The goal of this paper is to theoretically and empirically demonstrate the consequences of different imputation methods using recent data from the International Price Program. We suppose that prices are missing due to random or erratic reporting. We consider three different imputation methods: carry-forward, which just assumes that the missing price is the same as in the previous period; cell-mean, which imputes the missing price using either the short-term or long-term index for related commodities; and linear interpolation, which uses the last and next observations for the item to linearly interpolate. Certain hybrid techniques, combining either carry-forward or cell-mean with linear interpolation, are also considered.
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Published price indexes are nearly always constructed from individual prices collected by somesampling framework, were the samples are chosen, in part, to minimize the time and expenseinvolved in collecting the prices. In particular, the time spent by reporting firms or consumers isquite rightly treated as precious. It is inevitable that questionnaires sent out in repeated monthswill sometimes not be returned. For example, about one-quarter of the individual items trackedunder the International Price Program (IPP) of the Bureau of Labor Statistics (BLS) do not report a price in any given month, though of these, about 60% eventually supply a price quote for thatmonth or a later month.
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