Regression analysis of proportions in finance with self selection

Cook, Douglas O.; Kieschnick, Robert L.; McCullough, B. D.

doi:10.1016/j.jempfin.2008.02.001

Cited by 195 publications

(138 citation statements)

References 42 publications

Supporting

Mentioning

131

Contrasting

Unclassified

Order By: Relevance

“…This may be the case for some types of proportions, as when city managers make a discrete choice not to spend resources on a certain type of program, giving rise to a sample selection issue. This implies the use of a model such as the zero-one inflated beta model (Cook et al 2008;Ospina and Ferrari 2012). The choice between the first and second approach rests on assumptions concerning the process generating zeroes or ones.…”

Section: Empirical Modelmentioning

confidence: 99%

Are high-growth firms overrepresented in high-tech industries?

2015

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: It is frequently argued that policymakers should target high-tech firms, i.e., firms with high R&D intensity, because such firms are considered more innovative and therefore potential fast-growers. This argument relies on the assumption that the association among high-tech status, innovativeness and growth is actually positive. We examine this assumption by studying the industry distribution of high-growth firms (HGFs) across all 4-digit NACE industries, using data covering all limited liability firms in Sweden during the period 1997-2008. The results of fractional logit regressions indicate that industries with high R&D intensity, ceteris paribus, can be expected to have a lower share of HGFs than can industries with lower R&D intensity. The findings cast doubt on the wisdom of targeting R&D industries or subsidizing R&D to promote firm growth. In contrast, we find that HGFs are overrepresented in knowledge-intensive service industries, i.e., service industries with a high share of human capital. Terms of use: Documents in

show abstract

Section: Empirical Modelmentioning

confidence: 99%

Are high-growth firms overrepresented in high-tech industries?

2015

View full text Add to dashboard Cite

show abstract

“…To our knowledge, the only previously published zero-augmented beta approach (Cook et al 2008) specified a different parameterization. This previous model specified the two parameters of the beta distribution mixture component as α and α exp{−X ′ β}, while we use φ/(1 + exp{−X ′ β}) and φ/(1 + exp{X ′ β}) (assuming a logit link).…”

Section: Zero-augmented Joint Modeling Approachmentioning

confidence: 99%

Multilevel Bayesian Models for Survival Times and Longitudinal Patient-Reported Outcomes With Many Zeros

Hatfield

Boye²,

Hackshaw

et al. 2012

Journal of the American Statistical Association

View full text Add to dashboard Cite

Regulatory approval of new therapies often depends on demonstrating prolonged survival.Particularly when these survival benefits are modest, consideration of therapeutic benefits to patient-reported outcomes (PROs) may add value to the traditional biomedical clinical trial endpoints. We extend a popular class of joint models for longitudinal and survival data to accommodate the excessive zeros common in PROs, building hierarchical Bayesian models that combine information from longitudinal PRO measurements and survival outcomes. Model development is motivated by a clinical trial for malignant pleural mesothelioma, a rapidly fatal form of pulmonary cancer usually associated with asbestos exposure. By separately modeling the presence and severity of PROs, using our zero-augmented beta likelihood, we are able to model PROs on their original scale and learn about individual-level parameters from both presence and severity of symptoms. Correlations among an individual's PROs and survival are modeled using latent random variables, adjusting the fitted trajectories to better accommodate the observed data for each individual. This work contributes to understanding the impact of treatment on two aspects of mesothelioma: patients' subjective experience of the disease process and their progression-free survival times. We uncover important differences between outcome types that are associated with therapy (periodic, worse in both treatment groups after therapy initiation) and those that are responsive to treatment (aperiodic, gradually widening gap between treatment groups). Finally, our work raises questions for future investigation into multivariate modeling, choice of link functions, and the relative contributions of multiple data sources in joint modeling contexts.

show abstract

“…Papke and Wooldridge (2008) extend fractional response models to balanced panels with unobserved heterogeneity and endogenous covariates, Wooldridge (2010a) develops the theory for unbalanced panels, and Wooldridge (2012) derives a general set-up for one-step estimators in non-linear models. Other contributions to the field are Loudermilk (2007), Cook, Kieschnick, and McCullough (2008), Ramalho, Ramalho, andMurteira (2011), andTiwari andPalm (2011).…”

Section: Econometrics Of Fractional Response Modelsmentioning

confidence: 99%