A Flexible Functional Form Approach To Mortality Modeling: Do We Need Additional Cohort Dummies?

Author(s):  
Han Li ◽  
Colin O'hare ◽  
Farshid Vahid
Author(s):  
Naila Alam ◽  
Muhammad Hanif

The Model assisted estimators are approximately design unbiased, consistent and provides robustness in the case of large sample sizes. The model assisted estimators result in reduction of the design variance if underlying model reasonably defines the regression relationship.  If the model is misspecified, then model assisted estimators might result in an increase of the design variance but remain approximately design unbiased and show robustness against model-misspecification. The well-known model assisted estimators, generalized regression estimators are members of a larger class of calibration estimators. Calibration method generates calibration weights that meet the calibration constraints and have minimum distance from the sampling design weights. By using different distance measures, classical calibration approach generates different calibration estimators but with asymptotically identical properties. The constraint of distance minimization was reduced for studying the properties of calibration estimators by proposing a simple functional form approach. The approach generates calibration weights that prove helpful to control the changes in calibration weights by using different choices of auxiliary variable’s functions.  This paper is an extended work on model assisted approach by using functional form of calibration weights. Some new model assisted estimators are considered to get efficient and stabilized regression weights by introducing a control matrix. The asymptotic un-biasedness of the proposed estimators is verified and the expressions for MSE are derived in three different cases.  A simulation study is done to compare and evaluate the efficiency of the proposed estimators with some existing model assisted estimators.


2019 ◽  
Vol 10 (11) ◽  
pp. 2020-2033
Author(s):  
Rubén Cabrera ◽  
Jhoana Díaz-Larrea ◽  
Schery Umanzor ◽  
Laura Georgina Núñez García

2018 ◽  
Vol 29 (2) ◽  
pp. 353-371 ◽  
Author(s):  
William A. Barnett ◽  
Neepa B. Gaekwad

1970 ◽  
Vol 10 (2) ◽  
pp. 272-280
Author(s):  
Richard C. Porter

A common problem of finite-horizon planning models is that there is no logical determinant of investment in the final year (s). Where post-horizon production is not valued by a model, later-year investment, whose sole function is creation of capacity for post-horizon output, looks as incongruous as last rites for an atheist. A number of artificial devices have been developed to handle this difficulty1, but one predominates: to assume that terminal-year investment is a function of terminal-year output. The purpose of this note is to show: 1) how varied and arbitrary are the assumed functions (Section I); 2) that the terminal-year variables and the apparent feasibility of the resulting Plan are highly sensitive to the choice of function (Section II); and 3) that the arbitrariness of functional form is inevitable in the sense that generally acceptable criteria do not much restrict the choice (Section III). Throughout this note, we shall neglect four complexities that are not essential to the problem at hand. One, the marginal capital-output ratio (


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