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Signal Extraction for Nonstationary Time Series with Diverse Sampling Rules
| Content Provider | Scilit |
|---|---|
| Author | Trimbur, Thomas McElroy, Tucker |
| Copyright Year | 2017 |
| Abstract | This paper presents a flexible framework for signal extraction of time series measured as stock or flow at diverse sampling frequencies. Our approach allows for a coherent treatment of series across diverse sampling rules, a deeper understanding of the main properties of signal estimators and the role of measurement, and a straightforward method for signal estimation and interpolation for discrete observations. We set out the essential theoretical foundations, including a proof of the continuous-time Wiener-Kolmogorov formula generalized to nonstationary signal or noise. Based on these results, we derive a new class of low-pass filters that provide the basis for trend estimation of stock and flow time series. Further, we introduce a simple and accurate method for low-frequency signal estimation and interpolation in discrete samples, and examine its properties for simulated series. Illustrations are given on economic data. |
| Related Links | http://www.degruyter.com/downloadpdf/j/jtse.2017.9.issue-1/jtse-2014-0026/jtse-2014-0026.xml |
| ISSN | 21946507 |
| e-ISSN | 19411928 |
| DOI | 10.1515/jtse-2014-0026 |
| Journal | Journal of Time Series Econometrics |
| Issue Number | 1 |
| Volume Number | 9 |
| Language | English |
| Publisher | Walter de Gruyter GmbH |
| Publisher Date | 2017-01-01 |
| Access Restriction | Open |
| Subject Keyword | Journal of Time Series Econometrics Automotive Engineering Statistics and Probability Continuous Time Models Hodrick-prescott Low-pass Filters Trends Turning Points Journal: Journal of Time Series Econometrics, Issue- 9 |
| Content Type | Text |
| Resource Type | Article |
| Subject | Economics and Econometrics |
