An econometric analysis of electricity consumption and real sector performance in Nigeria

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Isaac Chitedze ◽  
Chukwuemeka Cosmas Nwedeh Nwedeh ◽  
Adenikinju Adeola ◽  
Donald Chidera Chidera Abonyi
Keyword(s):  
Time Series ◽  
Power Supply ◽  
Manufacturing Sector ◽  
Electricity Consumption ◽  
Policy Implications ◽  
Series Data ◽  
Real Sector ◽  
Content Type ◽  
The Real ◽  
Sector Performance

Purpose The purpose of this paper is to examine the extent at which electricity consumption (EC) has contributed to real sector performance, to identify energy-dependent sectors of the economy for appropriate sector-specific policy interventions and to avoid energy conservation policies that may retard the growth of the real sector and economic growth in general. Design/methodology/approach This paper used time series data, covering the period between 1981 and 2015. Various time series econometric analyses such as unit root test for stationarity and vector autoregressive and vector error correction models were used to establish the long-run and short-run co-integration relationship among the variables. Findings This study finds that EC displays a little and insignificant impact on manufacturing sector output, as well as agriculture and service outputs. The empirical result from causality test suggests a unidirectional causality running from agriculture to EC, as well as service sector to EC, whereas bidirectional causality runs between EC and manufacturing sector. This study therefore recommends adequate power supply to the manufacturing sector, while energy efficiency policy and regulatory reform should address agriculture and service sectors. Originality/value Few studies have examined the impact of EC on disaggregated gross domestic product. This research gap has strong policy implications on Nigerian economy as the output of real sector plays vital role in driving the economy. Given the pressing needs for Nigeria to boost real sector output and be among the world’s 20 largest economies by 2030, it becomes imperative for this sector-specific research to be conducted to ensure that sectoral purpose-driven energy interventions are formulated to address power supply challenges in the real sector.

Impact of the banking sector reform in the construction sector

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Najimu Saka ◽  
Ayokunle Olubunmi Olanipekun
Keyword(s):  
Time Series ◽  
Developing Country ◽  
Banking Sector ◽  
Positive Impact ◽  
Series Data ◽  
Construction Sector ◽  
Sector Reform ◽  
Content Type ◽  
Sector Performance ◽  
The Impact

Purpose Banking sector reforms can impact the development of the real sector. However, there is very little known about this impact on the construction sector in a developing country context. This study aims to evaluate the impact of the banking sector reform on the construction output (CNS) using the banking sector reform in Nigeria in 2005 (2005 Banking Sector Reform Programme [BSRP]) as a case. Design/methodology/approach This study used econometric methodology comprising unit root test for stationarity, Johansen test for cointegration, analysis of variance (ANOVA) and the analysis of covariance. Time series data covering a period from 1981 to 2017 (37 years) about the banking and construction sector performances are analyzed using ten-time series equations. Findings The ANOVA estimates reveal that the 2005 BSRP positively impacted the CNS and construction sector growth rate. However, the ANOVA estimates reveal that the gross domestic product (GDP) and bank total loan had a positive impact on CNS in the period (1981–2017) before and after the 2005 BSRP, and consequently removing the effect of the 2005 BSRP on CNS. Practical implications This paper concludes that the banking sector reform has a positive impact on CNS in the Nigerian construction industry. The impact is greater and lasting when the reform is directly targeted at improving CNS. Originality/value This study provides empirical evidence of the dependence between banking sector reform and construction sector performance in a developing country context. Also, this study demonstrates the relationship between GDP, banking sector reform and construction sector performance in a developing country context.

The carbon dioxide emissions effect of income growth, electricity consumption and electricity power crisis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paul Adjei Kwakwa
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Growth And Development ◽  
Electricity Consumption ◽  
Policy Implications ◽  
Income Growth ◽  
Series Data ◽  
Ekc Hypothesis ◽  
Content Type ◽  
Electricity Power

PurposeAttaining higher economic growth and development is among the topmost agenda for many countries. However, the process to attain such growth and development involves higher level of energy consumption and that may not spare the quality of the environment. A similar concern has been raised for Ghana as it aims to attain an upper middle-income status in the near future. The country's energy sector has however not been robust in meeting the electricity demand, leading to a recurrent power crisis. The study seeks to analyze the effect of income growth, electricity consumption and power crisis on Ghana's carbon dioxide (CO2) emissions.Design/methodology/approachThe paper relies on annual time series data from the World Bank (2020) and employs the autoregressive distributed lag (ARDL) and fully modified ordinary least square (FMOLS) estimation techniques for regression analysis.FindingsThe results showed that the environmental Kuznets curve (EKC) hypothesis is valid for Ghana in the case of carbon emissions. Also, while electricity consumption has an insignificant effect on carbon emissions, electricity power crisis exerts a positive effect on emission of CO2. It was also noted that industrialization and financial development increase CO2 emissions.Research limitations/implicationsPolicy implications from the study include the EKC hypothesis can be a sound basis for environmental policy in Ghana. Other recommendations and areas for future research have been provided.Originality/valueThe study empirically estimates the effect of electricity crisis on CO2 emissions.

Exploring the dynamic contribution behavior of editors in wikis based on time series analysis

2016 ◽  
Vol 50 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Linghe Huang ◽  
Qinghua Zhu ◽  
Jia Tina Du ◽  
Baozhen Lee
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Time Of Day ◽  
Observation Time ◽  
Series Data ◽  
Clustering Methods ◽  
Content Type ◽  
Free Rider Problem ◽  
Series Analysis

Purpose – Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in wikis, “free rider problem” has been serious. In order to motivate editors to contribute more to a wiki system, it is important to fully understand their contribution behavior. The purpose of this paper is to explore the law of dynamic contribution behavior of editors in wikis. Design/methodology/approach – After developing a dynamic model of contribution behavior, the authors employed both the metrological and clustering methods to process the time series data. The experimental data were collected from Baidu Baike, a renowned Chinese wiki system similar to Wikipedia. Findings – There are four categories of editors: “testers,” “dropouts,” “delayers” and “stickers.” Testers, who contribute the least content and stop contributing rapidly after editing a few articles. After editing a large amount of content, dropouts stop contributing completely. Delayers are the editors who do not stop contributing during the observation time, but they may stop contributing in the near future. Stickers, who keep contributing and edit the most content, are the core editors. In addition, there are significant time-of-day and holiday effects on the number of editors’ contributions. Originality/value – By using the method of time series analysis, some new characteristics of editors and editor types were found. Compared with the former studies, this research also had a larger sample. Therefore, the results are more scientific and representative and can help managers to better optimize the wiki systems and formulate incentive strategies for editors.

Commercial Banks Credit and the Performance of Real Sector in Nigeria: 1990-2017

2020 ◽  
pp. 1-15
Keyword(s):  
Central Bank ◽  
Commercial Banks ◽  
Agricultural Sector ◽  
Manufacturing Sector ◽  
Bank Lending ◽  
Real Sector ◽  
Agricultural Credit ◽  
The Real ◽  
Scientific Discussion ◽  
The Cost

The study examined the arguments and counterarguments within the scientific discussion on commercial banks credit and the performance of real sector in Nigeria. The main objective of the study is to examine the effect of commercial banks credit on the performance of the real sector in Nigeria.Data was sourced from Central Bank of Nigeria Statistical Bulletin. A systematization literary approach for data analysis was Regression Analysis. Findings revealed that bank credit and bank lending rate does not have significant impact on real sector performance in Nigeria. It was showed that there was a positive and significant relationship between agricultural credit guarantee scheme fund and agricultural production in Nigeria. The study therefore recommends that banks should be directed to channel their credits towards the real sector to facilitate overall economic growth and development in Nigeria. It was recommended that there is the need policies that will favor the revamp of the agricultural sector in Nigeria should be given pride of place. Also, monetary authority through the Central Bank of Nigeria should create adequate policies and strategies towards deepening of the financial sector and reducing the cost of credit/loans so as to enhance productivity and consequently enhance the growth of the key sectors of economy such as manufacturing sector.

Dynamic interactions between stock markets and the real economy

2016 ◽  
Vol 11 (4) ◽  
pp. 715-746 ◽  
Author(s):  
Nikiforos T. Laopodis ◽  
Andreas Papastamou
Keyword(s):  
Economic Development ◽  
Stock Market ◽  
Methodological Approach ◽  
Financial System ◽  
Cointegration Analysis ◽  
Policy Implications ◽  
Real Economy ◽  
Dynamic Interactions ◽  
Content Type ◽  
The Real

Purpose The purpose of this paper is to re-examine the relationship between a country’s aggregate stock market and general economic development for 14 emerging economies for the period from 1995 to 2014. Design/methodology/approach The methodological approach of the paper is multifold. First, the authors use cointegration analysis to determine the simple dynamics among the variables. Second, the authors utilize vector autoregression analysis to study the dynamics among the variables for the 14 countries. Third, the authors employ panel analysis to determine common variations among the variables and across countries. Findings When examining the linkage between the stock market and economic development, proxied by gross domestic product growth or with gross fixed capital formation growth, the authors did not find a meaningful relationship between them. However, when the authors included additional control variables strong, dynamic interactions between the two magnitudes surfaced. Specifically, it was found that the stock market is positively and robustly correlated with contemporaneous and future real economic development and, thus, it directly contributed to a country’s economic development either through the production of goods and services or the accumulation of real capital. Thus, it can be inferred that the stock market alone is not capable of boosting economic development in these countries unless being part of a comprehensive financial system (which includes banks) as well as investment in real capital. Research limitations/implications The policy implications are clear. Government authorities must recognize that the stock market alone is not a driver of economic development and that a sound, efficient financial system (which includes banks) must be present in order to contribute and foster economic development. Originality/value The study is original in the sense that it examines various financial and economic variables to determine the degree of (or dynamic interactions among) the stock market and the real economy for each and all emerging markets in the sample.

An improved morphological weighted dynamic similarity measurement algorithm for time series data

2018 ◽  
Vol 11 (4) ◽  
pp. 486-495
Author(s):  
Ke Yi Zhou ◽  
Shaolin Hu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Morphological Changes ◽  
Series Data ◽  
Similarity Measurement ◽  
Improved Method ◽  
Control Measurement ◽  
Content Type ◽  
Basic Work ◽  
Measurement Algorithm

Purpose The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and many other data mining problems. The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm. The subsequence morphological information is taken into account by the proposed algorithm, and time series is represented by a pattern, so the similarity measurement algorithm is more accurate. Design/methodology/approach Following some previous researches on similarity measurement, an improved method is presented. This new method combines morphological representation and dynamic time warping (DTW) technique to measure the similarities of time series. After the segmentation of time series data into segments, three parameter values of median, point number and slope are introduced into the improved distance measurement formula. The effectiveness of the morphological weighted DTW algorithm (MW-DTW) is demonstrated by the example of momentum wheel data of an aircraft attitude control system. Findings The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data. Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement. Practical implications This improved method has been used to solve the problem of similarity measurement in time series, which is widely emerged in different fields of science and engineering, such as the field of control, measurement, monitoring, process signal processing and economic analysis. Originality/value In the similarity measurement of time series, the distance between sequences is often used as the only detection index. The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence, so it is necessary to incorporate the morphological changes of the sequence into similarity measurement. The MW-DTW is more suitable for the actual situation. At the same time, the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

Impact assessment of political administrations on the performance of the construction sector: a time series analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Najimu Saka ◽  
Abdullahi Babatunde Saka ◽  
Opeoluwa Akinradewo ◽  
Clinton O. Aigbavboa
Keyword(s):  
Time Series ◽  
Growth And Development ◽  
Time Series Data ◽  
Critical Factor ◽  
Sustainable Growth ◽  
Analysis Of Covariance ◽  
Series Data ◽  
Construction Sector ◽  
Content Type ◽  
Ancova Model

Purpose The complex interaction of politics and the economy is a critical factor for the sustainable growth and development of the construction sector (CNS). This study aims to investigate the effects of type of political administration including democracy and military on the performance of CNS using the Nigerian Construction Sector (NCS) as a case study. Design/methodology/approach A 48 year (1970–2017) time series data (TSD) on the NCS and the gross domestic product (GDP) based on 2010 constant USD were extracted from the United Nations Statistical Department database. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models were used to analyze the TSD. The ANCOVA model includes the GDP as correlational variable or covariate. Findings The estimates of the ANOVA model indicate that democratic administration is significantly better than military administration in construction performance. However, the ANCOVA model indicates that the GDP is more important than political administration in the performance of the CNS. The study recommends for a new national construction policy, favourable fiscal and monetary policy, local content development policy and construction credit guaranty scheme for the rapid growth and development of the NCS. Originality/value Hitherto, little is known about the influence of political administration on the performance of the CNS. This study provides empirical evidence from a developing economy perspective. It presents the relationships and highlights recommendations for driving growth in the construction industry.

Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zulkifli Halim ◽  
Shuhaida Mohamed Shuhidan ◽  
Zuraidah Mohd Sanusi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Distress ◽  
Time Series Data ◽  
Series Data ◽  
Learning Models ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Gated Recurrent Unit

PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

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