scholarly journals Liquidity Forms and Bank Size

2016 ◽  
Vol 64 (6) ◽  
pp. 1999-2006 ◽  
Author(s):  
Jana Laštůvková
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Analysis ◽  
Liquidity Risk ◽  
Risk Measurement ◽  
Specific Method ◽  
Panel Regression ◽  
Series Analysis ◽  
Dependent Variables ◽  
Size Groups

The article deals with relationship between bank liquidity and variables representing the size of banks – such a total assets, gross volume of loans and clients deposits. For higher complexity, multiple dependent variables are used. The values are calculated based on a specific method of liquidity risk measurement – gross liquidity flows. To determine the possible relations the robust panel regression analysis together with the time series analysis are performed. The differences have been showed not just among different size groups but also among the same size groups in the different banking sectors.

scholarly journals Liquidity Determinants of the Selected Banking Sectors and their Size Groups

2016 ◽  
Vol 64 (3) ◽  
pp. 971-978 ◽  
Author(s):  
Jana Laštůvková
Keyword(s):  
Liquidity Risk ◽  
Risk Measurement ◽  
Specific Method ◽  
Panel Regression ◽  
Factors Affecting ◽  
Macroeconomic Variables ◽  
Dependent Variables ◽  
Size Groups ◽  
The Given ◽  
Higher Sensitivity

The article focuses on the factors affecting the liquidity of selected bank sectors, as well as their size groups, using panel regression analysis. For higher complexity of the results, multiple dependent variables are used: liquidity creation, outflow and net change. The values are calculated based on the specific method of liquidity risk measurement – gross liquidity flows. The results indicate both multiple effects of some factors on the given variables, as well as isolated influence of factors on a single liquidity form or size group. Thus, when looking for determinants using just one form of liquidity, such as creation, the results need not necessarily comprehensively show the influence of the given factors, and can lead to erroneous conclusions. The results also point to the differing behaviours of the size groups and their different sensitivity on the factors; smaller banks have shown higher sensitivity on macroeconomic variables. Higher flexibility in regulation could lead to higher optimization.

scholarly journals An Impact of Gas Prices on Transit Demand Using a Time-series Analysis and a Regression Analysis

2014 ◽  
Vol 32 (1) ◽  
pp. 13-26 ◽  
Author(s):  
Kwang Sub Lee ◽  
Jin Ki Eom ◽  
Dae Seop Moon ◽  
Keun Yul Yang ◽  
Jun Lee
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Analysis ◽  
Series Analysis

Time series analysis in forecasting pririrodno focal infections

2015 ◽  
Vol 9 (4) ◽  
pp. 0-0
Author(s):  
Евстегнеева ◽  
V. Evstegneeva ◽  
Честнова ◽  
Tatyana Chestnova ◽  
Смольянинова ◽  
...  
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Analysis ◽  
Bank Vole ◽  
Linear Trend ◽  
Random Variables ◽  
Vole Population ◽  
Long Term Effects ◽  
Series Analysis

Mathematical methods and models used in forecasting problems may relate to a wide variety of topics: from the regression analysis, time series analysis, formulation and evaluation of expert opinions, simulation, systems of simultaneous equations, discriminant analysis, logit and probit models, logical unit decision functions, variance or covariance analysis, rank correlation and contingency tables, etc. In the analysis of the phenomenon over a long timeperiod, for example, the incidence of long-term dynamics with a forecast of further development of the process, you should use the time series, which is influenced by the following factors: • Emerging trends of the series (the trend in cumulative long-term effects of many factors on the dynamics of the phenomenon under study - ascending or descending); • forming a series of cyclical fluctuations related to the seasonality of the disease; • random factors. In our study, we conducted a study to identify cyclical time series of long-term dynamics of morbidity of HFRS and autumn bank vole population. This study was performed using the autocorrelation coefficient. As a result of time-series studies of incidence of HFRS, indicators autumn bank vole population revealed no recurrence, and these figures are random variables, which is confirmed by three tests: nonrepeatability of time series, the assessment increase and decrease time-series analysis of the sum of squares. This shows that a number of indicators of the time series are random variables, contains a strong non-linear trend, to identify which need further analysis, for example by means of regression analysis.

scholarly journals Combined Analysis to Characterize Yield Pattern of Greenhouse-grown Red Sweet Peppers

HortScience ◽  
2009 ◽  
Vol 44 (2) ◽  
pp. 362-365 ◽  
Author(s):  
Wei-Chin Lin ◽  
Dietmar Frey ◽  
Gordon D. Nigh ◽  
Cheng C. Ying
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Regression Analysis ◽  
Time Series Analysis ◽  
Negative Influence ◽  
Positive Influence ◽  
Series Analysis ◽  
Growing Environment ◽  
Effect On Yield ◽  
Biological Nature

Understanding the irregular yield pattern of greenhouse-grown sweet peppers (Capsicum annuum L.) has been a challenge to researchers and greenhouse producers. Experimental data from 4 years, each consisting of 26 production weeks, were used in a time series analysis, neural network (NN) modeling, and regression analysis. Time series analysis revealed that weekly yield was influenced by yields from the preceding 2 weeks (Yd_1 and Yd_2), cumulative light 2 and 4 weeks prior (L_2 and L_4), and average 24-h air temperature 5 weeks prior (T_5). Cumulative light (L) data were transformed into kL by dividing by 1000 for subsequent NN modeling and regression analysis. These five inputs were used to establish a NN model, which illustrated the positive influence of Yd_1, kL_4, and kL_2 and negative influence of Yd_2 and T_5. Again, these five inputs were used in a regression analysis illustrating the positive influence of Yd_1 and the negative influence of Yd_2. Each input was further modified to include its squared value before entering the regression, which resulted in significant inputs of Yd_1, Yd_1 squared, and Yd_2 squared. Among these three analyses, the most consistent parameters were Yd_1 and Yd_2, confirming that the irregular yield pattern of greenhouse-grown peppers is of a biological nature. Environmental factors kL_2, kL_4, and T_5 did not show a consistent effect on yield in all three analyses, indicating yield pattern is less influenced by growing environment.

Dynamical Peculiarities of Concatenational Changes in Functional Cardiovascular Parameters

2018 ◽  
Vol 3 (82) ◽  
Author(s):  
Eurelija Venskaitytė ◽  
Jonas Poderys ◽  
Tadas Česnaitis
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Functional State ◽  
The Body ◽  
Functional Changes ◽  
High Intensity Training ◽  
Series Analysis ◽  
Greco Roman ◽  
St Segment ◽  
Standard Lead

Research  background  and  hypothesis.  Traditional  time  series  analysis  techniques,  which  are  also  used  for the analysis of cardiovascular signals, do not reveal the relationship between the  changes in the indices recorded associated with the multiscale and chaotic structure of the tested object, which allows establishing short-and long-term structural and functional changes.Research aim was to reveal the dynamical peculiarities of interactions of cardiovascular system indices while evaluating the functional state of track-and-field athletes and Greco-Roman wrestlers.Research methods. Twenty two subjects participated in the study, their average age of 23.5 ± 1.7 years. During the study standard 12 lead electrocardiograms (ECG) were recorded. The following ECG parameters were used in the study: duration of RR interval taken from the II standard lead, duration of QRS complex, duration of JT interval and amplitude of ST segment taken from the V standard lead.Research  results.  Significant  differences  were  found  between  inter-parametric  connections  of  ST  segment amplitude and JT interval duration at the pre and post-training testing. Observed changes at different hierarchical levels of the body systems revealed inadequate cardiac metabolic processes, leading to changes in the metabolic rate of the myocardium and reflected in the dynamics of all investigated interactions.Discussion and conclusions. It has been found that peculiarities of the interactions of ECG indices interactions show the exposure of the  functional changes in the body at the onset of the workload. The alterations of the functional state of the body and the signs of fatigue, after athletes performed two high intensity training sessions per day, can be assessed using the approach of the evaluation of interactions between functional variables. Therefore the evaluation of the interactions of physiological signals by using time series analysis methods is suitable for the observation of these processes and the functional state of the body.Keywords: electrocardiogram, time series, functional state.

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