuals in vector autoregressions (VARs) into structural VAR analysis. Identification, estimation and testing procedures are discussed. The paper applies this approach to the wellknown problem of studying the effects of monetary policy in open ..."
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This paper integrates imposing a factor structure on residuals in vector autoregressions (VARs) into structural VAR analysis. Identification, estimation and testing procedures are discussed. The paper applies this approach to the wellknown problem of studying the effects of monetary policy in open economy VAR models. The use of factor structure in identifying structural shocks is shown to resolve three longstanding puzzles in VAR literature. First, the price level does not increase in response to a monetary tightening. Second, the exchange rate appreciates on impact and then gradually depreciates. Hence, no price level and exchange rate puzzles are found. Third, monetary policy shocks are much less volatile than suggested by standard VAR identification schemes. In addition, the paper suggests that the apparent weak contemporaneous crossvariable responses and strong own responses in structural VARs can be an artifact of identifying assumptions and vanish after imposing a factor structure on the shocks.