scholarly journals USING WAVELETS IN ECONOMICS. AN APPLICATION ON THE ANALYSIS OF WAGE-PRICE RELATION

2017 ◽  
Vol 2 (1) ◽  
pp. 32-42
Author(s):  
Vasile-Aurel Caus ◽  
Daniel Badulescu ◽  
Mircea Cristian Gherman
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transforms ◽  
Wavelet Decomposition ◽  
Fourier Transforms ◽  
Nominal Wage ◽  
Economic Issues ◽  
Wavelet Methods ◽  
Theoretical Considerations ◽  
Analysis Of Time Series

In the last decades, more and more approaches of economic issues have used mathematical tools, and among the most recent ones, spectral and wavelet methods are to be distinguished. If in the case of spectral analysis the approaches and results are sufficiently clear, while the use of wavelet decomposition, especially in the analysis of time series, is not fully valorized. The purpose of this paper is to emphasize how these methods are useful for time series analysis. After theoretical considerations on Fourier transforms versus wavelet transforms, we have presented the methodology we have used and the results obtained by using wavelets in the analysis of wage-price relation, based on some empirical data. The data we have used is concerning the Romanian economy - the inflation and the average nominal wage denominated in US dollars, between January 1991 and May 2016, highlighting that the relation between nominal salary and prices can be revealed more accurately by use of wavelets

Spectral analysis of time series of categorical variables in earth sciences

2016 ◽  
Vol 95 ◽  
pp. 99-104 ◽  
Author(s):  
Eulogio Pardo-Igúzquiza ◽  
Francisco J. Rodríguez-Tovar ◽  
Javier Dorador
Keyword(s):  
Earth Sciences ◽  
Time Series ◽  
Spectral Analysis ◽  
Categorical Variables ◽  
Analysis Of Time Series

Time-Local Spectral Analysis for Non-Stationary Time Series: The S-Transform for Noisy Signals

2003 ◽  
Vol 03 (03) ◽  
pp. L357-L364 ◽  
Author(s):  
C. R. Pinnegar ◽  
L. Mansinha
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transforms ◽  
Gaussian White Noise ◽  
Stationary Time Series ◽  
Time Frequency ◽  
S Transform ◽  
Undesirable Characteristic ◽  
Noisy Signals ◽  
Short Time

The S-transform is a method of time-local spectral analysis (also known as time-frequency analysis), a modified short-time Fourier Transform, in which the width of the analyzing window scales inversely with frequency, in analogy with continuous wavelet transforms. If the time series is non-stationary and consists of a mix of Gaussian white noise and a deterministic signal, though, this type of scaling leads to larger apparent noise amplitudes at higher frequencies. In this paper, we introduce a modified S-transform window with a different scaling function that addresses this undesirable characteristic.

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