New EM-type algorithms for the Heckman selection model

2020 ◽  
Vol 146 ◽  
pp. 106930
Author(s):  
Jun Zhao ◽  
Hea-Jung Kim ◽  
Hyoung-Moon Kim
Keyword(s):  
Selection Model ◽  
Heckman Selection Model ◽  
Heckman Selection

Abstract 19: Using the Heckman Selection Model to Assess Selection Bias in Ischemic Stroke Patients with Documented NIHSS

2015 ◽  
Vol 8 (suppl_2) ◽  
Author(s):  
Michael P Thompson ◽  
Zhehui Luo ◽  
Joseph Gardiner ◽  
James F Burke ◽  
Mathew J Reeves
Keyword(s):  
Ischemic Stroke ◽  
Correlation Coefficient ◽  
Selection Bias ◽  
Large Scale ◽  
Selection Process ◽  
Selection Model ◽  
Stroke Patients ◽  
Heckman Selection Model ◽  
Nihss Score ◽  
Heckman Selection

Objective: Complete documentation in large scale datasets such as administrative data or disease registries is often difficult. Given that the subset of patients with complete data documentation are most likely not a random sample of patients, selection bias threatens the validity of results if a complete case analysis is used. To demonstrate, we will assess the presence and magnitude of selection bias in ischemic stroke patients with documented National Institute of Health Stroke Scale (NIHSS) [[Unable to Display Character: &#8211;]] which is often incomplete [[Unable to Display Character: &#8211;]] using the Heckman Selection Model. Methods: Patient level variables including demographics, comorbidities, clinical EMS and admission variables, and medical history/comorbidities were obtained from 10,717 ischemic stroke patients aged 65 and older in the Michigan Stroke Registry in 2009-2012. The Heckman Selection Model assesses the presence and magnitude of selection bias by estimating a correlation coefficient between error components of a linear regression model predicting patient NIHSS score [[Unable to Display Character: &#8211;]] the outcome model [[Unable to Display Character: &#8211;]] and a binary probit model predicting NIHSS documentation [[Unable to Display Character: &#8211;]] the selection model [[Unable to Display Character: &#8211;]] conditional on patient and hospital predictors. The outcome model predicting NIHSS score was specified using a backward selection process with stepwise deletion of non-significant predictors. The selection model included all variables in the outcome model, plus additional significant predictors of NIHHS documentation. Quasi-maximum likelihood estimation was used to produce robust standard errors. All analyses were done using PROC QLIM procedure in SAS. Results: 7,956 cases (74.2%) of cases had NIHSS documented. Significant predictors in the outcome and selection models are shown in the Table. The Heckman Selection Model found a statistically significant but modest correlation coefficient of ρ =0.1089 (SE=0.0119, p<0.0001). The positive correlation indicates that NIHSS was more likely to be documented in patients with higher NIHSS scores, i.e., more severe strokes. Conclusions: We found statistically significant albeit weak selection bias in the documentation of NIHSS in stroke patients. The Heckman Selection Model is a novel method that can be used to assess the presence and magnitude of selection bias when missing data is common.

Is Openness Penalized? Stock Returns around Earnings Warnings

2007 ◽  
Vol 82 (4) ◽  
pp. 1055-1087 ◽  
Author(s):  
Jennifer W. Tucker
Keyword(s):  
Stock Returns ◽  
Selection Bias ◽  
Selection Model ◽  
Bad News ◽  
Short Term ◽  
Heckman Selection Model ◽  
Earnings Announcement ◽  
Self Selection ◽  
Earnings Warnings ◽  
Heckman Selection

Prior research finds that firms warning investors of an earnings shortfall experience lower returns than non-warning firms with similar risks and earnings news. Openness thus appears to be penalized by investors. Yet, this finding may be due to a self-selection bias that occurs when firms with a larger amount of unfavorable non-earnings news (“other bad news”) are more likely to warn. In this paper I use a Heckman selection model to infer the amount of other bad news and document that, on average, warning firms have a larger amount of other bad news than non-warning firms. After controlling for this effect, I find that warning firms' returns remain lower than those of non-warning firms in a short-term window ending five days after earnings announcement. When this window is extended by three months, however, warning and non-warning firms exhibit similar returns. My evidence suggests that openness is ultimately not penalized by investors.

scholarly journals A Heckman selection model for the safety analysis of signalized intersections

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0181544 ◽  
Author(s):  
Xuecai Xu ◽  
S. C. Wong ◽  
Feng Zhu ◽  
Xin Pei ◽  
Helai Huang ◽  
...  
Keyword(s):  
Safety Analysis ◽  
Signalized Intersections ◽  
Selection Model ◽  
Heckman Selection Model ◽  
Heckman Selection

scholarly journals Healthier foods in perspective: What drives garlic purchase decisions and consumption in urban South-West Nigeria?

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Dare Akerele ◽  
Solomon Ajoseh ◽  
Rahman Sanusi ◽  
Funminiyi Oyawole
Keyword(s):  
Household Income ◽  
Formal Education ◽  
Health Workers ◽  
Household Head ◽  
Health Benefits ◽  
Selection Model ◽  
Consumption Expenditure ◽  
Heckman Selection Model ◽  
South West ◽  
Heckman Selection

AbstractAlthough consumer demand for food products with more health functions has stimulated expansion of a number of food industries in the past years, not much is known about drivers of market participation and consumption of such foods in Africa, and Nigeria in particular. Consequently, the study examined factors influencing purchase decision and consumption-expenditure on garlic in South-West Nigeria. Descriptive statistics and the Heckman selection model were employed for data analysis. Results show that more than 70.0% of the respondent households became aware of the health benefits of garlic through media, friends/family and health workers, with more than 75.0% consuming garlic in raw and processed forms. The results of the Heckman selection model indicated that sex (p<0.05) and awareness of household head about the health benefits of garlic (p<0.01) substantially enhanced decisions to consume garlic while household income (p<0.01), household size (p<0.01), educational status (p<0.1) and occupation of the household head (p<0.1) significantly influenced consumption-expenditure. The study recommends public education programmes on the health benefits of garlic, efforts to enhance access to formal education and improvement in household income as strategies that could stimulate and raise garlic consumption. Our findings hold enormous implications for the sustainability of the garlic market in terms of research and product development as it relates to the forms in which consumers prefer to consume garlic and strategies for spreading knowledge about its health benefits in order to achieve greater demand in the country.

The Politics of EU Eastern Enlargement: Evidence from a Heckman Selection Model

2005 ◽  
Vol 36 (1) ◽  
pp. 17-38 ◽  
Author(s):  
THOMAS PLÜMPER ◽  
CHRISTINA J. SCHNEIDER ◽  
VERA E. TROEGER
Keyword(s):  
Selection Model ◽  
Policy Support ◽  
Reform Process ◽  
The European Union ◽  
Eastern Enlargement ◽  
Heckman Selection Model ◽  
Acquis Communautaire ◽  
Eu Eastern Enlargement ◽  
Heckman Selection ◽  
The Eu

The eastern enlargement of the European Union is a twofold process, in which governments of transition countries decide whether or not to apply for membership and in turn EU members decide whether or not to accept these applicants. The authors argue that the level of democracy and the extent of market reforms together determine the first decision, while the second decision is largely determined by the EU observing the reform process in applicant countries imposed by the acquis communautaire conditionality. The natural procedure to test this theory is a Heckman selection model. A Heckman specification with panel probit estimators in both stages is used. The data supports the argument that uncontested reforms signal the policy support of relevant political parties to the EUand increase the likelihood of joining the Union. The authors also test for specification errors and check the robustness of the findings.

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