Abstract
This paper shows that the trimmed Whittle estimation of the SVAR is superior to filtering (or differencing) undesired, low-frequency fluctuations that may arise in macroeconomic data. Pre-filtering destroys the low-frequency range of the spectrum, thus biasing the estimated parameters and the responses of the variables to shocks. The proposed method, by contrast, accounts for the undesired fluctuations while overcoming these drawbacks. Furthermore, the method remains reliable even when the observed low-frequency variability has been incorrectly considered as external to the SVAR. An empirical application that examines the effect of technology shocks on hours worked is provided to illustrate the results. We find the response of hours positive and similar using both long and short-run identification restrictions, thus providing a solution to a wide debate in the business cycle literature.
Funding source: Ministerio de Economia y Competividad
Award Identifier / Grant number: ECO2016-75410-P
Funding statement: Perez-Laborda acknowledges financial support from the Ministerio de Economia y Competividad (Funder Id 10.13039/501100003329, ECO2016-75410-P, Spain).
Acknowledgements
We are grateful to an anonymous referee for helpful suggestions and constructive comments that greatly improved the paper. All remaining errors are our own.
Appendix
Results from the robustness checks
This Appendix presents selected results from the robustness checks in Section 3.3.
Figure 7 and Figure 8 depict the simulation results for the cross-response of the first variable using the alternative specifications for the external process. For the shift in the mean, we divide the sample into three equal subsamples and assume different means for each part:
which generates a cycle with similar amplitude to the trigonometric cycle. For the random walk trend, Ct = Ct–1+ξt, the standard error of the innovations is set to std(ξt) = 0.3σ1, where
Simulation results for data contaminated with the shift in the mean.
Notes: See Figure 2.
Simulation results for data contaminated with the random walk trend.
Notes: See Figure 2.
Finally, Figure 9 and Figure 10 present the results increasing the frequency of the data from quarterly to monthly, and BP filtering the two series, respectively.
Simulation results for T = 720 monthly data contaminated with the trigonometric cycle.
Notes: See Figure 2.
Simulation results BP filtering the two series.
Notes: See Figure 2.
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Supplementary Material
The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/snde-2018-0030).
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