Research Discussion Papers, Bank of Finland
No 33/2009:
An analysis of the embedded frequency content of macroeconomic indicators and their counterparts using the Hilbert-Huang transform
Patrick M Crowley ()
and Tony Schildt ()
Abstract: Many indicators of business and growth cycles have been
constructed by both private and public agencies and are now in use as
monitoring devices of economic conditions and for forecasting purposes. As
these indicators are largely composite constructs using other economic
data, their frequency composition is likely different to that of the
variables they are used as indicators for.
In this paper we use the
Hilbert-Huang transform, which comprises the empirical mode decomposition
(EMD) and the Hilbert spectrum, in order to analyse the frequency content
of comparable OECD confidence indicators and national sentiment indicators
for industrial production and consumption. We then compare these with the
frequency content of both industrial production and real consumption growth
data. The Hilbert-Huang methodology first uses a sifting process (EMD) to
identify the embedded frequencies within a time series, and the changing
nature of these embedded frequencies (IMFs) can then be analysed by
estimating the instantaneous frequency (using the Hilbert spectrum). This
methodology has several advantages over conventional spectral analysis: it
handles non-stationary and non-linear processes, and it can cope with short
data series.
The aim of this paper is to decompose both indicator and
actual economic variables to evaluate i) whether the number of IMFs are
equivalent in both indicators and actual variables and ii) to see which
frequencies are accounted for in indicators and which frequencies are
not.
Keywords: economic growth; Hilbert-Huang transform; empirical mode decomposition; frequency domain; economic indicators; (follow links to similar papers)
JEL-Codes: C63; E21; E32; (follow links to similar papers)
51 pages, December 22, 2009
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