Multi-population Mortality Projection: The Augmented Common Factor Model with Structural Breaks

2020 ◽  
Author(s):  
PENGJIE WANG ◽  
Athanasios A. Pantelous ◽  
Farshid Vahid
Keyword(s):  
Structural Breaks ◽  
Factor Model ◽  
Common Factor ◽  
Common Factor Model ◽  
Mortality Projection ◽  
Population Mortality

SMOOTHING POISSON COMMON FACTOR MODEL FOR PROJECTING MORTALITY JOINTLY FOR BOTH SEXES

2018 ◽  
Vol 48 (02) ◽  
pp. 509-541 ◽  
Author(s):  
David Pitt ◽  
Jackie Li ◽  
Tian Kang Lim
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Linear Constraints ◽  
Female Ratio ◽  
Common Factor Model ◽  
Mortality Projection ◽  
Out Of Sample ◽  
Smoothing Process ◽  
Sample Testing ◽  
Male To Female

AbstractWe consider a modification to the Poisson common factor model and utilise a generalised linear model (GLM) framework that incorporates a smoothing process and a set of linear constraints. We extend the standard GLM model structure to adopt Lagrange methods and P-splines such that smoothing and constraints are applied simultaneously as the parameters are estimated. Our results on Australian, Canadian and Norwegian data show that this modification results in an improvement in mortality projection in terms of producing more accurate forecasts in the out-of-sample testing. At the same time, projected male-to-female ratio of death rates at each age converges to a constant and the residuals of the models are sufficiently random, indicating that the use of smoothing does not adversely affect the fit of the model. Further, the irregular patterns in the estimates of the age-specific parameters are moderated as a result of smoothing and this model can be used to produce more regular projected life tables for pricing purposes.

A DOUBLE COMMON FACTOR MODEL FOR MORTALITY PROJECTION USING BEST-PERFORMANCE MORTALITY RATES AS REFERENCE

2021 ◽  
pp. 1-26
Author(s):  
Jackie Li ◽  
Maggie Lee ◽  
Simon Guthrie
Keyword(s):  
Best Practice ◽  
Factor Model ◽  
Common Factor ◽  
Model Structure ◽  
Death Rates ◽  
Common Factor Model ◽  
Mortality Projection ◽  
Proposed Model ◽  
Out Of Sample ◽  
Collective Experience

Abstract We construct a double common factor model for projecting the mortality of a population using as a reference the minimum death rate at each age among a large number of countries. In particular, the female and male minimum death rates, described as best-performance or best-practice rates, are first modelled by a common factor model structure with both common and sex-specific parameters. The differences between the death rates of the population under study and the best-performance rates are then modelled by another common factor model structure. An important result of using our proposed model is that the projected death rates of the population being considered are coherent with the projected best-performance rates in the long term, the latter of which serves as a very useful reference for the projection based on the collective experience of multiple countries. Our out-of-sample analysis shows that the new model has potential to outperform some conventional approaches in mortality projection.

From Bi-Dimensionality to Uni-Dimensionality in Self-Report Questionnaires

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Bjarne Schmalbach ◽  
Markus Zenger ◽  
Michalis P. Michaelides ◽  
Karin Schermelleh-Engel ◽  
Andreas Hinz ◽  
...  
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Self Regulation ◽  
Model Fit ◽  
Self Report ◽  
External Criterion ◽  
Common Factor Model ◽  
Random Intercept ◽  
Using Data ◽  
Core Self

Abstract. The common factor model – by far the most widely used model for factor analysis – assumes equal item intercepts across respondents. Due to idiosyncratic ways of understanding and answering items of a questionnaire, this assumption is often violated, leading to an underestimation of model fit. Maydeu-Olivares and Coffman (2006) suggested the introduction of a random intercept into the model to address this concern. The present study applies this method to six established instruments (measuring depression, procrastination, optimism, self-esteem, core self-evaluations, and self-regulation) with ambiguous factor structures, using data from representative general population samples. In testing and comparing three alternative factor models (one-factor model, two-factor model, and one-factor model with a random intercept) and analyzing differential correlational patterns with an external criterion, we empirically demonstrate the random intercept model’s merit, and clarify the factor structure for the above-mentioned questionnaires. In sum, we recommend the random intercept model for cases in which acquiescence is suspected to affect response behavior.

Structural Equation Modeling with Factors and Composites

2017 ◽  
Vol 13 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Ned Kock
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Factor Model ◽  
Common Factor ◽  
Equation Modeling ◽  
A Algorithm ◽  
Multivariate Statistical ◽  
Full Information Maximum Likelihood ◽  
Common Factor Model ◽  
Path Coefficients

Recent methodological developments building on partial least squares (PLS) techniques and related ideas have significantly contributed to bridging the gap between factor-based and composite-based structural equation modeling (SEM) methods. PLS-SEM is extensively used in the field of e-collaboration, as well as in many other fields where multivariate statistical analyses are employed. The author compares results obtained with four methods: covariance-based SEM with full information maximum likelihood (FIML), factor-based SEM with common factor model assumptions (FSEM1), factor-based SEM building on the PLS Regression algorithm (FSEM2), and PLS-SEM employing the Mode A algorithm (PLSA). The comparison suggests that FSEM1 yields path coefficients and loadings that are very similar to FIML's; and that FSEM2 yields path coefficients that are very similar to FIML's and loadings that are very similar to PLSA's.

Analysis of variances and covariances is misleading as a guide to a common factor model

Intelligence ◽  
1981 ◽  
Vol 5 (2) ◽  
pp. 157-163 ◽  
Author(s):  
Lloyd G. Humphreys ◽  
Randolph K. Park
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Common Factor Model

A modified common factor model for modelling mortality jointly for both sexes

2020 ◽  
Vol 37 (2) ◽  
pp. 181-212
Author(s):  
Kenneth Wong ◽  
Jackie Li ◽  
Sixian Tang
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Common Factor Model
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