On signal-noise decomposition of timeseries using the continuous wavelet transform: Application to sunspot index

We show that the continuous wavelet transform can provide a unique decomposition of a timeseries in to 'signal-like' and 'noise-like' components: From the overall wavelet spectrum two mutually independent skeleton spectra can be extracted, allowing the separate detection and monitoring in even non-stationary timeseries of the evolution of (a) both stable but also transient, evolving periodicities, such as the output of low dimensional dynamical systems and (b) scale-invariant structures, such as discontinuities, self-similar structures or noise. An indicative application to the monthly-averaged sunspot index reveals, apart from the well-known 11-year periodicity, 3 of its harmonics, the 2-year periodicity (quasi-biennial oscillation, QBO) and several more (some of which detected previously in various solar, earth-solar connection and climate indices), here proposed being just harmonics of the QBO, in all supporting the double-cycle solar magnetic dynamo model (Benevolenskaya, 1998, 2000). The scale maximal spectrum reveals the presence of 1/f fluctuations with timescales up to 1 year in the sunspot number, indicating that the solar magnetic configurations involved in the transient solar activity phenomena with those characteristic timescales are in a self-organized-critical state (SOC), as previously proposed for the solar flare occurence (Lu and Hamilton, 1991).Comment: 22 pages, 2 figure

The great moderation under the microscope: decomposition of macroeconomic cycles in US and UK aggregate demand

In this paper the relationship between the growth of real GDP components is explored in the frequency domain using both static and dynamic wavelet analysis. This analysis is carried out separately for the US and UK using quarterly data, and the results are found to be substantially different for the two countries. One of the key findings of this research is that the ‘great moderation’ shows up only at certain frequencies, and not in all components of real GDP. We use these results to explain why the incidence of the great moderation has been so patchy across GDP components, countries and time periods. This also explains why it has been so hard to detect periods of moderation (or other periods) reliably in the aggregate data. We argue this cannot be done without separating the GDP components into their frequency components over time. Our results show why: the predictions of traditional real business cycle theory often appear not to be upheld in the data.business cycles; growth cycles; discrete wavelet analysis; US real GDP; UK real GDP

The yield curve and the macro-economy across time and frequencies

This paper assesses the relation between the yield curve and the main macroeconomic variables in the U.S. between early 1960s and 2010 across time and frequencies, using wavelet analyses. The shape of the yield curve is modelled by latent factors corresponding to its level, slope and curvature, estimated by maximum likelihood with the Kalman filter. The macroeconomic variables measure economic activity, unemployment, inflation and the fed funds rate. The cross wavelet tools employed — coherency and phase difference —, the set of variables and the length of the sample, allow for a thorough appraisal of the timevariation and structural breaks in the direction, intensity, synchronization and periodicity of the relation between the yield curve and the macro-economy. Our evidence establishes a number of new stylized facts on the yield curve-macro relation; and sheds light on several results found in the literature, which could not have been achieved with analyses conducted strictly in the time-domain (as most of the literature) or purely in the frequency-domain.Macro-finance; Yield curve; Kalman filter; Continuous wavelet transform; Wavelet coherency; Phase-difference.

The yield curve and the macro-economy across time and frequencies

This paper assesses the relation between the yield curve and the main macroeconomic variables in the U.S. between early 1960s and 2009 across time and frequencies, using wavelet analyses. The shape of the yield curve is modelled by latent factors corresponding to its level, slope and curvature, estimated by maximum likelihood with the Kalman filter. The macroeconomic variables measure econmic activity, unemployment, inflation and the fed funds rate. The cross wavelet tools employed - coherency and phase difference - , the set of variables and the length of the sample, allow for a thorough appraisal of the time- variation and structural breaks in the direction,intensity,synchronization and periodicity of the relation between the yield curve and the macro-economy. Our evidence establishes a number of new stylized facts on the yield curve-macro relation; and sheds light on several results found in the literature, which could not have been achieved with analyses conducted strictly in the time-domain(as most of the literature)or purely in the frequency-domain.Macro-finance; Yield curve; Kalman filter; Continuous wavelet transform;Wavelet coherency;Phase-difference

An analysis of spending behaviour under liquidity constraints with an application to financial hedging

Imperial Users onl

A time-frequency analysis of the Canadian macroeconomy and the yield curve

We use wavelet analysis to study the relationship between the yield curve and macroeconomic indicators in Canada. We rely on the Nelson-Siegel approach to model the zero coupon yield curve, and use the Kalman lter to estimate its time-varying factors: the level, the slope and the curvature. Apart from the bidirectional yield-macro relation, the paper broadens the existing literature by exploring the link between the monetary policy and the yield curve.COMPETE 2020, Portugal 2020, FEDER, FCTinfo:eu-repo/semantics/publishedVersio

Univariate Potential Output Estimations for Hungary

Potential output figures are important ingredients of many macroeconomic models and are routinely applied by policy makers and global agencies. Despite its widespread use, estimation of potential output is at best uncertain and depends heavily on the model. The task of estimating potential output is an even more dubious exercise for countries experiencing huge structural changes, such as transition countries. In this paper we apply univariate methods to estimate and evaluate Hungarian potential output, paying special attention to structural breaks. In addition to statistical evaluation, we also assess the appropriateness of various methods by expertise judgement of the results, since we argue that mechanical adoption of univariate techniques might led to erroneous interpretation of the business cycle. As all methods have strengths and weaknesses, we derive a single measure of potential output by weighting those methods that pass both the statistical and expertise criteria. As standard errors, which might be used for deriving weights, are not available for some of the methods, we base our weights on similar but computable statistics, namely on revisions of the output gap for all dates by recursively estimating the models. Finally, we compare our estimated gaps with the result of the only published Hungarian output gap measure of Darvas-Simon (2000b), which is based on an economic model.combination, detrending, output gap, revision.