1,720,993 research outputs found
Business cycle synchronization according to wavelets – the case of Poland and the euro zone member countries
In the paper time-frequency analysis in the form of the maximal overlap discrete wavelet transform (MODWT) and its complex variant – the maximal overlap discrete Hilbert wavelet transform (MODHWT) is applied to study changing patterns of business cycle synchronization between Poland and 8 euro zone member countries (France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal and Spain). We also touch upon the endogeneity hypothesis of the optimum currency area criteria and ask about the recent changes in business cycle variability and their influence on the level of synchronization. Wavelet analysis is a very convenient way of studying business cycles as it possesses good localization properties and is highly efficient in extracting time- -varying frequency content of time series. In the paper we make use of these properties and provide a detailed characterization of the degree of business cycle synchronization among the countries under study as well as of the changing amplitudes of business cycles which are measured here as the appropriate frequency components of industrial production indices. In the examination we apply wavelet analysis of variance, wavelet correlation and cross-correlation examination as well as wavelet coherence and wavelet phase angle analysis in their global and (or) local (short-term) versions. The empirical examination points at an increasing synchronization of the Polish business cycle with the euro zone cycles as well as a fairly stable level of business cycle synchronization among the euro zone countries themselves.business cycle synchronization, euro zone, wavelet analysis, maximal overlap discrete wavelet transform, Hilbert wavelet pairs
Approximate wavelet-based simulation of long memory processes
In this article, we investigate an algorithm for the fast O(N) and approximate simulation of long memory (LM) processes of length N using the discrete wavelet transform. The algorithm generates stationary processes and is based on the notion that we can improve standard wavelet-based simulation schemes by noting that the decorrelation property of wavelet transforms is not perfect for certain LM process. The method involves the simulation of circular autoregressive process of order one. We demonstrate some of the statistical properties of the processes generated, with some focus on four commonly used LM processes. We compare this simulation method with the white noise wavelet simulation scheme of Percival and Walden [Percival, D. and Walden, A., 2000, Wavelet Methods for Time Series Analysis (Cambridge: Cambridge University Press).]
Some interesting facets of spectral analysis
Spectral analysis is used to explore the features of a stochastic process in the frequency domain typically via a Fourier transform. Spectral analysis is ideally suited to analyzing periodicities. It is also used for the analysis of dependence inherent in stationary or certain non-stationary processes. For many processes modeling spectra is simpler than modeling, for example, the (auto)
covariance function. One application of this is to the study of long memory processes. In this annotation, after giving some background, I provide some interesting developments of the spectral analysis of stochastic processes. Whenever possible I focus on the Bayesian developments. This is certainly not an exhaustive exploration of the literature
Simulating a class of stationary Gaussian processes using the Davies-Harte algorithm, with application to long memory processes
We demonstrate that the fast and exact Davies–Harte algorithm is valid for simulating a certain class of stationary Gaussian processes – those with a negative autocovariance sequence for all non-zero lags. The result applies to well known classes of long memory processes: Gaussian fractionally differenced (FD) processes, fractional Gaussian noise (fGn) and the nonstationary fractional Brownian Motion (fBm)
Going Beyond Counting First Authors in Author Co-citation Analysis
The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Global and local spectral-based tests for periodicities
We investigate tests for periodicity based on a spectral analysis of a time series, differentiating between global and local spectral-based tests. Global tests use information across the entire frequency band,whereas local tests are based on a window around the test frequency.We show that many spectral-based tests can be expressed in terms of a regression-based F test, which allows for approximate size and power calculations. Since global tests are usually derived assuming white noise errors, we extend to the correlated noise case. We demonstrate via a Monte Carlo study that although the global test may have better size and power, local tests are easier to use, and are comparable or better in terms of the power to detect periodicities, especially for spectra with a large dynamic range. We apply this methodology to a nonbehavioural test of hearing
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