The Typical Spectral Shape of an Economic Variable: A Visual Guide with 100 Examples
Granger (1966) describes how the spectral shape of an economic variable concentrates spectral mass at low frequencies, declining smoothly as frequency increases. Despite a discussion about how to assess robustness of his results, the empirical exercise focused on the evidence obtained from a handful of series. In this paper, I focus on a broad range of economic variables to investigate their spectral shape. Hence, through different examples taken from both actual and simulated series, I provide an intuition of the typical spectral shape of a wide range of economic variables and the impact of their typical treatments. After performing 100 different exercises, the results show that Granger's assertion holds more often than not. I also confirm that the basic shape holds for a number of transformations, time aggregations, series' anomalies, variables of the real economy, and also, but to a lesser extent, financial variables. Especially fuzzy cases are those that exhibit some degree of transition to a different regime, as are those estimated with a very short bandwidth.
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- Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
- Daniel Levy & Hashem Dezhbakhsh, 2003.
"On the typical spectral shape of an economic variable,"
Applied Economics Letters,
Taylor & Francis Journals, vol. 10(7), pages 417-423.
- Daniel Levy & Hashem Dezhbakhsh, 2002. "On the Typical Spectral Shape of an Economic Variable," Emory Economics 0203, Department of Economics, Emory University (Atlanta).
- Daniel Levy & Hashem Dezhbakhsh, 2002. "On the Typical Spectral Shape of an Economic Variable," Working Papers 2002-16, Bar-Ilan University, Department of Economics.
- Daniel Levy & Hashem Dezhbakhsh, 2004. "On the Typical Spectral Shape of an Economic Variable," Macroeconomics 0402017, EconWPA.
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