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Article Two-Part Models for Fractional Responses Defined as Ratios of Integers
, 2014
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zbw Leibniz-Informationszentrum WirtschaftLeibniz Information Centre for Economics
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Censored Fractional Response Model: Estimating Heterogeneous Relative Risk Aversion of European Households
"... Abstract This paper estimates relative risk aversion using the observed shares of risky assets and characteristics of households from the Household Finance and Consumption Survey of the European Central Bank. Given that the risky share is a fractional response variable belonging to [0, ..."
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Abstract This paper estimates relative risk aversion using the observed shares of risky assets and characteristics of households from the Household Finance and Consumption Survey of the European Central Bank. Given that the risky share is a fractional response variable belonging to [0,
Exponential regression of fixed effects fractional response models
, 2012
"... New fixed-effects panel data estimators are proposed for logit and complementary loglog fractional responses. The fractional regression models are written as exponential models from which the individual effects are eliminated by quasi difference transformations. The resultant estimators are robust t ..."
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New fixed-effects panel data estimators are proposed for logit and complementary loglog fractional responses. The fractional regression models are written as exponential models from which the individual effects are eliminated by quasi difference transformations. The resultant estimators are robust to both time-variant and time-invariant heterogeneity, accommodate endogenous explanatory variables and can applied to dynamic panel data models.
unknown title
, 2015
"... Exponential regression of panel data fractional response models with an application to firm capital structure∗ ..."
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Exponential regression of panel data fractional response models with an application to firm capital structure∗
Predicting Recovery Rate at the Time of Corporate Default
"... A new beta regression model for recovery rates is proposed and implemented on a sample of 3,827 defaulted debts obtained from Moody’s Ultimate Recovery Database. This model is constructed by first extending the support of the beta distribution and then censoring the part below 0 (and above 1) to cre ..."
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A new beta regression model for recovery rates is proposed and implemented on a sample of 3,827 defaulted debts obtained from Moody’s Ultimate Recovery Database. This model is constructed by first extending the support of the beta distribution and then censoring the part below 0 (and above 1) to create point mass for the recovery rate of 0 (and 1). The empirical results clearly show that debt attributes known from the issuance time and industry distress level at the time of default are both significant in predicting recovery rate. Bi-modality typically observed in recovery rate data makes it particularly important to account for available information in predicting recovery rate, and thus a straightforward use of an unconditional average recovery rate of, say 40%, can be quite misleading. A performance study based on an analysis of 20 randomly selected pairs of in-sample and out-of-sample datasets of equal size shows that our proposed conditional recovery rate model outperforms four alternative models considered in this study.
RESEARCH Open Access Interpreting
"... small treatment differences from quality of life data in cancer trials: an alternative measure of treatment benefit and effect size for the EORTC-QLQ-C30 Iftekhar Khan1*, Zahid Bashir2 and Martin Forster3 Background: The EORTC-QLQ-C30 is a widely used health related quality of life (HRQoL) questionn ..."
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small treatment differences from quality of life data in cancer trials: an alternative measure of treatment benefit and effect size for the EORTC-QLQ-C30 Iftekhar Khan1*, Zahid Bashir2 and Martin Forster3 Background: The EORTC-QLQ-C30 is a widely used health related quality of life (HRQoL) questionnaire in lung cancer patients. Small HRQoL treatment effects are often reported as mean differences (MDs) between treatments, which are rarely justified or understood by patients and clinicians. An alternative approach using odds ratios (OR) for reporting effects is proposed. This may offer advantages including facilitating alignment between patient and clinician understanding of HRQoL effects. Methods: Data from six CRUK sponsored randomized controlled lung cancer trials (2 small cell and 4 in non-small cell, in 2909 patients) were used to HRQoL effects. Results from Beta-Binomial (BB) standard mixed effects were compared. Preferences for ORs vs MDs were determined and Time to Deterioration (TD) was also compared. Results: HRQoL effects using ORs offered coherent interpretations: MDs>0 resulted in ORs>1 and vice versa; effect sizes were classified as ‘Trivial ’ if the OR was between 1 ± 0.05 (i.e. 0.95 to 1.05); ‘Small’: for 1 ± 0.1; ‘Medium’: 1 ± 0.2 and ‘Large’: OR <0.8 or>1.20. Small HRQoL effects on the MD scale may translate to important treatment differences on the OR scale: for example, a worsening in symptoms (MD) by 2.6 points (p = 0.1314) would be a 17 % deterioration (p < 0.0001) with an OR. Hence important differences may be missed with MD; conversely, small ORs are unlikely to yield large MDs because methods based on OR model skewed data well. Initial evidence also suggests oncologists prefer ORs over MDs since interpretation is similar to hazard ratios. Conclusion: Reporting HRQoL benefits as MDs can be misleading. Estimates of HRQoL treatment effects in terms of ORs are preferred over MDs. Future analysis of QLQ-C30 and other HRQoL measures should consider reporting HRQoL treatment effects as ORs.
WORKING PAPER NO. 15-08 CREDIT RISK MODELING IN SEGMENTED PORTFOLIOS: AN APPLICATION TO CREDIT CARDS
, 2015
"... The Great Recession offers a unique opportunity to analyze the performance of credit risk models under conditions of economic stress. We focus on the performance of models of credit risk applied to risk-segmented credit card portfolios. Specifically, we focus on models of default and loss and analyz ..."
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The Great Recession offers a unique opportunity to analyze the performance of credit risk models under conditions of economic stress. We focus on the performance of models of credit risk applied to risk-segmented credit card portfolios. Specifically, we focus on models of default and loss and analyze three important sources of model risk: model selection, model specification, and sample selection. Forecast errors can be significant along any of these three model-risk dimensions. Simple linear regression models are not generally outperformed by more complex or stylized models. The impact of macroeconomic variables is heterogeneous across risk segments. Model specifications that do not consider this heterogeneity display large projection errors across risk segments. Prime segments are proportionally more severely impacted by a downturn in economic conditions relative to the subprime or near-prime segments. The sensitivity of modeled losses to macroeconomic factors is conditional on the model development sample. Models estimated over a period that does not incorporate a significant period of the Great Recession may fail to project default rates, or loss rates, consistent with those experienced during the Great Recession.
