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Evaluating the quality of credit portfolio risk models is an important issue for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data. We show that their proposal disregards cross-sectional dependence in resampled portfolios, which renders standard statistical inference invalid. We proceed by suggesting the Berkowitz (1999) procedure, which relies on standard likelihood ratio tests performed on transformed default data. We simulate the power of this approach in various settings including one in which the test is extended to incorporate cross-sectional information. To compare the predictive ability of alternative models, we propose to use either Bonferroni bounds or the likelihood-ratio of the two models. Monte Carlo simulations show that a default history of ten years can be sufficient to resolve uncertainties currently present in credit risk modeling.
Evaluating the quality of credit portfolio risk models is an important question for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data and to produce measures of forecast accuracy. We first show that their proposal disregards crosssectional dependence in simulated subportfolios, which renders standard statistical inference invalid. We proceed by suggesting another evaluation methodology which draws on the concept of likelihood ratio tests. Specifically, we compare the predictive quality of alternative models by comparing the probabilities that observed data have been generated by these models. The distribution of the test statistic can be derived through Monte Carlo simulation. To exploit differences in cross-sectional predictions of alternative models, the test can be based on a linear combination of subportfolio statistics. In the construction of the test, the weight of a subportfolio depends on the difference in the loss distributions which alternative models predict for this particular portfolio. This makes efficient use of the data, and reduces computational burden. Monte Carlo simulations suggest that the power of the tests is satisfactory.
JEL classification: G2; G28; C52
We compare the cost effectiveness of two pronatalist policies:
(a) child allowances; and
(b) daycare subsidies.
We pay special attention to estimating how intended fertility (fertility before children are born) responds to these policies. We use two evaluation tools:
(i) a dynamic model on fertility, labor supply, outsourced childcare time, parental time, asset accumulation and consumption; and
(ii) randomized vignette-survey policy experiments.
We implement both tools in the United States and Germany, finding consistent evidence that daycare subsidies are more cost effective. Nevertheless, the required public expenditure to increase fertility to the replacement level might be viewed as prohibitively high.
Under a new Basel capital accord, bank regulators might use quantitative measures when evaluating the eligibility of internal credit rating systems for the internal ratings based approach. Based on data from Deutsche Bundesbank and using a simulation approach, we find that it is possible to identify strongly inferior rating systems out-of time based on statistics that measure either the quality of ranking borrowers from good to bad, or the quality of individual default probability forecasts. Banks do not significantly improve system quality if they use credit scores instead of ratings, or logistic regression default probability estimates instead of historical data. Banks that are not able to discriminate between high- and low-risk borrowers increase their average capital requirements due to the concavity of the capital requirements function.
This paper investigates the accuracy of point and density forecasts of four DSGE models for inflation, output growth and the federal funds rate. Model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. Point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods. Despite their common underlying New Keynesian modeling philosophy, forecasts of different DSGE models turn out to be quite distinct. Weighted forecasts are more precise than forecasts from individual models. The accuracy of a simple average of DSGE model forecasts is comparable to Greenbook projections for medium term horizons. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts.
This paper investigates the accuracy of point and density forecasts of four DSGE models for inflation, output growth and the federal funds rate. Model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. Point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods. Despite their common underlying New Keynesian modeling philosophy, forecasts of different DSGE models turn out to be quite distinct. Weighted forecasts are more precise than forecasts from individual models. The accuracy of a simple average of DSGE model forecasts is comparable to Greenbook projections for medium term horizons. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts.
This paper investigates the accuracy of forecasts from four DSGE models for inflation, output growth and the federal funds rate using a real-time dataset synchronized with the Fed’s Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as accurate as the Greenbook projections for output growth and the federal funds rate. Only for inflation the model forecasts are dominated by the Greenbook projections. A comparison with forecasts from Bayesian VARs shows that the economic structure of the DSGE models which is useful for the interpretation of forecasts does not lower the accuracy of forecasts. Combining forecasts of several DSGE models increases precision in comparison to individual model forecasts. Comparing density forecasts with the actual distribution of observations shows that DSGE models overestimate uncertainty around point forecasts.
This paper evaluates the effects of Public Sponsored Training in East Germany in the context of reiterated treatments. Selection bias based on observed characteristics is corrected for by applying kernel matching based on the propensity score. We control for further selection and the presence of Ashenfelter's Dip before the program with conditional difference-in-differences estimators. Training as a first treatment shows insignificant effects on the transition rates. The effect of program sequences and the incremental effect of a second program on the reemployment probability are insignificant. However, the incremental effect on the probability to remain employed is slightly positive. JEL - Klassifikation: H43 , C23 , J6 , J64 , C14
We develop a novel empirical approach to identify the effectiveness of policies against a pandemic. The essence of our approach is the insight that epidemic dynamics are best tracked over stages, rather than over time. We use a normalization procedure that makes the pre-policy paths of the epidemic identical across regions. The procedure uncovers regional variation in the stage of the epidemic at the time of policy implementation. This variation delivers clean identification of the policy effect based on the epidemic path of a leading region that serves as a counterfactual for other regions. We apply our method to evaluate the effectiveness of the nationwide stay-home policy enacted in Spain against the Covid-19 pandemic. We find that the policy saved 15.9% of lives relative to the number of deaths that would have occurred had it not been for the policy intervention. Its effectiveness evolves with the epidemic and is larger when implemented at earlier stages.
Innovations are a key factor to ensure the competitiveness of establishments as well as to enhance the growth and wealth of nations. But more than any other economic activity, decisions about innovations are plagued by failures of the market mechanism. As a response, public instruments have been implemented to stimulate private innovation activities. The effectiveness of these measures, however, is ambiguous and calls for an empirical evaluation. In this paper we make use of the IAB Establishment Panel and apply various microeconometric methods to estimate the effect of public measures on innovation activities of German establishments. We find that neglecting sample selection due to observable as well as to unobservable characteristics leads to an overestimation of the treatment effect and that there are considerable differences with regard to size class and betweenWest and East German establishments.
In dieser Studie werden die Wirkungen von Arbeitsbeschaffungsmaßnahmen (ABM) in Deutschland auf die individuellen Eingliederungswahrscheinlichkeiten der Teilnehmer in reguläre Beschäftigung evaluiert. Für die Untersuchung wird ein umfangreicher und informativer Datensatz aus den Datenquellen der Bundesagentur für Arbeit (BA) verwendet, der es ermöglicht, die Wirkungen der Programme differenziert nach individuellen Unterschieden der Teilnehmer und mit Berücksichtigung der heterogenen Arbeitsmarktstruktur zu untersuchen. Der Datensatz enthält Informationen zu allen Teilnehmern in ABM, die ihre Maßnahmen im Februar 2000 begonnen haben, und zu einer Kontrollgruppe von Nichtteilnehmern, die im Januar 2000 arbeitslos waren und im Februar 2000 nicht in die Programme eingetreten sind. Mit Hilfe der Informationen der Beschäftigtenstatistik ist es hierbei erstmals möglich, den Abgang in reguläre Beschäftigung auf Grundlage administrativer Daten zu untersuchen. Der vorliegende Verbleibszeitraum reicht bis Dezember 2002. Unter Verwendung von Matching-Methoden auf dem Ansatz potenzieller Ergebnisse werden die Effekte von ABM mit regionaler Unterscheidung und für besondere Problem- und Zielgruppen des Arbeitsmarktes geschätzt. Die Ergebnisse zeigen zwar deutliche Unterschiede in den Effekten für Subgruppen, insgesamt weisen die empirischen Befunde jedoch darauf hin, dass das Ziel der Eingliederung in reguläre ungeförderte Beschäftigung durch ABM weitgehend nicht realisiert werden konnte. JEL: C40 , C13 , J64 , H43 , J68
In dieser Studie werden die Wirkungen von Arbeitsbeschaffungsmaßnahmen (ABM) in Deutschland auf die individuellen Eingliederungswahrscheinlichkeiten der Teilnehmer in reguläre Beschäftigung evaluiert. Für die Untersuchung wird ein umfangreicher und informativer Datensatz aus den Datenquellen der Bundesagentur für Arbeit (BA) verwendet, der es ermöglicht, die Wirkungen der Programme differenziert nach individuellen Unterschieden der Teilnehmer und mit Berücksichtigung der heterogenen Arbeitsmarktstruktur zu untersuchen. Der Datensatz enthält Informationen zu allen Teilnehmern in ABM, die ihre Maßnahmen im Februar 2000 begonnen haben, und zu einer Kontrollgruppe von Nichtteilnehmern, die im Januar 2000 arbeitslos waren und im Februar 2000 nicht in die Programme eingetreten sind. Mit Hilfe der Informationen der Beschäftigtenstatistik ist es hierbei erstmals möglich, den Abgang in reguläre Beschäftigung auf Grundlage administrativer Daten zu untersuchen. Der vorliegende Verbleibszeitraum reicht bis Dezember 2002. Unter Verwendung von Matching-Methoden auf dem Ansatz potenzieller Ergebnisse werden die Effekte von ABM mit regionaler Unterscheidung und für besondere Problem- und Zielgruppen des Arbeitsmarktes geschätzt. Die Ergebnisse zeigen zwar deutliche Unterschiede in den Effekten für Subgruppen, insgesamt weisen die empirischen Befunde jedoch darauf hin, dass das Ziel der Eingliederung in reguläre ungeförderte Beschäftigung durch ABM weitgehend nicht realisiert werden konnte. JEL: C40 , C13 , J64 , H43 , J68
Persistently high unemployment, tight government budgets and the growing scepticism regarding the effects of active labour market policies (ALMP) are the basis for a growing interest in evaluating these measures. This paper intends to explain the need for evaluation on the micro- and macroeconomic level, introduce the fundamental evaluation problem and solutions to it, give an overview of the newer developments in evaluation literature and finally take a look on empirical estimations of ALMP effects. JEL Classification: C14, C33, H43, J64, J68
We develop a methodology to identify and rank “systemically important financial institutions” (SIFIs). Our approach is consistent with that followed by the Financial Stability Board (FSB) but, unlike the latter, it is free of judgment and it is based entirely on publicly available data, thus filling the gap between the official views of the regulator and those that market participants can form with their own information set. We apply the methodology to annual data on three samples of banks (global, EU and euro area) for the years 2007-2012. We examine the evolution of the SIFIs over time and document the shifs in the relative weights of the major geographic areas. We also discuss the implication of the 2013 update of the identification methodology proposed by the FSB.
Prior studies indicate the protective role of Ultraviolet-B (UVB) radiation in human health, mediated by vitamin D synthesis. In this observational study, we empirically outline a negative association of UVB radiation as measured by ultraviolet index (UVI) with the number of COVID-19 deaths. We apply a fixed-effect log-linear regression model to a panel dataset of 152 countries over 108 days (n = 6524). We use the cumulative number of COVID-19 deaths and case-fatality rate (CFR) as the main dependent variables and isolate the UVI effect from potential confounding factors. After controlling for time-constant and time-varying factors, we find that a permanent unit increase in UVI is associated with a 1.2 percentage points decline in daily growth rates of cumulative COVID-19 deaths [p < 0.01] and a 1.0 percentage points decline in the CFR daily growth rate [p < 0.05]. These results represent a significant percentage reduction in terms of daily growth rates of cumulative COVID-19 deaths (− 12%) and CFR (− 38%). We find a significant negative association between UVI and COVID-19 deaths, indicating evidence of the protective role of UVB in mitigating COVID-19 deaths. If confirmed via clinical studies, then the possibility of mitigating COVID-19 deaths via sensible sunlight exposure or vitamin D intervention would be very attractive.