Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/79445 
Year of Publication: 
2000
Series/Report no.: 
Working Paper No. 00-2
Publisher: 
University of California, Department of Economics, Davis, CA
Abstract: 
This paper is a general investigation of temporal aggregation in time series analysis. It encompasses traditional research on time aggregation as a particular case and extends the analysis to irregular intervals of aggregation. The Data Generating Process is allowed to evolve at regular, deterministic-irregular or even stochastic intervals of time (operational time). The time scale of this process is then transformed to generate the observational time process. This transformation can be deterministic (such as the familiar aggregation of monthly data into quarters) or more generally, stochastic (such as aggregating stock market quotes by the hour). In general, the observational time model exhibits persistence, time-varying parameters and non-spherical disturbances. Consequently, we review detection, specification, estimation and structural inference in this context, provide new solutions to these issues, and apply our results to high frequency, FX data.
Subjects: 
time aggregation
time-scale transformation
irregularly spaced date
autoregressive conditional intensity model
JEL: 
C13
C22
C43
Document Type: 
Working Paper

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