Continuous Chain Ladder: Reformulating and generalizing a classical insurance problem

Miranda, María Dolores Martínez; Nielsen, Jens Perch; Sperlich, Stefan; Verrall, Richard

doi:10.1016/j.eswa.2013.04.006

Cited by 32 publications

(39 citation statements)

References 38 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CLM forecast uses f CL (l), the CLM estimate for the density of claim duration l, which is given in equation (A1) in the Appendix A. The density representation of the CLM is presented in greater detail in Martínez-Miranda et al (2013). The second forecast, O…”

Section: Forecast Amentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro‐level Analysis?

Wilke

2017

Oxf Bull Econ Stat

View full text Add to dashboard Cite

Forecasting labour market flows is important for budgeting and decision-making in government departments and public administration. Macroeconomic forecasts are normally obtained from time series data. In this article, we follow another approach that uses individual-level statistical analysis to predict the number of exits out of unemployment insurance claims. We present a comparative study of econometric, actuarial and statistical methodologies that base on different data structures. The results with records of the German unemployment insurance suggest that prediction based on individual-level statistical duration analysis constitutes an interesting alternative to aggregate data-based forecasting. In particular, forecasts of up to six months ahead are surprisingly precise and are found to be more precise than considered time series forecasts. JEL Classification numbers: C53, C55, J60. *Thanks are due to Jens P. Nielsen, Lola Martínez, Héctor C. Villanova and Enzo Weber for helpful discussions and to the former two for making their R code for the chain ladder model (-to be released as package 'Double Chain Ladder-) available. The comments of two reviewers have been also gratefully acknowledged.

show abstract

Section: Forecast Amentioning

confidence: 99%

“…Observed data triangle: O tl regression problem, seeMartínez-Miranda et al (2013). The goal is to estimate t and l .…”

mentioning

confidence: 99%

Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro‐level Analysis?

Wilke

2017

Oxf Bull Econ Stat

View full text Add to dashboard Cite

show abstract

“…Neither of these attempts performed as well as the do-validated bandwidth. It would be interesting to see whether indirect cross-validation and do-validation would also be useful to improve other variants of the kernel density estimation problem, such as the problem considered by Gavriliadis and Athanassoulis (2012), Park (2013), Eidous (2012) or Martínez-Miranda et al (2013).…”

Section: Simulation Experiments About Indirect Do-validationmentioning

confidence: 99%

Further theoretical and practical insight to the do-validated bandwidth selector

Mammen

Miranda

Nielsen

et al. 2014

Journal of the Korean Statistical Society

Self Cite

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. and that the recent do-validated method seems to provide the most practical alternative among these methods. In this paper we show step by step how classical cross-validation improves in theory, as well as in practice, from indirectness and that do-validated estimators improve in theory, but not in practice, from further indirectness. This paper therefore provides a strong support for the practical and theoretical properties of do-validated bandwidth selection. Do-validation is currently being introduced to survival analysis in a number of contexts and this paper provides evidence that this might be the immediate step forward. Permanent repository link

show abstract

“…In a nonparametric universe, the estimators resulting from the multiplicative Poisson models are structured histograms. Martínez-Miranda et al (2013) showed the link between actuarial parametric chain ladder-type models [Kuang, Nielsen and Nielsen (2009)] and structured smoothing as considered in this paper.…”

Section: Introductionmentioning

confidence: 99%

Operational time and in-sample density forecasting

Lee¹,

Mammen²,

Nielsen³

et al. 2017

Ann. Statist.

View full text Add to dashboard Cite

This is the published version of the paper.This version of the publication may differ from the final published version. In this paper, we consider a new structural model for in-sample density forecasting. In-sample density forecasting is to estimate a structured density on a region where data are observed and then reuse the estimated structured density on some region where data are not observed. Our structural assumption is that the density is a product of one-dimensional functions with one function sitting on the scale of a transformed space of observations. The transformation involves another unknown one-dimensional function, so that our model is formulated via a known smooth function of three underlying unknown one-dimensional functions. We present an innovative way of estimating the one-dimensional functions and show that all the estimators of the three components achieve the optimal one-dimensional rate of convergence. We illustrate how one can use our approach by analyzing a real dataset, and also verify the tractable finite sample performance of the method via a simulation study. Permanent repository link

show abstract

Continuous Chain Ladder: Reformulating and generalizing a classical insurance problem

Abstract: This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent

Cited by 32 publications

References 38 publications

Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro‐level Analysis?

Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro‐level Analysis?

Further theoretical and practical insight to the do-validated bandwidth selector

Operational time and in-sample density forecasting

Contact Info

Product

Resources

About