A MIXTURE MODEL FOR PAYMENTS AND PAYMENT NUMBERS IN CLAIMS RESERVING

2016 ◽  
Vol 48 (1) ◽  
pp. 25-53 ◽  
Author(s):  
Patrizia Gigante ◽  
Liviana Picech ◽  
Luciano Sigalotti
Keyword(s):  
Random Effects ◽  
Linear Models ◽  
External Information ◽  
Parameter Estimates ◽  
Prediction Errors ◽  
Poisson Regression Model ◽  
External Data ◽  
Fixed And Random Effects ◽  
Likelihood Approach ◽  
The Impact

AbstractWe consider a Tweedie's compound Poisson regression model with fixed and random effects, to describe the payment numbers and the incremental payments, jointly, in claims reserving. The parameter estimates are obtained within the framework of hierarchical generalized linear models, by applying the h-likelihood approach. Regression structures are allowed for the means and also for the dispersions. Predictions and prediction errors of the claims reserves are evaluated. Through the parameters of the distributions of the random effects, some external information (e.g. a development pattern of industry wide-data) can be incorporated into the model. A numerical example shows the impact of external data on the reserve and prediction error evaluations.

Estimating a model of sportfishing trip expenditures using a quasi-maximum likelihood approach

2016 ◽  
Vol 23 (2) ◽  
pp. 448-459 ◽  
Author(s):  
Richard T. Melstrom
Keyword(s):  
Maximum Likelihood ◽  
Exponential Model ◽  
Parameter Estimates ◽  
Maximum Likelihood Approach ◽  
Likelihood Approach ◽  
Quasi Maximum Likelihood ◽  
Species Preferences ◽  
The Impact ◽  
Consistent Parameter

This article presents an exponential model of tourist expenditures estimated by a quasi-maximum likelihood (QML) technique. The advantage of this approach is that, unlike conventional OLS and Tobit estimators, it produces consistent parameter estimates under conditions of a corner solution at zero and heteroscedasticity. An application to sportfishing evaluates the role of socioeconomic demographics and species preferences on trip spending. The bias from an inappropriate estimator is illustrated by comparing the results from QML and OLS estimation, which shows that OLS significantly overstates the impact of trip duration on trip expenditures compared with the QML estimator. Both sets of estimates imply that trout and bass anglers spend significantly more on their fishing trips compared with other anglers.

scholarly journals COMPARATIVE ANALYSIS OF THE IMPACT OF GLOBALISATION ON HUMAN WELFARE IN SUB-SAHARAN AFRICA

10.23856/3002 ◽  
2018 ◽  
Vol 30 (5) ◽  
pp. 25-42
Author(s):  
Olukayode Emmanuel Maku ◽  
Bolaji Adesola Adesoye ◽  
Awoyemi Olayiwola Babasanya ◽  
Oluwaseyi Adedayo Adelowokan
Keyword(s):  
Comparative Analysis ◽  
Random Effects ◽  
Trade Openness ◽  
Sub Saharan Africa ◽  
Human Welfare ◽  
Fixed And Random Effects ◽  
The World ◽  
Sub Saharan ◽  
The Impact ◽  
Ssa Countries

The world has become more linked owing to the increased intensity of globalisation across regions. Sub-Saharan Africa (SSA) has become more relatively integrated into the world economy as shown by increasing degree of trade openness and foreign direct investment. Over the same period, quality of life of people in SSA in terms of access to basic necessity, monetary and non-monetary indices of poverty have been on the declining trend. This study adopted endogenous growth theory in analysing the comparative effects of globalisation between the highly and weakly globalised economies in SSA countries. Four channels of transmission of impact of globalisation were considered: trade openness, financial and capital flows labour mobility and access to telephone. Data for 16 SSA countries – 8 weakly globalised and 8 strongly globalised countries based on KOF globalisation index, were sourced from the world Development indicator for the period of 1980-2012. The feasible generalised least square (GLS) estimator was utilized to estimate the fixed and random effects panel regression models. Hausman test was used to determine the efficient estimator between fixed and random effects. All estimated coefficients were evaluated at 5% level of significance. The outcome of the comparative analysis revealed a mix result in some cases and unidirectional in some. In all, countries with higher intensity of globalisation have a greater improvement in their human welfare indicators compared to countries with weak globalisation indices. The study then recommended an improved reform in global integration to enable the region maximize the immense benefits inherent in global connections.

scholarly journals Including random effects in statistical models in ecology: fewer than five levels?

2021 ◽  
Author(s):  
Dylan G.E. Gomes
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Linear Models ◽  
Random Effect ◽  
Mixed Effects ◽  
Parameter Estimates ◽  
Type I ◽  
Type I Errors ◽  
Ecological Sites ◽  
Generalized Linear Mixed Effects

AbstractAs generalized linear mixed-effects models (GLMMs) have become a widespread tool in ecology, the need to guide the use of such tools is increasingly important. One common guideline is that one needs at least five levels of a random effect. Having such few levels makes the estimation of the variance of random effects terms (such as ecological sites, individuals, or populations) difficult, but it need not muddy one’s ability to estimate fixed effects terms – which are often of primary interest in ecology. Here, I simulate ecological datasets and fit simple models and show that having too few random effects terms does not influence the parameter estimates or uncertainty around those estimates for fixed effects terms. Thus, it should be acceptable to use fewer levels of random effects if one is not interested in making inference about the random effects terms (i.e. they are ‘nuisance’ parameters used to group non-independent data). I also use simulations to assess the potential for pseudoreplication in (generalized) linear models (LMs), when random effects are explicitly ignored and find that LMs do not show increased type-I errors compared to their mixed-effects model counterparts. Instead, LM uncertainty (and p values) appears to be more conservative in an analysis with a real ecological dataset presented here. These results challenge the view that it is never appropriate to model random effects terms with fewer than five levels – specifically when inference is not being made for the random effects, but suggest that in simple cases LMs might be robust to ignored random effects terms. Given the widespread accessibility of GLMMs in ecology and evolution, future simulation studies and further assessments of these statistical methods are necessary to understand the consequences of both violating and blindly following simple guidelines.

scholarly journals Classification Ratemaking Using Decision Tree in the Insurance Market of Bosnia and Herzegovina

2020 ◽  
Vol 15 (2) ◽  
pp. 124-139
Author(s):  
Amela Omerašević ◽  
Jasmina Selimović
Keyword(s):  
Data Mining ◽  
Bosnia And Herzegovina ◽  
Linear Models ◽  
Risk Model ◽  
Parameter Estimates ◽  
East European ◽  
Reliable Parameter ◽  
Mining Methods ◽  
Claims Frequency ◽  
The Impact

AbstractThis paper investigates the impact of risk classification on life insurance ratemaking with particular reference to Bosnia and Herzegovina (BiH). The research is based on a sample of over eighteen thousand insurance policies for passenger vehicles collected over the period 2015-2020. In our empirical investigation we develop a standard risk model based on the application of Poisson Generalized linear models (GLM) for claims frequency estimate and Gamma GLM for claim severity estimate. The analysis reveals that GLM does not provide a reliable parameter estimates for Multi-level factor (MLF) categorical predictors. Although GLM is widely used method to deter insurance premiums, improvements of GLM by using the data mining methods identified in this paper may solve practical challenges for the risk models. The popularity of applying data mining methods in the actuarial community has been growing in recent years due to its efficiency and precision. These models are recommended to be considered in BiH and South East European region in general.

Do Women Directors Impact Financial Outcomes? The Indian Evidence

2020 ◽  
Vol 13 (2) ◽  
pp. 119-139
Author(s):  
Anil Kumar ◽  
Narander Kumar Nigam ◽  
Kirtivardhan Singh
Keyword(s):  
Stock Returns ◽  
Random Effects ◽  
Firm Growth ◽  
Resource Dependence ◽  
Women Directors ◽  
Market Returns ◽  
Financial Outcomes ◽  
Board Processes ◽  
Fixed And Random Effects ◽  
The Impact

This article investigates the impact of women directors on financial outcomes—return and risk of Indian companies. It applies fixed and random effects Tobit regressions to examine the effect of female directors on financial outcomes (returns and risk) of the firm, controlling promoters’ shareholding, leverage, firm growth and age, board size and board meetings. The study does not find any support to agency and resource dependence theories because the proportion of women directors in most Indian boards is too small to make much impact. However, it has a moderating influence to reduce variations in accounting profits and stock returns. The investors reward also meeting the regulatory quota of woman member on the boards by higher market returns indicating a signalling effect. The study adds an understanding of quota induced women directors’ influence on the firm’s financial outcomes. However, the regulators should be cautious in mandating induction of women members on the boards as they might be inexperienced or lack the needed grounding to effectively influence board processes.

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