Methods for multicountry studies of corporate governance: Evidence from the BRIKT countries
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Endogeneity
Omitted-variable bias
Predictive power
Value (mathematics)
Multilevel semicontinuous data occur frequently in medical, environmental, insurance and financial studies. Such data are often measured with covariates at different levels; however, these data have traditionally been modelled with covariate-independent random effects. Ignoring dependence of cluster-specific random effects and cluster-specific covariates in these traditional approaches may lead to ecological fallacy and result in misleading results. In this paper, we propose Tweedie compound Poisson model with covariate-dependent random effects to analyze multilevel semicontinuous data where covariates at different levels are incorporated at relevant levels. The estimation of our models has been developed based on the orthodox best linear unbiased predictor of random effect. Explicit expressions of random effects predictors facilitate computation and interpretation of our models. Our approach is illustrated through the analysis of the basic symptoms inventory study data where 409 adolescents from 269 families were observed at varying number of times from 1 to 17 times. The performance of the proposed methodology was also examined through the simulation studies.
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Since endogeneity problem is pervasive in identifying the causal effects of social capital, this chapter discusses four major sources of estimation bias derived from endogeneity, which are: omitted variable bias, self-selection bias, sample-selection bias, and simultaneity bias. Focusing on these identification challenges, this chapter also explains how to apply advanced approaches to deal with them according to examples in labour economics and sociological literature.
Endogeneity
Instrumental variable
Simultaneity
Omitted-variable bias
Identification
Sample (material)
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In this paper we present a discrete survival model with covariates and random effects, where the random effects may depend on the observed covariates. The dependence between the covariates and the random effects is modelled through correlation parameters, and these parameters can only be identified for time-varying covariates. For time-varying covariates, however, it is possible to separate regression effects and selection effects in the case of a certain dependene structure between the random effects and the time-varying covariates that are assumed to be conditionally independent given the initial level of the covariate. The proposed model is equivalent to a model with independent random effects and the initial level of the covariates as further covariates. The model is applied to simulated data that illustrates some identifiability problems, and further indicate how the proposed model may be an approximation to retrospectively collected data with incorrect specification of the waiting times. The model is fitted by maximum likelihood estimation that is implemented as iteratively reweighted least squares. © 1998 John Wiley & Sons, Ltd.
Identifiability
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Subjective measures used in models of political choice are typically open to criticism with respect to their endogeneity. Economic perceptions have been subject to particular criticism in this respect. We address these concerns through the application of estimation procedures introduced from econometrics and the introduction of a new measure of economic perceptions that acts as an instrumental variable. Together, these eliminate simultaneity bias and bias due to unobserved heterogeneity, reduce omitted-variable bias, and reduce noise/measurement error in economic perceptions. An analysis of three-wave panel surveys produces estimates of the effect of economic perceptions on party evaluation that are not biased upward by the presence of endogeneity and that help address the discrepancy between competing subjective models of economic effects on government approval.
Endogeneity
Instrumental variable
Omitted-variable bias
Simultaneity
Specification
Economic model
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In various medical related researches, excessive zeros, which make the standard Poisson regression model inadequate, often exist in count data. We proposed a covariate‐dependent random effect model to accommodate the excess zeros and the heterogeneity in the population simultaneously. This work is motivated by a data set from a survey on the dental health status of Hong Kong preschool children where the response variable is the number of decayed, missing, or filled teeth. The random effect has a sound biological interpretation as the overall oral health status or other personal qualities of an individual child that is unobserved and unable to be quantified easily. The overall measure of oral health status, responsible for accommodating the excessive zeros and also the heterogeneity among the children, is covariate dependent. This covariate‐dependent random effect model allows one to distinguish whether a potential covariate has an effect on the conceived overall oral health condition of the children, that is, the random effect, or has a direct effect on the magnitude of the counts, or both. We proposed a multiple imputation approach for estimation of the parameters. We discussed the choice of the imputation size. We evaluated the performance of the proposed estimation method through simulation studies, and we applied the model and method to the dental data. Copyright © 2012 John Wiley & Sons, Ltd.
Imputation (statistics)
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OBJECTIVE To apply random coefficients model and covariance pattern model to analyze longitudinal data with time varying covariate. METHODS An example of light, medium primary hypertension clinical trial was given to show the application of random coefficients model and covariance pattern model with time varying covariate-dose considering the dose at all-time points changing with illness and the MIXED procedure of the SAS system was used. RESULTS The results of the two models were very close. At 5% level of significance, there were no statistically significant differences between the groups, and the effects of time, age, dose and diastolic pressure before treatment were statistically significant (P﹤0.05). CONCLUSION It can get more objective results of medicine effect by using random coefficients model and covariance pattern model in longitudinal data because the models consider not only the data correlation and the effect of time varying covariate but also can handle the material with missing value.
Mixed model
Analysis of covariance
Longitudinal data
Covariance mapping
Repeated measures design
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Strategy scholars are increasingly concerned about biased empirical analyses from endogeneity. Treatments of the topic offer a dreary assessment, suggesting endogeneity is pervasive and remedies to attenuate bias are often deleterious. We examine the extent to which endogeneity from omitted variables appears to practically bias strategy research. We leverage the impact threshold of a confounding variable (ITCV), which allows us to estimate the likelihood a given study featured biased causal inference. Our content analysis of Strategic Management Journal suggests omitted variable bias is less pronounced than extant scholarship may contend. We then re-specify a seminal simulation on the topic with values informed by ITCV statistics from our content analysis. We find that omitted variable bias is often less pronounced than scholars suggest and more problematic than the techniques used to attenuate it.
Endogeneity
Omitted-variable bias
Instrumental variable
Leverage (statistics)
Variables
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The work reported in this article was undertaken to evaluate the utility of SAS PROC.MIXED for testing hypotheses concerning GROUP and TIME × GROUP effects in repeated measurements designs with dropouts. If dropouts are not completely at random, covariate control over informative individual differences on which dropout data patterns depend is widely recognized to be important. However, the inclusion of baseline scores and time-in-study as between-subject covariates in an otherwise well formulated SAS PROC.MIXED model resulted in inadequate control over type I error in simulated data with or without dropouts present. The inadequate model formulations and resulting deviant test sizes are presented here as a warning for others who might be guided by the same information sources to employ similar model specifications when analyzing data from actual clinical trials. It is important that the complete model specification be provided in detail when reporting applications of the general linear mixed-model procedure. A single random-coefficients model produced appropriate test sizes, but it provided inferior power when informative covariates were added in the attempt to adjust for dropouts. As an alternative, the incorporation of covariate controls in simpler two-stage endpoint or random regression analyses is documented to be effective in dealing with dropouts under specifiable conditions.
Mixed model
Dropout (neural networks)
Repeated measures design
Specification
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Nonlinear mixed-effects modeling is one of the most popular tools for analyzing repeated measurement data, particularly for applications in the biomedical fields. Multiple integration and nonlinear optimization are the two major challenges for likelihood-based methods in nonlinear mixed-effects modeling. To solve these problems, approaches based on empirical Bayesian estimates have been proposed by breaking the problem into a nonlinear mixed-effects model with no covariates and a linear regression model without random effect. This approach is time-efficient as it involves no covariates in the nonlinear optimization. However, covariate effects based on empirical Bayesian estimates are underestimated and the bias depends on the extent of shrinkage. Marginal correction method has been proposed to correct the bias caused by shrinkage to some extent. However, the marginal approach appears to be suboptimal when testing covariate effects on multiple model parameters, a situation that is often encountered in real-world data analysis. In addition, the marginal approach cannot correct the inaccuracy in the associated p-values. In this paper, we proposed a simultaneous correction method (nSCEBE), which can handle the situation where covariate analysis is performed on multiple model parameters. Simulation studies and real data analysis showed that nSCEBE is accurate and efficient for both effect-size estimation and p-value calculation compared with the existing methods. Importantly, nSCEBE can be >2000 times faster than the standard mixed-effects models, potentially allowing utilization for high-dimension covariate analysis for longitudinal or repeated measured outcomes.
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This paper argues that the true cause of the endogeneity bias that allegedly appears when estimating production functions, and which the literature has tried to deal with since the 1940s, is simply the result of omitted-variable bias due to an incorrect approximation to an accounting identity. As a result we question recent attempts to solve the problem by developing new estimators.
Endogeneity
Omitted-variable bias
Instrumental variable
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