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The Intrinsic Estimator, Alternative Estimates, and Predictions of Mortality Trends: A Comment on Masters, Hummer, Powers, Beck, Lin, and Finch

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ABSTRACT

In this article, we discuss a study by Masters et al. (2014), published in Demography. Masters and associates estimated age, period, and cohort (APC) effects on U.S. mortality rates between 1959 and 2009 using the intrinsic estimator (IE). We first argue that before applying the IE, a grounded theoretical justification is needed for its fundamental constraint on minimum variance of the estimates. We next demonstrate IE’s high sensitivity to the type of dummy parameterization used to obtain the estimates. Finally, we discuss challenges in the interpretation of APC models. Our comments are not restricted to the article in question but pertain generally to any research that uses the IE.

No MeSH data available.


IE default (Masters et al. 2014) estimated all-cause mortality rates in black and white females, for cohort (at ages 15–19 and period 2005–2009). Note the different scaling of the y-axes compared with Figs. 3 and 5
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Fig4: IE default (Masters et al. 2014) estimated all-cause mortality rates in black and white females, for cohort (at ages 15–19 and period 2005–2009). Note the different scaling of the y-axes compared with Figs. 3 and 5

Mentions: Furthermore, in the APC regression models with dummy variables, the predicted mortality rate in each cohort depends on which age group and period is chosen. So, comparing cohort trends for blacks and whites by referring to one particular age group and period may lead to conclusions that hold for that specific cohort and period combination but generally not for other cohorts and/or periods. To illustrate our point, we calculated the estimated mortality rates for all cohorts using the first age group and the last period as a baseline (see Fig. 4).Fig. 4


The Intrinsic Estimator, Alternative Estimates, and Predictions of Mortality Trends: A Comment on Masters, Hummer, Powers, Beck, Lin, and Finch
IE default (Masters et al. 2014) estimated all-cause mortality rates in black and white females, for cohort (at ages 15–19 and period 2005–2009). Note the different scaling of the y-axes compared with Figs. 3 and 5
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5016552&req=5

Fig4: IE default (Masters et al. 2014) estimated all-cause mortality rates in black and white females, for cohort (at ages 15–19 and period 2005–2009). Note the different scaling of the y-axes compared with Figs. 3 and 5
Mentions: Furthermore, in the APC regression models with dummy variables, the predicted mortality rate in each cohort depends on which age group and period is chosen. So, comparing cohort trends for blacks and whites by referring to one particular age group and period may lead to conclusions that hold for that specific cohort and period combination but generally not for other cohorts and/or periods. To illustrate our point, we calculated the estimated mortality rates for all cohorts using the first age group and the last period as a baseline (see Fig. 4).Fig. 4

View Article: PubMed Central - PubMed

ABSTRACT

In this article, we discuss a study by Masters et al. (2014), published in Demography. Masters and associates estimated age, period, and cohort (APC) effects on U.S. mortality rates between 1959 and 2009 using the intrinsic estimator (IE). We first argue that before applying the IE, a grounded theoretical justification is needed for its fundamental constraint on minimum variance of the estimates. We next demonstrate IE’s high sensitivity to the type of dummy parameterization used to obtain the estimates. Finally, we discuss challenges in the interpretation of APC models. Our comments are not restricted to the article in question but pertain generally to any research that uses the IE.

No MeSH data available.