scholarly journals Drivers of green bond issuance and new evidence on the “greenium”

2021 ◽  
Vol 11 (1) ◽  
pp. 1-24
Author(s):  
Kristin Ulrike Löffler ◽  
Aleksandar Petreski ◽  
Andreas Stephan
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Bond Markets ◽  
Exact Matching ◽  
Secondary Markets ◽  
Coarsened Exact Matching ◽  
Secondary Bond ◽  
New Evidence

AbstractThis paper examines whether a premium for green bonds, called “greenium”, found in previous studies, exists in primary and secondary bond markets. Using a universe of about 2000 green and 180,000 non-green bonds from 650 international issuers, we apply both propensity score matching and coarsened exact matching to determine a sample of conventional bonds that is most similar to the sample of green bonds. We find that green bonds have larger issue sizes and lower rated issuers, on average, compared to conventional bonds. The estimates show that the yield for green bonds is, on average, 15–20 basis points lower than that of conventional bonds, both on primary and secondary markets, thus a “greenium” exists.

Propensity score matching versus coarsened exact matching in observational comparative effectiveness research

2021 ◽  
Author(s):  
David Guy ◽  
Igor Karp ◽  
Piotr Wilk ◽  
Joseph Chin ◽  
George Rodrigues
Keyword(s):  
Clinical Trials ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Observational Data ◽  
Randomized Clinical Trials ◽  
Original Data ◽  
Considerable Proportion ◽  
Exact Matching ◽  
Coarsened Exact Matching ◽  
Comparison Groups

Aim & methods: We compared propensity score matching (PSM) and coarsened exact matching (CEM) in balancing baseline characteristics between treatment groups using observational data obtained from a pan-Canadian prostate cancer radiotherapy database. Changes in effect estimates were evaluated as a function of improvements in balance, using results from randomized clinical trials to guide interpretation. Results: CEM and PSM improved balance between groups in both comparisons, while retaining the majority of original data. Improvements in balance were associated with effect estimates closer to those obtained in randomized clinical trials. Conclusion: CEM and PSM led to substantial improvements in balance between comparison groups, while retaining a considerable proportion of original data. This could lead to improved accuracy in effect estimates obtained using observational data in a variety of clinical situations.

scholarly journals Evaluating the Utility of Coarsened Exact Matching for Pharmacoepidemiology Using Real and Simulated Claims Data

2019 ◽  
Vol 189 (6) ◽  
pp. 613-622 ◽  
Author(s):  
John E Ripollone ◽  
Krista F Huybrechts ◽  
Kenneth J Rothman ◽  
Ryan E Ferguson ◽  
Jessica M Franklin
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Mahalanobis Distance ◽  
Claims Data ◽  
The Elderly ◽  
Effect Estimate ◽  
Exact Matching ◽  
Coarsened Exact Matching ◽  
Covariate Balance ◽  
Insurance Claims Data

Abstract Coarsened exact matching (CEM) is a matching method proposed as an alternative to other techniques commonly used to control confounding. We compared CEM with 3 techniques that have been used in pharmacoepidemiology: propensity score matching, Mahalanobis distance matching, and fine stratification by propensity score (FS). We evaluated confounding control and effect-estimate precision using insurance claims data from the Pharmaceutical Assistance Contract for the Elderly (1999–2002) and Medicaid Analytic eXtract (2000–2007) databases (United States) and from simulated claims-based cohorts. CEM generally achieved the best covariate balance. However, it often led to high bias and low precision of the risk ratio due to extreme losses in study size and numbers of outcomes (i.e., sparse data bias)—especially with larger covariate sets. FS usually was optimal with respect to bias and precision and always created good covariate balance. Propensity score matching usually performed almost as well as FS, especially with higher index exposure prevalence. The performance of Mahalanobis distance matching was relatively poor. These findings suggest that CEM, although it achieves good covariate balance, might not be optimal for large claims-database studies with rich covariate information; it might be ideal if only a few (<10) strong confounders must be controlled.

scholarly journals Can the Ceftriaxone Breakpoints Be Increased Without Compromising Patient Outcomes?

2018 ◽  
Vol 5 (6) ◽  
Author(s):  
Pranita D Tamma ◽  
Virginia M Pierce ◽  
Sara E Cosgrove ◽  
Ebbing Lautenbach ◽  
Anthony Harris ◽  
...  
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Patient Outcomes ◽  
Nearest Neighbor ◽  
Clinical Laboratory ◽  
Severity Of Illness ◽  
Bloodstream Infections ◽  
Source Control ◽  
Exact Matching ◽  
Extended Spectrum

Abstract Background In 2010, the Clinical Laboratory and Standards Institute recommended a 3-fold lowering of ceftriaxone breakpoints to 1 mcg/mL for Enterobacteriaceae. Supportive clinical data at the time were from fewer than 50 patients. We compared the clinical outcomes of adults with Enterobacteriaceae bloodstream infections treated with ceftriaxone compared with matched patients (with exact matching on ceftriaxone minimum inhibitory concentrations [MICs]) treated with extended-spectrum agents to determine if ceftriaxone breakpoints could be increased without negatively impacting patient outcomes. Methods A retrospective cohort study was conducted at 3 large academic medical centers and included patients with Enterobacteriaceae bacteremia with ceftriaxone MICs of 2 mcg/mL treated with ceftriaxone or extended-spectrum β-lactams (ie, cefepime, piperacillin/tazobactam, meropenem, or imipenem/cilastatin) between 2008 and 2014; 1:2 nearest neighbor propensity score matching was performed to estimate the odds of recurrent bacteremia and mortality within 30 days. Results Propensity score matching yielded 108 patients in the ceftriaxone group and 216 patients in the extended-spectrum β-lactam group, with both groups well-balanced on demographics, preexisting medical conditions, severity of illness, source of bacteremia, and source control interventions. No difference in recurrent bacteremia (odds ratio [OR], 1.16; 95% confidence interval [CI], 0.49–2.73) or mortality (OR, 1.27; 95% CI, 0.56–2.91) between the treatment groups was observed for patients with isolates with ceftriaxone MICs of 2 mcg/mL. Only 6 isolates (1.6%) with ceftriaxone MICs of 2 mcg/mL were extended-spectrum β-lactamase (ESBL)–producing. Conclusions Our findings suggest that patient outcomes are similar when receiving ceftriaxone vs extended-spectrum agents for the treatment of Enterobacteriaceae bloodstream infections with ceftriaxone MICs of 2 mcg/mL. This warrants consideration of adjusting the ceftriaxone susceptibility breakpoint from 1 to 2 mcg/mL, as a relatively small increase in the antibiotic breakpoint could have the potential to limit the use of large numbers of extended-spectrum antibiotic agents.

scholarly journals Performance of Matching Methods as Compared With Unmatched Ordinary Least Squares Regression Under Constant Effects

2019 ◽  
Vol 188 (7) ◽  
pp. 1345-1354 ◽  
Author(s):  
Anusha M Vable ◽  
Mathew V Kiang ◽  
M Maria Glymour ◽  
Joseph Rigdon ◽  
Emmanuel F Drabo ◽  
...  
Keyword(s):  
Propensity Score ◽  
Least Squares ◽  
Propensity Score Matching ◽  
Confidence Intervals ◽  
Error Rate ◽  
Type I Error ◽  
Ordinary Least Squares ◽  
Type I ◽  
Exact Matching ◽  
Common Support

AbstractMatching methods are assumed to reduce the likelihood of a biased inference compared with ordinary least squares (OLS) regression. Using simulations, we compared inferences from propensity score matching, coarsened exact matching, and unmatched covariate-adjusted OLS regression to identify which methods, in which scenarios, produced unbiased inferences at the expected type I error rate of 5%. We simulated multiple data sets and systematically varied common support, discontinuities in the exposure and/or outcome, exposure prevalence, and analytical model misspecification. Matching inferences were often biased in comparison with OLS, particularly when common support was poor; when analysis models were correctly specified and common support was poor, the type I error rate was 1.6% for propensity score matching (statistically inefficient), 18.2% for coarsened exact matching (high), and 4.8% for OLS (expected). Our results suggest that when estimates from matching and OLS are similar (i.e., confidence intervals overlap), OLS inferences are unbiased more often than matching inferences; however, when estimates from matching and OLS are dissimilar (i.e., confidence intervals do not overlap), matching inferences are unbiased more often than OLS inferences. This empirical “rule of thumb” may help applied researchers identify situations in which OLS inferences may be unbiased as compared with matching inferences.

scholarly journals The difference in tax evasion amount among various taxpayers’ groups in Russia

2021 ◽  
Vol 62 ◽  
pp. 66-84
Author(s):  
Sergei Belev ◽  
◽  
Konstantin Vekerle ◽  
Anastasiia Evdokimova ◽  
◽  
...  
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Tax Evasion ◽  
Demographic Characteristics ◽  
Small Companies ◽  
Exact Matching ◽  
Individual Income ◽  
Economic Slowdown ◽  
The Difference ◽  
Social Demographic

This paper evaluates tax evasion in Russia through the estimation of individual «income‐consumption» gap. Our analysis is held on the balanced in social‐demographic characteristics subsample of the data of the RLMS of the Higher School of Economics. We reach this by using the combination of propensity score matching and exact matching. Given equal declared income consumption levels of self‐employed and worker from small companies is, on average, higher than for workers from the big ones. This leads to the conclusion that former ones evade 13–31% of their actual income. This result is heterogeneous in year. During the economic slowdown, differences in concealment of income between self‐employed and employees of small and large enterprises become insignificant.

L’impatto della laurea magistrale sui redditi da lavoro

Sinappsi ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Valentina Ferri ◽  
Giuliana Tesauro
Keyword(s):  
Sensitivity Analysis ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Selection Bias ◽  
Master's Degree ◽  
Bachelor's Degree ◽  
Sulla Base ◽  
Average Treatment ◽  
New Evidence ◽  
Italian National Institute

L’articolo è finalizzato a valutare l’impatto del conseguimento della laurea magistrale sui redditi da lavoro. Sulla base dei dati dell’indagine sull’inserimento professionale dei laureati (Istat), effettuiamo anzitutto la stima dei redditi da lavoro con correzione à la Heckman. Successivamente stimiamo l’effetto medio del Trattamento sui trattati (ATET) attraverso il propensity score matching (PSM). Concludiamo con i test di sensitività basati sulla simulazione con calibrated confounders. -------------------------------------------------------------------------------------------------------------------- The paper provides new evidence on Italian graduates’ earnings in their early careers comparing wages for workers who had a master’s degree with those for workers who had a bachelor’s degree four years after graduation. The data used in this article come from the Italian National Institute of Statistics. In order to mitigate a potential selection bias into treatment (master degrees), we conducted a propensity score matching (PSM) analysis and we estimate the Average Treatment on the Treated. Further we perform a sensitivity analysis based on the simulation with calibrated confounders.

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