scholarly journals Wage-Increment and Development of Black Economy in Nigeria: Influences of Inflation and Taxation

2020 ◽  
Vol 1 (2) ◽  
pp. 141-153
Author(s):  
Emmanuel Okon
Keyword(s):  
Standard Deviation ◽  
Minimum Wage ◽  
Impulse Response ◽  
Response Function ◽  
Value At Risk ◽  
Positive Impact ◽  
Impulse Response Function ◽  
Black Economy ◽  
Dynamic Relationship ◽  
Negative Effect

Purpose: In this paper, wage-black economy relationship was examined while taking into consideration the influences of inflation and taxation. Approach/Methodology/Design: In particular, Value at Risk (VAR) approach was implemented using annually data for Nigeria covering the period 1990-2018 to assess the dynamic relationship among the variables. In light of national minimum wage in Nigeria, impulse response function was used to highlight the plausible responses from black economy to a shock of one standard deviation in each indicated variable. Findings: The result shows that a shock to national minimum wage (LOGMWA) will have a negative effect on black economy (LOGBEC) in Nigeria.  Shocks to inflation (LOGINFL) will have a positive impact on black economy (LOGBEC). Shocks to tax (LOGATAX) will have asymmetric impacts on black economy (LOGBEC). The results were robust even when unemployment (LOGUEMP) was included. An unemployment shock was shown to enhance black economy in Nigeria. Practical Implications: The study is significant for the concerned authorities in Nigeria so that policy measures are taken and directed towards the causes that drive the black economy in the country. Originality/value: The impulse response function was estimated. It was estimated to show the plots of the responses from black economy (LOGBEC) to a one standard deviation shock in each indicated variable (national minimum wage (LOGMWA), inflation (LOGINFL), and tax (ATAX)). The shocks to national minimum wage (LOGMWA) will have a negative effect on black economy (LOGBEC) in Nigeria.

scholarly journals Damage Index Analysis of Retaining Wall Structures Based on the Impulse Response Function and Virtual Impulse Response Function

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Qian Xu
Keyword(s):  
Standard Deviation ◽  
Impulse Response ◽  
Response Function ◽  
Damage Identification ◽  
Feature Vector ◽  
Retaining Wall ◽  
Damage Index ◽  
Impulse Response Function ◽  
Damage State ◽  
Wall Structures

To identify the damage within retaining wall structures, the Hilbert–Huang Transforms of the impulse response function and virtual impulse response function were performed. The Hilbert marginal energy ratio spectrums of the impulse response function and virtual impulse response function were acquired. To reflect damage information effectively, those bands with stronger damage sensitivity were extracted via the threshold value ε0. Then, the Hilbert feature bands, which were more sensitive to damage within retaining walls, were selected by considering the contribution of the residual band to the damage identification. Based on the feature bands, the Hilbert damage feature vector, which reflects the variations of Hilbert marginal energy ratio caused by damage, was created. Based on the damage feature vector, two damage identification indexes (the energy ration standard deviation and Energy Ration Standard Deviation), which were based on the impulse response function and virtual impulse response function, respectively, were proposed to identify damage within retaining walls. To investigate the validity of the damage indexes, vibration tests on a pile plate retaining wall were done. The test results show that the damage feature vector is a zero vector or the value of damage index is zero when the wall is undamaged. The damage feature vector is a nonzero vector or the value of the damage index is more than zero when the wall is damaged. Thus, the damage state of the wall can be detected sensitively via the damage feature vector or damage indexes. Partial damage causes greater fluctuation of trend surface of the damage index. The location of partial damage can be diagnosed validly via the coordinate of peak value in the trend surface. The quantitative relationship formula between the damage index and damage intensity is established. The damage intensity of the wall can be calculated reversely, when the damage index is available. Either the energy ration standard deviation or Energy Ration Standard Deviation can be used to detect the damage state, diagnose the damage location, and identify the damage intensity. In comparison with the energy ration standard deviation, the stability and damage sensitivity of the Energy Ration Standard Deviation is much better.

Fractional Vector-Order h-Realisation of the Impulse Response Function

2020 ◽  
Vol 14 (2) ◽  
pp. 108-113
Author(s):  
Ewa Pawłuszewicz
Keyword(s):  
Control Systems ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Linear Control ◽  
Linear Control Systems

AbstractThe problem of realisation of linear control systems with the h–difference of Caputo-, Riemann–Liouville- and Grünwald–Letnikov-type fractional vector-order operators is studied. The problem of existing minimal realisation is discussed.

scholarly journals Convolution-based time-domain simulation for fluidelastic instability in tube arrays

2021 ◽  
Author(s):  
Mingjie Zhang ◽  
Ole Øiseth
Keyword(s):  
Impulse Response ◽  
Response Function ◽  
Time Domain ◽  
Impulse Response Function ◽  
Time Domain Simulation ◽  
Unit Step ◽  
Tube Arrays ◽  
Unit Impulse ◽  
The Time Domain ◽  
Unit Impulse Response

AbstractA convolution-based numerical algorithm is presented for the time-domain analysis of fluidelastic instability in tube arrays, emphasizing in detail some key numerical issues involved in the time-domain simulation. The unit-step and unit-impulse response functions, as two elementary building blocks for the time-domain analysis, are interpreted systematically. An amplitude-dependent unit-step or unit-impulse response function is introduced to capture the main features of the nonlinear fluidelastic (FE) forces. Connections of these elementary functions with conventional frequency-domain unsteady FE force coefficients are discussed to facilitate the identification of model parameters. Due to the lack of a reliable method to directly identify the unit-step or unit-impulse response function, the response function is indirectly identified based on the unsteady FE force coefficients. However, the transient feature captured by the indirectly identified response function may not be consistent with the physical fluid-memory effects. A recursive function is derived for FE force simulation to reduce the computational cost of the convolution operation. Numerical examples of two tube arrays, containing both a single flexible tube and multiple flexible tubes, are provided to validate the fidelity of the time-domain simulation. It is proven that the present time-domain simulation can achieve the same level of accuracy as the frequency-domain simulation based on the unsteady FE force coefficients. The convolution-based time-domain simulation can be used to more accurately evaluate the integrity of tube arrays by considering various nonlinear effects and non-uniform flow conditions. However, the indirectly identified unit-step or unit-impulse response function may fail to capture the underlying discontinuity in the stability curve due to the prespecified expression for fluid-memory effects.

IMPROVING EXTRACTION OF IMPULSE RESPONSE FUNCTIONS USING STATIONARY WAVELET SHRINKAGE IN TERAHERTZ REFLECTION IMAGING

2010 ◽  
Vol 09 (04) ◽  
pp. 387-394 ◽  
Author(s):  
YANG CHEN ◽  
YIWEN SUN ◽  
EMMA PICKWELL-MACPHERSON
Keyword(s):  
Experimental Data ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Terahertz Imaging ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Inverse Filtering ◽  
Wavelet Shrinkage ◽  
Gaussian Filtering

In terahertz imaging, deconvolution is often performed to extract the impulse response function of the sample of interest. The inverse filtering process amplifies the noise and in this paper we investigate how we can suppress the noise without over-smoothing and losing useful information. We propose a robust deconvolution process utilizing stationary wavelet shrinkage theory which shows significant improvement over other popular methods such as double Gaussian filtering. We demonstrate the success of our approach on experimental data of water and isopropanol.

Interest Rate–Economic Growth Nexus Under Currency Board Operations

2021 ◽  
Vol 8 (1) ◽  
pp. 13-24
Author(s):  
Martinianus Tshimologo Tibinyane ◽  
Teresia Kaulihowa
Keyword(s):  
Economic Growth ◽  
Interest Rate ◽  
Impulse Response ◽  
Response Function ◽  
Open Economy ◽  
Small Open Economy ◽  
Impulse Response Function ◽  
Policy Instrument ◽  
Variance Decomposition ◽  
Currency Board

This paper analyses the effect of the prime interest rate as a monetary policy instrument to stimulate economic growth in Namibia, a small open economy that is constrained by currency board operations. A Vector Autoregressive Model (VAR) was used for the period 1980–2019. The result shows that Namibia’s prime interest rate has no significant effect on economic growth. This finding remains robust and consistent when impulse response function and variance decomposition are employed. The impulse response function indicates a shock on the prime interest rate exhibits an inverse relationship. However, this effect is insignificant in both short and long-run scenarios. The variance decomposition indicates that the prime interest rate has a strongly exogenous impact, implying it has a weak influence on GDP growth. Policy implication indicates that small open economies under currency board operations need to identify different policy responses to circumvent external shocks and addresses their development needs.

Image quality characterization of fine resolution RISAT-1 data using impulse response function

2018 ◽  
Vol 34 (6) ◽  
pp. 586-596
Author(s):  
Gautam Dadhich ◽  
Shweta Sharma ◽  
Mihir Rambhia ◽  
Aloke K. Mathur ◽  
P. R. Patel ◽  
...  
Keyword(s):  
Image Quality ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Fine Resolution
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