scholarly journals Estimation of Informal Sector of the Nigerian Construction Industry

2020 ◽  
pp. 203-208
Author(s):  
Ojo O. J. ◽  
Yusuf B. A. ◽  
Aremu J. A.
Keyword(s):  
Construction Industry ◽  
Informal Sector ◽  
Instrumental Variable ◽  
Value Added ◽  
Least Square Method ◽  
Cement Industry ◽  
Least Square ◽  
Pool Data ◽  
Time Series Regression ◽  
Data Set

The study estimated the output of informal sector of the Nigerian Cement Industry through the consumption of cement by the Nigerian Construction industry. The research was conducted using secondary data. The study adopted the statistical model of informal sector estimation, Nigeria construction industry being cement intensive, cement consumption approach was used for the estimation of the informal sector of the industry by using annual cement consumption as an independent variable against the annual construction output in a time series regression analysis, treating the informal sector output as an omitted variable in the ordinary least square method of estimation. Annual value added tax (VAT) pool data set was chosen as an instrumental variable. Using the instrumental variable method of estimation, the informal sector proportion of the Nigeria construction industry was therefore estimated. The study concluded that the informal sector of the Nigerian construction industry is 4.07 percent of the industry's output.

scholarly journals Data approximation using Lotka-Volterra models and a software minimization function

2019 ◽  
Vol 15 (2) ◽  
pp. 5-14
Author(s):  
M. Fečkan ◽  
J. Pačuta
Keyword(s):  
Mathematical Models ◽  
Historical Data ◽  
Least Square Method ◽  
Least Square ◽  
Data Set ◽  
Data Approximation ◽  
Standard Methods ◽  
Volterra Models ◽  
Coefficient Estimation

Abstract In recent years, a lot of effort has been put into finding suitable mathematical models that fit historical data set. Such models often include coefficients and the accuracy of data approximation depends on them. So the goal is to choose the unknown coefficients to achieve the best possible approximation of data by the corresponding solution of the model. One of the standard methods for coefficient estimation is the least square method. This can provide us data approximation but it can also serve as a starting method for further minimizations such as Matlab function fminsearch.

scholarly journals MIGRASI ORANG TUA DAN KOGNITIF ANAK: ANALISIS DATA IFLS 2007 DAN 2014

2021 ◽  
Vol 15 (2) ◽  
pp. 145
Author(s):  
Lutvianti Zahra
Keyword(s):  
Cognitive Ability ◽  
Instrumental Variable ◽  
Least Square Method ◽  
Least Square ◽  
Education Expenditure ◽  
Parental Migration ◽  
Capital Development ◽  
Child Human Capital ◽  
Economic And Social Impacts ◽  
Migrant Parents

Study about migration commonly focuses on the migrants themselves. However, some studies showed that migration also has economic and social impacts on their families, including their children. Cognitive ability is one of the fundamental aspects of child human capital development. This research aims to study the effect of parental migration on children's cognitive ability. This study employs longitudinal data from the Indonesian Family Life Survey (IFLS) in 2007 and 2014 analysed using the Pooled Least Square method and Instrumental Variable Two-Step Least Square (IV 2SLS). Descriptive results show a decline in the average cognitive score of children aged 14-25 years. Moreover, there were no significant differences in cognitive scores between children of migrant parents and non-migrant parents. Inferential results also found that parental migration did not significantly affect children's cognitive ability. Children's cognition is influenced by other characteristics such as age, sex, years of schooling, mother's education, per capita education expenditure, and area of residence.

scholarly journals Unboxing the black box on the dimensions of social globalisation and the efficiency of microfinance institutions in Asia

2021 ◽  
Vol 12 (3) ◽  
pp. 557-592
Author(s):  
Hafezali Iqbal Hussain ◽  
Katarzyna Szczepańska-Woszczyna ◽  
Fakarudin Kamarudin ◽  
Nazratul Aina Mohamad Anwar ◽  
Mohd Haizam Mohd Saudi
Keyword(s):  
Value Added ◽  
Least Square ◽  
Data Envelopment ◽  
Microfinance Institutions ◽  
Social Efficiency ◽  
Data Set ◽  
Positive Correlation ◽  
The Social ◽  
Potential Factors ◽  
Scientific Practical

Research background: Microfinance institutions (MFIs) play an important role in alleviating poverty. Thus, MFIs should be efficient in order to ensure that their objectives on social welfare and financial performance can be achieved by identifying the potential determinants, specifically on social globalisation. Purpose of the article: This paper examines the impacts of the social globalisation dimensions of interpersonal, informational, and cultural globalisations on the financial and social efficiency of MFIs. Methods: The data period covered the years 2011?2018; the data set consists of 176 MFIs from six Asian countries. The Data Envelopment Analysis (DEA) approach was employed to examine the MFIs? efficiency levels. Generalised Least Square (GLS) regressions were used to analyse the impacts of social globalisation and other determinants towards the efficiency of MFIs. Findings and value added: Interpersonal globalisation had a significantly negative correlation with social efficiency, suggesting that increasing the number of foreigners in management intrudes on local managers? decisions. Informational globalisation had a significantly positive correlation with financial and social efficiency, which signifies that more information produces monopolistic profits in this industry. Finally, cultural globalisation had a positive correlation with social efficiency, demonstrating that a global trading culture improves the abilities and technological skills for labour development and enhances MFIs? social efficiency. In general, the Cobb Douglas Production theory explained the understanding of the impacts social globalisation has on MFI efficiency. Furthermore, the findings from this study could provide important scientific, practical gap and contribute new insights and implications to various parties. Firstly, governments or policymakers can establish effective national policies and strategies. Secondly, this study could support investors in monitoring and understanding the performance of MFIs. Finally, the research could fill scholarly gaps and uncover more potential factors that influence the efficiency of MFIs.

scholarly journals Local magnitude scale in Slovenia

2013 ◽  
Vol 34 ◽  
pp. 23-28 ◽  
Author(s):  
J. Bajc ◽  
Ž. Zaplotnik ◽  
M. Živčić ◽  
M. Čarman
Keyword(s):  
Vertical Component ◽  
Large Data ◽  
Least Square Method ◽  
Least Square ◽  
Magnitude Scale ◽  
Local Magnitude ◽  
Data Set ◽  
Seismic Stations ◽  
Short Period ◽  
Environment Agency

Abstract. In the paper a calibration study of the local magnitude scale in Slovenia is presented. The Seismology and Geology Office of the Slovenian Environment Agency routinely reports the magnitudes MLV of the earthquakes recorded by the Slovenian seismic stations. The magnitudes are computed from the maximum vertical component of the ground velocity with the magnitude equation that was derived some thirty years ago by regression analysis of the magnitudes recorded by a Wood-Anderson seismograph in Trieste and a short period seismograph in Ljubljana. In the study the present single magnitude MLV equation is replaced by a general form of the Richter local magnitude MWA equation. The attenuation function and station-component corrections that compensate the local effects near seismic stations are determined from the synthetic Wood-Anderson seismograms of a large data set by iterative least-square method. The data set used consists of approximately 18 000 earthquakes during a period of 14 yr, each digitally recorded on up to 29 stations. The derived magnitude equation is used to make the final comparison between the new MWA magnitudes and the routinely calculated MLV magnitudes. The results show good overall accordance between both magnitude equations. The main advantage of the introduction of station-component corrections is the reduced uncertainty of the local magnitude that is assigned to a certain earthquake.

scholarly journals Least Squares Consensus for Matching Local Features

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 275
Author(s):  
Qingming Zhang ◽  
Buhai Shi ◽  
Haibo Xu
Keyword(s):  
Least Squares ◽  
Order Statistics ◽  
Random Sampling ◽  
Sampling Methods ◽  
Least Square Method ◽  
Least Square ◽  
Nearest Neighbour ◽  
Data Set ◽  
Alternative Scheme ◽  
New Approach

This paper presents a new approach to estimate the consensus in a data set. Under the framework of RANSAC, the perturbation on data has not been considered sufficiently. We analysis the computation of homography in RANSAC and find that the variance of its estimation monotonically decreases when the size of sample increases. From this result, we carry out an approach which can suppress the perturbation and estimate the consensus set simultaneously. Different from other consensus estimators based on random sampling methods, our approach builds on the least square method and the order statistics and therefore is an alternative scheme for consensus estimation. Combined with the nearest neighbour-based method, our approach reaches higher matching precision than the plain RANSAC and MSAC, which is shown in our simulations.

scholarly journals An optical model for deriving the spectral particulate backscattering coefficients in oceanic waters

Ocean Science ◽  
2013 ◽  
Vol 9 (6) ◽  
pp. 987-1001 ◽  
Author(s):  
S. P. Tiwari ◽  
P. Shanmugam
Keyword(s):  
Optical Model ◽  
Trust Region ◽  
Simulated Data ◽  
Correlation Coefficients ◽  
Least Square Method ◽  
Least Square ◽  
Data Set ◽  
New Model ◽  
Trust Region Algorithm ◽  
In Situ Data

Abstract. An optical model is developed based on the diffuse attenuation coefficient (Kd) to estimate particulate backscattering coefficients bbp(λ) in oceanic waters. A large in situ data set is used to establish robust relationships between bbp(530) and bbp(555) and Kd(490) using an efficient nonlinear least-square method which uses the trust region algorithm with Bisquare weights scheme to adjust the coefficients. These relationships are obtained with good correlation coefficients (R2 = 0.786 and 0.790), low root mean square error (RMSE = 0.00076 and 0.00072) and 95% confidence bounds. The new model is tested with three independent data sets: the NOMAD SeaWiFS Match ups, OOXIX IOP algorithm workshop evaluation data set (Version 2.0w APLHA), and IOCCG simulated data set. Results show that the new model makes good retrievals of bbp at all key wavelengths (from 412–683 nm), with statistically significant improvements over other inversion models. Thus, the new model has the potential to improve our present knowledge of particulate matter and their optical variability in oceanic waters.

scholarly journals Correlation effect of transformed or corrected data inversion

2021 ◽  
Author(s):  
László Balázs
Keyword(s):  
Likelihood Function ◽  
Measurement Data ◽  
Least Square Method ◽  
Estimation Procedure ◽  
Correlation Effect ◽  
Least Square ◽  
Inversion Method ◽  
Data Set ◽  
Data Inversion ◽  
Fitting Procedure

AbstractBefore performing the inversion process, the original measured data set is often transformed (corrected, smoothed, Fourier-transformed, interpolated etc.). These preliminary transformations may make the original (statistically independent) noisy measurement data correlated. The noise correlation on transformed data must be taken into account in the parameter fitting procedure (inversion) by proper derivation of likelihood function. The covariance matrix of transformed data system is no longer diagonal, so the likelihood based metrics, which determines the fitting process is also changed as well as the results of inversion. In the practice, these changes are often neglected using the “customary” estimation procedure (simple least square method) resulting wrong uncertainty estimation and sometimes biased results. In this article the consequence of neglected correlation is studied and discussed by decomposing the inversion functional to “customary” and additional part which represents the effect of correlation. The ratio of two components demonstrates the importance and justification of the inversion method modification.

scholarly journals Response of Nigerian Construction Industry to Economic Growth

2020 ◽  
pp. 506-512
Author(s):  
Ojo O. J. ◽  
Yusuf B. A. ◽  
Anjonrin-Ohu A.
Keyword(s):  
Economic Growth ◽  
Construction Industry ◽  
Correlation Coefficient ◽  
Secondary Data ◽  
Least Square ◽  
National Account ◽  
Time Series Regression ◽  
Distributed Lag ◽  
Long Run ◽  
The Impact

The study assessed response of Nigerian Construction Industry to economic growth of Nigeria. The research was conducted using secondary data. The secondary data used was the National Account Dataset from 1981 to 2018 as 2010 constant price year. This was gotten from the Central Bank of Nigeria (CBN) publication reports. The response was evaluated through Impact propensity (IP), Finite Distributed Lag (FDL) and the Long Run Propensity (LRP). These parameters were calculated from the time series regression analysis using ordinary Least Square Method of estimation. The results show that the impact propensity of economic growth on construction is weak with correlation coefficient of -0.012. Delayed impact of economic growth on construction was observed with finite distributed lag of two year cycle. Maximum correlation coefficient of 1.265 with the economics of the preceding year (t-1) was observed. Long run propensity of 1.333 establishes a high growth propensity for construction industry given a one percent permanent GDP growth. Therefore, the study concluded that a consistent economic growth is desirable so as to achieve improved construction industry contribution to GDP.

scholarly journals Global Value Chain Participation and Current Account Balances in Landlocked African Countries

2020 ◽  
pp. 1-14
Author(s):  
Kenfack Geraud Francis ◽  
Ningaye Paul ◽  
Kuipou Toukam Christophe
Keyword(s):  
Current Account ◽  
Value Chain ◽  
Trade Openness ◽  
Value Added ◽  
Least Square ◽  
Current Account Balance ◽  
Global Value Chain ◽  
African Countries ◽  
Data Set ◽  
Development Indicator

The objective of this paper is to find out the direction of Global Value Chain Participation (GVCP) that contribute more to the Current Account Balance (CAB) in landlocked African countries from 2000 to 2018. Our specification follows the IMF's External Balance Assessment (EBA) model. The Feasible Generalized Least Square (FGLS) econometric technique is applied on data from three sources: (1) UNCTAD-EORA database for forward and backward participation indicators, (2) World Development Indicator (WDI) data set, for current account balance, foreign direct investment (FDI), population and trade openness and (3) Penn World Tables (PWT) for exchange rates. Results highlight a positive and significant contribution of forward GVCP on CAB in landlocked African countries. The study recommends that landlocked African countries should be active providers of value-added intermediary inputs to other Global Value Chain actors.

What about the relevance of the diffusion parameters identified in the case of incomplete Fickian and non-Fickian kinetics?

2018 ◽  
Vol 53 (11) ◽  
pp. 1555-1565
Author(s):  
Julie Cocaud ◽  
Amandine Célino ◽  
Sylvain Fréour ◽  
Frédéric Jacquemin
Keyword(s):  
Reference Values ◽  
Model Identification ◽  
Water Concentration ◽  
Least Square Method ◽  
Least Square ◽  
Data Sets ◽  
Data Set ◽  
Diffusion Parameters ◽  
Alternative Approach ◽  
Truncation Threshold

The aim of this work is to study the relevance of the diffusion parameters identified on kinetics whose saturation levels are unknown. Two types of diffusion kinetics have been considered: a Fickian and a non-Fickian kinetics. Numerical experiments, based on the generation of noisy data corresponding to reference values of diffusion parameters, have been carried out. The two models used in this study are Fick's model and the «Dual-Fick» model. Identification procedures were then applied on these data sets, truncated at different critical times. In the specific case of Dual Fick kinetics, the classical least square method was compared to an alternative approach based on the piecewise analysis of the shape of the diffusion curves. The identified parameters may deviate significantly from the expected reference values when the truncation threshold of the data set precedes the steady state. Also, these inaccurate parameters impact the predictions of global water uptake, water concentration profiles and stresses gradients through the thickness of the sample.

Sign in / Sign up

Export Citation Format

Share Document