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Identifying Assumptions (identifying + assumption)
Selected AbstractsEstimating a Dynamic Model of Household Choices in the Presence of Income TaxationINTERNATIONAL ECONOMIC REVIEW, Issue 3 2000Holger Sieg The purpose of the article is to study the incentive and distributional consequences of income taxation. The article analyzes tax changes in a dynamic setting. The framework is estimated under a set of different identifying assumptions using parametric, nonparametric, and semiparametric techniques. The empirical results focus on tax reforms in Germany in the 1980s. The article shows that these reforms did not significantly lower effective tax rates. The findings also suggest that estimated elasticities for male labor supply are small, ranging between 0.02 and 0.2. [source] On the Specification and Estimation of the Production Function for Cognitive Achievement*THE ECONOMIC JOURNAL, Issue 485 2003Petra E. Todd This paper considers methods for modelling the production function for cognitive achievement in a way that captures theoretical notions that child development is a cumulative process depending on the history of family and school inputs and on innate ability. It develops a general modelling framework that accommodates many of the estimating equations used in the literatures. It considers different ways of addressing data limitations, and it makes precise the identifying assumptions needed to justify alternative approaches. Commonly used specifications are shown to place restrictive assumptions on the production technology. Ways of testing modelling assumptions and of relaxing them are discussed. [source] Related Causal Frameworks for Surrogate OutcomesBIOMETRICS, Issue 2 2009Marshall M. Joffe Summary Four major frameworks have been developed for evaluating surrogate markers in randomized trials: one based on conditional independence of observable variables, another based on direct and indirect effects, a third based on a meta-analysis, and a fourth based on principal stratification. The first two of these fit into a paradigm we call the causal-effects (CE) paradigm, in which, for a good surrogate, the effect of treatment on the surrogate, combined with the effect of the surrogate on the clinical outcome, allow prediction of the effect of the treatment on the clinical outcome. The last two approaches fall into the causal-association (CA) paradigm, in which the effect of the treatment on the surrogate is associated with its effect on the clinical outcome. We consider the CE paradigm first, and consider identifying assumptions and some simple estimation procedures; we then consider the CA paradigm. We examine the relationships among these approaches and associated estimators. We perform a small simulation study to illustrate properties of the various estimators under different scenarios, and conclude with a discussion of the applicability of both paradigms. [source] Methods for Conducting Sensitivity Analysis of Trials with Potentially Nonignorable Competing Causes of CensoringBIOMETRICS, Issue 1 2001Rotnitzky Andrea Summary. We consider inference for the treatment-arm mean difference of an outcome that would have been measured at the end of a randomized follow-up study if, during the course of the study, patients had not initiated a nonrandomized therapy or dropped out. We argue that the treatment-arm mean difference is not identified unless unverifiable assumptions are made. We describe identifying assumptions that are tantamount to postulating relationships between the components of a pattern-mixture model but that can also be interpreted as imposing restrictions on the cause-specific censoring probabilities of a selection model. We then argue that, although sufficient for identification, these assumptions are insufficient for inference due to the curse of dimensionality. We propose reducing dimensionality by specifying semiparametric cause-specific selection models. These models are useful for conducting a sensitivity analysis to examine how inference for the treatment-arm mean difference changes as one varies the magnitude of the cause-specific selection bias over a plausible range. We provide methodology for conducting such sensitivity analysis and illustrate our methods with an analysis of data from the AIDS Clinical Trial Group (ACTG) study 002. [source] | ||||||||||