Abstract
Temporal disaggregation methods are used to disaggregate low frequency time series to higher frequency series, where either the sum, the average, the first or the last value of the resulting high frequency series is consistent with the low frequency series. Temporal disaggregation can be performed with or without one or more high frequency indicator series. The package tempdisagg is a collection of several methods for temporal disaggregation.
Highlights
Not having a time series at the desired frequency is a common problem for researchers and analysts
While there is no way to fully make up for the missing data, there are useful workarounds: with the help of one or more high frequency indicator series, the low frequency series may be disaggregated into a high frequency series
Denton (Denton, 1971) and Denton-Cholette (e.g. Dagum and Cholette, 2006) are primarily concerned with movement preservation, generating a series that is similar to the indicator series whether or not the indicator is correlated with the low frequency series
Summary
Not having a time series at the desired frequency is a common problem for researchers and analysts. Dagum and Cholette, 2006) are primarily concerned with movement preservation, generating a series that is similar to the indicator series whether or not the indicator is correlated with the low frequency series These methods can disaggregate a series without an indicator. Chow-Lin, Fernandez and Litterman use one or several indicators and perform a regression on the low frequency series. All disaggregation methods ensure that either the sum, the average, the first or the last value of the resulting high frequency series is consistent with the low frequency series. The aim of temporal disaggregation is to find an unknown high frequency series y, whose sums, averages, first or last values are consistent with a known low frequency series yl (The subscript l denotes low frequency variables). The diversity of temporal disaggregation methods can be narrowed by putting the methods in a two-step framework: First, a preliminary quarterly series p has to be determined; second, the
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