Abstract
It is a common occurrence in empirical work that some time series are available on a monthly basis, some quarterly, and some only on an annual basis, and one wishes to construct a higher-frequency series from a low-frequency one (say quarterly from annual) given information on related time series that are available at a higher frequency. Two examples of this are: (1) stock variables like plant and equipment, information on which may be available only at particular times of the year (say the first quarter); and (2) flow variables such as national income or price level, which may be available only for the entire year. In the case of a stock variable, one would want the interpolated series to agree with the actual series at the point in time when information on the latter is available. In the case of a flow variable, one would want the sum (or average, as the case may be) of the interpolated (say quarterly) series to agree with the the actual annual series.
Work supported by NSF grant SES86–07652. Section 2.2 of this paper builds on some earlier formulations by Andreas Hornstein. We wish to thank Professor Michael Powell for furnishing us with the source code for his nonlinear-programming algorithm (Powell, 1989), and Wayne Fuller, Baldev Raj, and Thomas Url for stimulating comments.
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Chipman, J.S., Lapham, B.J. (1995). Interpolation of Economic Time Series, with Application to German and Swedish Data. In: Url, T., Wörgötter, A. (eds) Econometrics of Short and Unreliable Time Series. Studies in Empirical Economics. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-99782-2_6
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