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Abstract:
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While most health surveys collect substantial data on large race-ethnic groups, little is known about the extent of disparities for smaller groups, such as Asian subgroups and American Indians/Alaska Natives. In fact, major national health surveys have limited or no ability to assess adequately the health of major race-ethnic subgroups smaller than that of Mexican-American. One solution to improving the precision of estimates is to pool data over time. The simplest solution, using the average of several recent years to estimate the present year, is biased in the presence of a linear trend and inefficient in the presence of autocorrelation. We propose and evaluate a Kalman filter approach in which we detrend repeated cross-sections and exponentially weight on the basis of an autocorrelation estimated by pooling across larger race-ethnic groups.
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