Partial identification in nonseparable count data instrumental variable models

2019 ◽  
Vol 23 (2) ◽  
pp. 232-250 ◽  
Author(s):  
Dongwoo Kim

Summary This paper investigates undesirable limitations of widely used count data instrumental variable models. To overcome the limitations, I propose a partially identifying single-equation model that requires neither strong separability of unobserved heterogeneity nor a triangular system. Sharp bounds (identified sets) of structural features are characterised by conditional moment inequalities. Numerical examples show that the size of an identified set can be very small when the support of an outcome is rich or instruments are strong. An algorithm for estimation and inference is presented. I illustrate the usefulness of the proposed model in an empirical application to effects of supplemental insurance on healthcare utilisation.

10.3982/qe674 ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 527-563 ◽  
Author(s):  
Benjamin Williams

In this paper, I study identification of a nonseparable model with endogeneity arising due to unobserved heterogeneity. Identification relies on the availability of binary proxies that can be used to control for the unobserved heterogeneity. I show that the model is identified in the limit as the number of proxies increases. The argument does not require an instrumental variable that is excluded from the outcome equation nor does it require the support of the unobserved heterogeneity to be finite. I then propose a nonparametric estimator that is consistent as the number of proxies increases with the sample size. I also show that, for a fixed number of proxies, nontrivial bounds on objects of interest can be obtained. Finally, I study two real data applications that illustrate computation of the bounds and estimation with a large number of items.


2021 ◽  
Vol 56 ◽  
pp. 0-0
Author(s):  
Claudia Bauer-Krösbacher ◽  
Josef Mazanec

Purpose. In this study, the authors explore the role of museum visitors’ perceptions and experiences of authenticity. They introduce several variants of authenticity experience and analyse how they are intertwined and feed visitor satisfaction. Method. The authors apply a multi-step model fitting and validation procedure including inferred causation methods and finite mixture modelling to verify whether the visitors’ perceptions of authenticity are subject to unobserved heterogeneity. They elaborate an Authenticity Model that demonstrates out-of-sample validity and generalisability by being exposed to new data for another cultural attraction in another city. Then, they address the heterogeneity hypothesis and evaluate it for the case study with the larger sample. Findings. In both application cases, the Sisi museum in Vienna and the Guinness Storehouse in Dublin, the empirical results support the assumed cause-effect sequence, translating high quality information display—from traditional and multimedia sources—into Perceived Authenticity and its experiential consequences such as Depth and Satisfaction. Accounting for unobserved heterogeneity detects three latent classes with segment-specific strength of relationships within the structural model. Research and conclusions limitations. The combined latent-class, structural-equation model needs validation with another sample that would have to be larger than the available Guinness database. Future studies will have to complement the purely data-driven search for heterogeneity with theory-guided reasoning about potential causes of diversity in the strength of the structural relationships. Practical implications. Cultural heritage sites are among the attractions most typical of city tourism. History tends to materialise in the artefacts accumulated by the population among the urban agglomerations, and museums are the natural places for preserving exhibits of cultural value. Authenticity must be considered an important quality assessment criterion for many visitors, whereby, the distinction between object authenticity and existential authenticity is crucial. Originality. In addition to making substantive contributions to authenticity theory, the authors also extend previous research in terms of methodological effort. Authenticity research, so far, has neither exploited inferred causation methods nor combined latent variable modelling with detecting unobserved heterogeneity. Type of paper: Research article.


2018 ◽  
Vol 57 ◽  
pp. 398 ◽  
Author(s):  
Rajeev Prakash Bhanot ◽  
Dmitry Strunin

Author(s):  
Olanrewaju, Samuel Olayemi

One of the assumptions of a single equation model is that there is one -way causation between the dependent variable Y and the independent variables X. When the assumption is not valid, as, in many econometric models, of lack of correlation between the independent variables and the error terms (U) is further violated, Ordinary Least Square estimator would no longer efficient, that was why this study examined the effects of multicollinearity and a correlation between the error terms on the performance of seven estimators and identified the estimator that yields the most preferred estimates under the separate or joint influence of the two correlation effects under consideration. A two-equation model in which the two correlation problems were introduced was used in this study. The error terms of the two equations were also correlated. The levels of correlation between the error terms and multicollinearity were specified between -1 and +1 at an interval of 0.2 except when the correlation value approached unity. A Monte Carlo experiment of 1000 trials was carried out at five levels of sample sizes 20, 30, 50, 100, and 250 at two runs.


2011 ◽  
Vol 28 (2) ◽  
pp. 328-362 ◽  
Author(s):  
Herman J. Bierens ◽  
Li Wang

In this paper we propose consistent integrated conditional moment tests for the validity of parametric conditional distribution models, based on the integrated squared difference between the empirical characteristic function of the actual data and the characteristic function implied by the model. To avoid numerical evaluation of the conditional characteristic function of the model distribution, a simulated integrated conditional moment test is proposed. As an empirical application we test the validity of a few common health economic count data models.


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