Working Paper: NBER ID: w12671
Authors: Ulrich Mueller; Mark W. Watson
Abstract: We develop a framework to assess how successfully standard times eries models explain low-frequency variability of a data series. The low-frequency information is extracted by computing a finite number of weighted averages of the original data, where the weights are low-frequency trigonometric series. The properties of these weighted averages are then compared to the asymptotic implications of a number of common time series models. We apply the framework to twenty U.S. macroeconomic and financial time series using frequencies lower than the business cycle.
Keywords: Low-frequency variability; Time series models; Macroeconomics; Financial time series
JEL Codes: C22; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| standard time series models (C22) | low-frequency variability (E32) |
| unit root model (C22) | low-frequency variability (E32) |
| low-frequency heteroskedasticity (C22) | models' fit (C52) |
| low-frequency variability in second moment (C32) | standard model assumptions (C20) |
| nontrivial dynamics below business cycle frequencies (E32) | rejection of i0 model (C52) |