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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.


The IE constraint, with b0 (grey line), bIE (solid line), and a possible vector with the set of parameters that generated the data (dashed line)
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Fig1: The IE constraint, with b0 (grey line), bIE (solid line), and a possible vector with the set of parameters that generated the data (dashed line)

Mentions: Figure 1 represents the IE constraint and its components. The vector with the IE estimates (bIE)—set perpendicular to b0—is the shortest route from the origin to the horizontal line that represents the variance explained by all age, period, and cohort groups. Because the vector is shortest, the IE estimates have the smallest variance; that is, the sum of squared IE estimates is smallest. Any other set of estimates with exactly the same explanatory power will have a larger variance in its estimates. Whether the parameters that generated the data also have smallest variance, however, is unknown. Therefore, the IE estimates can be severely biased, as depicted in Fig. 1, in which the true parameter vector is very different from the IE estimates vector.Fig. 1


The Intrinsic Estimator, Alternative Estimates, and Predictions of Mortality Trends: A Comment on Masters, Hummer, Powers, Beck, Lin, and Finch
The IE constraint, with b0 (grey line), bIE (solid line), and a possible vector with the set of parameters that generated the data (dashed line)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC5016552&req=5

Fig1: The IE constraint, with b0 (grey line), bIE (solid line), and a possible vector with the set of parameters that generated the data (dashed line)
Mentions: Figure 1 represents the IE constraint and its components. The vector with the IE estimates (bIE)—set perpendicular to b0—is the shortest route from the origin to the horizontal line that represents the variance explained by all age, period, and cohort groups. Because the vector is shortest, the IE estimates have the smallest variance; that is, the sum of squared IE estimates is smallest. Any other set of estimates with exactly the same explanatory power will have a larger variance in its estimates. Whether the parameters that generated the data also have smallest variance, however, is unknown. Therefore, the IE estimates can be severely biased, as depicted in Fig. 1, in which the true parameter vector is very different from the IE estimates vector.Fig. 1

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.