Harmonic analysis, time series variations and the distributional properties of financial ratios

Omega ◽  
1995 ◽  
Vol 23 (4) ◽  
pp. 419-427 ◽  
Author(s):  
N. Fuller-Love ◽  
H. Rhys ◽  
M. Tippett
1994 ◽  
Vol 25 (2) ◽  
pp. 65-71
Author(s):  
A. C. Jordaan ◽  
E. V.D.M. Smit ◽  
W. D. Hamman

In this article we examine some of the inter-temporal and cross-sectional distributional properties of a selected number of financial ratios of South African industrial companies and we evaluate the effect of a simple procedure of outlier rejection. The normality assumption is rejected consistently in the case of the industry analysis and frequently in the sectoral and yearly analyses.


Author(s):  
Jean-Frédéric Morin ◽  
Christian Olsson ◽  
Ece Özlem Atikcan

This chapter focuses on time series analysis, a statistical method of longitudinal analysis which is suitable if researchers are interested in the temporality of social phenomena and want to analyse social change and patterns of recurrence over time. In contrast to other statistical methods of longitudinal analysis, time series analysis can be applied even if researchers have only a few cases (maybe even only one) and only a few (maybe even only one) variables. Time series can be built for any level of analysis, as cases can be persons, but are usually organizations or countries. In order to build a time series, the variables need to have been measured several times over a given period, and for each measurement one needs to know the measurement date. There are different goals when doing time series analysis, which can be used in descriptive, explanatory, and interpretive approaches.


2007 ◽  
Vol 41 (20) ◽  
pp. 7030-7038 ◽  
Author(s):  
Shabnam Dilmaghani ◽  
Isaac C. Henry ◽  
Puripus Soonthornnonda ◽  
Erik R. Christensen ◽  
Ronald C. Henry

2013 ◽  
Vol 30 (3) ◽  
pp. 569-589 ◽  
Author(s):  
Pascal Matte ◽  
David A. Jay ◽  
Edward D. Zaron

Abstract One of the most challenging areas in tidal analysis is the study of nonstationary signals with a tidal component, as they confront both current analysis methods and dynamical understanding. A new analysis tool has been developed, NS_TIDE, adapted to the study of nonstationary signals, in this case, river tides. It builds the nonstationary forcing directly into the tidal basis functions. It is implemented by modification of T_TIDE; however, certain concepts, particularly the meaning of a constituent and the Rayleigh criterion, are redefined to account for the smearing effects on the tidal spectral lines by nontidal energy. An error estimation procedure is included that constructs a covariance matrix of the regression coefficients, based on either an uncorrelated or a correlated noise model. The output of NS_TIDE consists of time series of subtidal water levels [mean water level (MWL)] and tidal properties (amplitudes and phases), expressed in terms of external forcing functions. The method was tested using records from a station on the Columbia River, 172 km from the ocean entrance, where the tides are strongly altered by river flow. NS_TIDE hindcast explains 96.4% of the signal variance with a root-mean-square error of 0.165 m obtained from 288 parameters, far better than traditional harmonic analysis (38.5%, 0.604 m, and 127 parameters). While keeping the benefits of harmonic analysis, its advantages compared to existing tidal analysis methods include its capacity to distinguish frequencies within tidal bands without losing resolution in the time domain or data at the endpoints of the time series.


2014 ◽  
Vol 88 (10) ◽  
pp. 975-988 ◽  
Author(s):  
A. R. Amiri-Simkooei ◽  
S. Zaminpardaz ◽  
M. A. Sharifi

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