Template-Type: ReDIF-Article 1.0 Author-Name: Slah Slimani Author-X-Name-First: Slah Author-X-Name-Last: Slimani Title: Fiscal policy feasibility in Tunisia: a neo-Keynesian DSGE model approach Abstract: The main objective of this article is to apply a neo-Keynesian DSGE model with nominal rigidity and monopolistic competition to analyse the impact of public expenditure's variation in Tunisia on the main macroeconomic aggregates (business cycle, private consumption, wages, interest rate and inflation rate). The simulation results show that the implementation of fiscal policy via the increase in public spending in Tunisia is pro-cyclical. Indeed, the increase in public spending generates two first effects. GDP increases due to the rise in labour supply and the rise in aggregate demand due to an incomplete crowding out of private consumption. Thus, after the rise in aggregate demand, the Central Bank increases the nominal interest rate, which evolves in concert with the inflation rate to counter inflationary pressures. Consequently, households reduce their consumption expenditure as the real interest rate rises. At this level, some companies react to the change in the interest rate by reducing their expenses, their employment requirements and their capital utilisation rates. Journal: Int. J. of Computational Economics and Econometrics Pages: 353-373 Issue: 4 Volume: 13 Year: 2023 Keywords: neo-Keynesian model; dynamic and stochastic general equilibrium; DSGE; fiscal policy; public spending; shocks. File-URL: http://www.inderscience.com/link.php?id=133895 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:4:p:353-373 Template-Type: ReDIF-Article 1.0 Author-Name: Abdelati Hakmaoui Author-X-Name-First: Abdelati Author-X-Name-Last: Hakmaoui Author-Name: Ouael El Jebari Author-X-Name-First: Ouael El Author-X-Name-Last: Jebari Title: An empirical analysis of herding behaviour: evidence from developed and frontier financial markets Abstract: The present article aims at testing for the existence of herding under different market conditions in the financial markets of the USA, France, Morocco, and Tunisia. We use the newly innovated models of the cross-sectional absolute deviation (CSAD) suggested by Pochea et al. (2017), to which we apply a quantile regression for a more thorough analysis. The results confirm the existence of herding bias in the stock markets of the USA and Morocco, whilst the detailed analysis of herding dynamics has suggested different patterns of herding between developed and frontier markets. Herding behaviour is more pronounced in frontier markets under extreme market returns and volatility. This paper concludes that herding may be a viable investing strategy in developed markets under normal conditions, with investors' tendency to rely on the fundamentals during periods of turbulent markets conditions. No evidence of herding is detected in the French and the Tunisian financial markets. Journal: Int. J. of Computational Economics and Econometrics Pages: 374-403 Issue: 4 Volume: 13 Year: 2023 Keywords: herding behaviour; quantile regression; extreme returns; volatility; developed markets; frontier markets; cross-sectional absolute deviation; CSAD. File-URL: http://www.inderscience.com/link.php?id=133899 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:4:p:374-403 Template-Type: ReDIF-Article 1.0 Author-Name: Aristide Medenou Author-X-Name-First: Aristide Author-X-Name-Last: Medenou Author-Name: Arouna Ogouchôni Lekoyo Author-X-Name-First: Arouna Ogouchôni Author-X-Name-Last: Lekoyo Author-Name: Rafiou Raphaël Bétila Author-X-Name-First: Rafiou Raphaël Author-X-Name-Last: Bétila Title: Impact of the increase in the price of smuggled gasoline on the Beninese economy: an analysis using a dynamic computable general equilibrium model Abstract: This paper aims to study the impact of an increase in the informal oil price on Benin's economy. To this end, we have opted for a dynamic computable general equilibrium model calibrated on data from the 2015 Social Accounting Matrix (SAM). The two simulated scenarios involve an oil price increase of 50% and 5%. The results show that an increase in the informal oil price harms GDP, leading to higher production costs and lower wages and incomes for resident agents. The sectoral analysis reveals that this rise affects more the oil-intensive sectors. Overall, our model predicts that the impact of higher prices for informal refined oil from Nigeria hurts Benin's economy. The authorities should implement policies to reduce Benin's dependence on the informal oil trade. Journal: Int. J. of Computational Economics and Econometrics Pages: 404-422 Issue: 4 Volume: 13 Year: 2023 Keywords: smuggled gasoline; kpayo; dynamic computable general equilibrium; CGE; Benin; Nigeria; computational economics. File-URL: http://www.inderscience.com/link.php?id=133910 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:4:p:404-422 Template-Type: ReDIF-Article 1.0 Author-Name: Catherine Georgiou Author-X-Name-First: Catherine Author-X-Name-Last: Georgiou Title: Interpreting return variability via the dividend-price-earnings ratio Abstract: This paper aims to introduce a new predictor of returns based on the long-run equilibrium relationship between dividends, prices and earnings (dpe for short). We compare results to the classical dividend-price (dp) and its modified version (mdp based on the cointegration relationship between dividends and prices). An investor who employs dpe and mdp improves in-sample forecasts by 49% and 43% respectively at the ten-year horizon, against dp which interprets merely 22% of time-varying expected returns. Additionally, out-of-sample (oos) performance testing shows that dp fails to generalise well, while mdp proves the strongest oos performer. Our proposed dpe contributes to empirical literature by resolving certain econometric issues and enhancing predictability findings in return forecasting. Also, this study introduces a simple modification in treating dividends, prices and earnings that can be easily replicated by practitioners in the field and can aid the work of financial analysts, investors, fellow researchers and portfolio managers. Journal: Int. J. of Computational Economics and Econometrics Pages: 423-445 Issue: 4 Volume: 13 Year: 2023 Keywords: dividend-price ratio; non-stationary ratios; modified ratios; in-sample predictive regressions; out-of-sample performance. File-URL: http://www.inderscience.com/link.php?id=133912 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:4:p:423-445 Template-Type: ReDIF-Article 1.0 Author-Name: Anders Nõu Author-X-Name-First: Anders Author-X-Name-Last: Nõu Author-Name: Darya Lapitskaya Author-X-Name-First: Darya Author-X-Name-Last: Lapitskaya Author-Name: M. Hakan Eratalay Author-X-Name-First: M. Hakan Author-X-Name-Last: Eratalay Author-Name: Rajesh Sharma Author-X-Name-First: Rajesh Author-X-Name-Last: Sharma Title: Predicting stock return and volatility with machine learning and econometric models - a comparative case study of the Baltic stock market Abstract: For stock market predictions, a crucial problem is predicting the prices as accurately as possible. There are different approaches (for example, econometrics and machine learning) for predicting stock returns. However, it is non-trivial to find an approach which works the best. In Baltic countries, the interest in stock market investment has grown due to the popularisation of investment and the favourable trading conditions of banks, however, there are very few attempts to apply machine learning and neural network models in the studies of the Baltic region. In this paper, we make a thorough analysis of the predictive accuracy of several machine learning and econometric approaches (ARMA, GARCH, random forest, SVR, KNN and GARCH-ANN) for predicting the returns and volatilities on the OMX Baltic Benchmark Price Index. Our results show that the machine learning methods predict the returns better than autoregressive moving average models for most of the metrics. Journal: Int. J. of Computational Economics and Econometrics Pages: 446-489 Issue: 4 Volume: 13 Year: 2023 Keywords: machine learning; neural networks; autoregressive moving average; ARMA; generalised autoregressive conditionally heteroscedastic; GARCH. File-URL: http://www.inderscience.com/link.php?id=133923 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:4:p:446-489 Template-Type: ReDIF-Article 1.0 Author-Name: Muhammad Nadeem Author-X-Name-First: Muhammad Author-X-Name-Last: Nadeem Author-Name: Mumtaz Anwar Author-X-Name-First: Mumtaz Author-X-Name-Last: Anwar Author-Name: Zahid Pervaiz Author-X-Name-First: Zahid Author-X-Name-Last: Pervaiz Title: Polarisation, institutional quality, and social cohesion: evidence in worldwide scenario Abstract: The current study is an effort to investigate the impact of legal institutional quality on social cohesion in the presence of polarisation by using panel data. The results of the study indicate that legal institutions help to nurture social cohesion. Polarisation is a threat to social cohesion and this threat is more when there is the coexistence of low legal institutional quality and high polarisation. To check the impact of coexistence of legal institutional quality and polarisation on social cohesion, an interaction term has been used. The results of the interactive term indicate that in the presence of better-quality legal institutions, the negative effects of polarisation are vanished. Income inequality and globalisation are also found bad for social cohesion. Gender equality, per capita income level, enhance the level of social cohesion in a society. There is a need to develop high-quality legal institutions to enhance the level of social cohesion. Journal: Int. J. of Computational Economics and Econometrics Pages: 256-269 Issue: 3 Volume: 13 Year: 2023 Keywords: polarisation; institutional quality; social cohesion; income inequality; globalisation; gender equality; per capita income; panel data; Hausman test; fixed-effect model. File-URL: http://www.inderscience.com/link.php?id=132137 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:3:p:256-269 Template-Type: ReDIF-Article 1.0 Author-Name: Siyan Chen Author-X-Name-First: Siyan Author-X-Name-Last: Chen Author-Name: Saul Desiderio Author-X-Name-First: Saul Author-X-Name-Last: Desiderio Title: The (relative) importance of the attack in the game of football: evidence from a team-level study of Italian Serie A Abstract: Attackers are recognised as the most important players of football, and this reflects on their high wages and market values. A natural question is whether such high financial costs are justified by the actual contribution of the attack to the success of a football team. In this paper we use team-level data relative to 34 seasons of Italian Serie A to see if the offensive sector as a whole is indeed the major determinant of the strength of a team. Results show only a moderate prevalence of the attack over the defence, which suggests that offensive players are likely to be overvalued with respect to their actual contribution to the strength of the team. In addition, we found that a good defence is the key for ending the season on top spots, whereas a bad attack is the main reason for ending at the bottom of the ranking. Journal: Int. J. of Computational Economics and Econometrics Pages: 243-255 Issue: 3 Volume: 13 Year: 2023 Keywords: football; strikers; team success; Italian Serie A; pooled OLS; probit. File-URL: http://www.inderscience.com/link.php?id=132138 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:3:p:243-255 Template-Type: ReDIF-Article 1.0 Author-Name: Massimiliano Caporin Author-X-Name-First: Massimiliano Author-X-Name-Last: Caporin Author-Name: Mohammed Elseidi Author-X-Name-First: Mohammed Author-X-Name-Last: Elseidi Title: Quantile regression-based seasonal adjustment Abstract: We introduce a seasonal adjustment method based on quantile regression that focuses on capturing different forms of deterministic seasonal patterns. Given a variable of interest, by describing its seasonal behaviour over an approximation of the entire conditional distribution, we are capable of removing seasonal patterns affecting the mean and/or the variance or seasonal patterns varying over quantiles of the conditional distribution. We provide empirical examples based on simulated and real data through which we compare our proposal to least squares approaches. Journal: Int. J. of Computational Economics and Econometrics Pages: 270-304 Issue: 3 Volume: 13 Year: 2023 Keywords: quantile regression; seasonal adjustment; deterministic seasonal patterns. File-URL: http://www.inderscience.com/link.php?id=132144 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:3:p:270-304 Template-Type: ReDIF-Article 1.0 Author-Name: Spyros P. Georgopoulos Author-X-Name-First: Spyros P. Author-X-Name-Last: Georgopoulos Author-Name: Panagiotis Tziatzios Author-X-Name-First: Panagiotis Author-X-Name-Last: Tziatzios Author-Name: Stavros G. Stavrinides Author-X-Name-First: Stavros G. Author-X-Name-Last: Stavrinides Author-Name: Ioannis P. Antoniades Author-X-Name-First: Ioannis P. Author-X-Name-Last: Antoniades Author-Name: Michael P. Hanias Author-X-Name-First: Michael P. Author-X-Name-Last: Hanias Title: Reservoir computing vs. neural networks in financial forecasting Abstract: Stock market prediction techniques are a major research area, thus, extracting time-dependent patterns for the existing predictive models is of major significance. In this work, we compare forecasting performance of the nonlinear model of recurrent neural networks (RNN) in two implementations, LSTM and CNN-LSTM, to the relatively novel approach of reservoir computing (RC), and in specific, the particular class of the echo state networks (ESN). This comparison focuses on exploiting data latent dynamics, in performing efficient training and high quality predictions of the evolution of real-world financial data. Applying a multivariate scheme to a stock market index without any stationarity techniques, a definite precedence of the ESN-RC over both types of RNN's in computational efficiency as well as prediction quality, emerges. Finally, the implemented approach is friendly to the trader, since specific values of a stock market timeseries provide with a frame allowing for in time forecasting, under real-world circumstances. Journal: Int. J. of Computational Economics and Econometrics Pages: 1-22 Issue: 1 Volume: 13 Year: 2023 Keywords: deep learning; neural networks; reservoir computing; machine learning; time series analysis; financial-economic forecasting; algorithmic comparisons. File-URL: http://www.inderscience.com/link.php?id=127283 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:1:p:1-22 Template-Type: ReDIF-Article 1.0 Author-Name: Greta Falavigna Author-X-Name-First: Greta Author-X-Name-Last: Falavigna Author-Name: Alessandro Manello Author-X-Name-First: Alessandro Author-X-Name-Last: Manello Title: The influence of financial and technological structure on eco-efficiency: an application of DDF bootstrapped framework in the Italian polluting industries Abstract: In this paper, we estimate environmental corrected efficiency scores for a large sample of Italian firms operating in four different polluting industrial sectors subjected to the same European normative framework. Merging economic and emission data coming from reliable public sources, we measure overall performances through the non-parametric directional distance function and in order to improve the robustness of the results, we perform an extension of the bootstrap proposed for standard efficiency scores. Results are analysed through a truncated regression after testing for the validity of separability condition between input-output space and explanatory variables as well as in light of industrial specificity. Results show that both the financial structure and the technological status of the firms have a significant explanatory power in relation to environmental corrected efficiency scores. Policymakers should carefully consider both aspects as important issues for supporting sustainable practices. Journal: Int. J. of Computational Economics and Econometrics Pages: 35-60 Issue: 1 Volume: 13 Year: 2023 Keywords: environmental corrected efficiency; directional distance function; DDF; bootstrapping; two-stage procedure; separability conditions. File-URL: http://www.inderscience.com/link.php?id=127290 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:1:p:35-60 Template-Type: ReDIF-Article 1.0 Author-Name: Walid Gani Author-X-Name-First: Walid Author-X-Name-Last: Gani Title: Measuring market power in antitrust: a new hybrid approach Abstract: This paper proposes a new hybrid approach for measuring market power in antitrust using a mixture of market share calculation, price comparison, and mark-up assessment. The proposed approach is termed 'hybrid' because it combines elements from structural and non-structural approaches. These elements are directly observable and measurable, making our proposed approach more practical and realistic for the antitrust analysis requirements. Through an empirical study involving the use of real industrial data, we show that our hybrid approach provides a more reliable estimation of market power in comparison with existing methods. Also, we empirically prove that classical approaches are myopic regarding the pricing behaviour of firms with market power. This drawback may increase the type I error in the assessment of market power and lead to the over-enforcement of competition law. Journal: Int. J. of Computational Economics and Econometrics Pages: 332-351 Issue: 3 Volume: 13 Year: 2023 Keywords: algorithm; competition law; computational antitrust; market power; market share; mark-up. File-URL: http://www.inderscience.com/link.php?id=132155 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:3:p:332-351 Template-Type: ReDIF-Article 1.0 Author-Name: Ricardo Ramalhete Moreira Author-X-Name-First: Ricardo Ramalhete Author-X-Name-Last: Moreira Title: Consumption per effective labour in Brazil: testing for the optimising behaviour Abstract: We contributed to the literature on household consumption in specific ways: firstly, we adopted a long-run cointegration analysis instead of a first-difference one, which was conventional in previous works; second, by building on monthly data, we enlarged the number of observations of our sample, thus overcoming a common problem of low-frequency data in studies based on quarterly or annual time series. We thus confirmed the hypothesis of an optimising behaviour in consumption for the Brazilian economy, although jointly with a relevant role of income. We also inferred the effect of risk aversion in lowering the optimality of consumption after the <i>Subprime crisis</i> period, based on a Markov-switching approach. Journal: Int. J. of Computational Economics and Econometrics Pages: 23-34 Issue: 1 Volume: 13 Year: 2023 Keywords: consumption; households; interest rate; income; Brazil. File-URL: http://www.inderscience.com/link.php?id=127291 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:1:p:23-34 Template-Type: ReDIF-Article 1.0 Author-Name: Agnieszka Kleszcz Author-X-Name-First: Agnieszka Author-X-Name-Last: Kleszcz Title: The use of classification models to identify factors differentiating the competitiveness of the EU-15 and EU-13 countries Abstract: This paper reports on a study of the Global Competitiveness Index pillars, aiming to differentiate the European Union countries grouped by their accession year in terms of their competitiveness. A linear (regularised logistic regression) and nonlinear (random forests) classifiers are proposed, to model the relationship between multidimensional economic condition indicators and the country's group. The key discriminators of the competitiveness of the EU-15 (accession before 2004) and the EU-13 (accession in or after 2004) are obtained by analysis of feature importance in classification models. Upon study of 12 competitive indicators from the World Economic Reports (2007-2017 edition) we conclude that the highest disparities between the groups of countries can be observed in infrastructure. Innovation, market size and institutions are the next three most important differentiating factors. A major methodological contribution of the paper is the use of explainable statistical models for identifying key features differentiating groups of countries. Journal: Int. J. of Computational Economics and Econometrics Pages: 110-128 Issue: 1 Volume: 13 Year: 2023 Keywords: logistic regression; random forest; European Union; Global Competitiveness Index; GCI; feature importance. File-URL: http://www.inderscience.com/link.php?id=127296 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:1:p:110-128 Template-Type: ReDIF-Article 1.0 Author-Name: Rui Wang Author-X-Name-First: Rui Author-X-Name-Last: Wang Title: Price stickiness and wage stickiness in generalised new Keynesian model Abstract: Given different non-zero annual target inflation rates, we extend the standard new Keynesian dynamic stochastic general equilibrium (DSGE) model to allow both staggered price setting and staggered wage setting and derive a generalised version of new Keynesian model to study how these distortions affect the steady state and dynamics of model. The main finding is that the imperfection of labour market has more distortionary power on aggregate output and aggregate welfare given positive target inflation rate. The change in structural parameters that represents the monopolistic competition in intermediate-good market and labour market result in asymmetric distortion effect on the steady state of aggregate output and aggregate welfare. This asymmetric effect is especially significant given higher target inflation rate. Given the same target inflation rate, wage stickiness is more distortionary than the price stickiness. The existence of positive target inflation rate can also change the first-order dynamics of model, amplifying or reducing the dynamic response of model according to the type of exogenous shocks. Numerical results also provide us a macroeconomic structural model-based explanation for the reason that the most central banks set the target inflation rate within a range from 1% to 2%. Journal: Int. J. of Computational Economics and Econometrics Pages: 305-331 Issue: 3 Volume: 13 Year: 2023 Keywords: price stickiness; wage stickiness; generalised new Keynesian model; distortion. File-URL: http://www.inderscience.com/link.php?id=132162 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:3:p:305-331 Template-Type: ReDIF-Article 1.0 Author-Name: Athanasios Anastasiou Author-X-Name-First: Athanasios Author-X-Name-Last: Anastasiou Author-Name: Charalampos Kalligosfyris Author-X-Name-First: Charalampos Author-X-Name-Last: Kalligosfyris Author-Name: Eleni Kalamara Author-X-Name-First: Eleni Author-X-Name-Last: Kalamara Title: Measuring tax administrations efficiency using data envelopment analysis: evidence from 26 European countries Abstract: The purpose of this paper is to assess the efficiency of tax administrations of 26 European countries, using data envelopment analysis. In particular, by applying the CCR data environment analysis (DEA) output-oriented model, the quantification of the tax administrations performance of 26 European countries is attempted in the areas of taxpayers' servicing, public revenue collection, strengthening voluntary tax compliance and targeted tax audits, the assessment of relative efficiency, the evaluation of results and the identification of fully efficient and inefficient tax administrations, in which a real improvement in their efficiency can be achieved. Subsequently, for the tax administrations that are assessed as inefficient, the reference units are identified, the missing quantity of outputs and the excess amount of inputs are estimated, in order to make them efficient and a set of possible ways of improving their operation is proposed, through specific changes. Journal: Int. J. of Computational Economics and Econometrics Pages: 61-109 Issue: 1 Volume: 13 Year: 2023 Keywords: tax administration; efficiency; data envelopment analysis; DEA; tax compliance; tax audit; tax revenue. File-URL: http://www.inderscience.com/link.php?id=127311 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:1:p:61-109 Template-Type: ReDIF-Article 1.0 Author-Name: Dimitrios Kartsonakis-Mademlis Author-X-Name-First: Dimitrios Author-X-Name-Last: Kartsonakis-Mademlis Author-Name: Nikolaos Dritsakis Author-X-Name-First: Nikolaos Author-X-Name-Last: Dritsakis Title: The stock market - oil prices variability relationship in the USA: the financial crisis effect Abstract: This paper employs bivariate GARCH models to investigate the relationship between Dow Jones industrial average index and crude oil Brent. The models are used to generate the conditional variances of our indices and test for volatility spillover effects. Our evidence supports that for the entire sample period, there is no causal relationship between the volatilities of Dow Jones and Brent. For the period before the financial crisis, there is evidence of a unidirectional link regarding the transmission of shocks from the stock to the oil market and a bidirectional link concerning the volatility spillover between the markets. Considering the period of the crisis, bidirectional shock and volatility linkages are found. In contrast, for the period after the financial crisis, only the effect of the transmission of shocks and volatility spillover from Dow Jones to Brent is significant. We also compute the optimal portfolio weights and dynamic risk-minimising hedge ratios to highlight the importance of our empirical results. Journal: Int. J. of Computational Economics and Econometrics Pages: 129-152 Issue: 2 Volume: 13 Year: 2023 Keywords: BEKK-GARCH model; financial crisis; stock market; oil prices; volatility spillover. File-URL: http://www.inderscience.com/link.php?id=129976 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:2:p:129-152 Template-Type: ReDIF-Article 1.0 Author-Name: Hager Farhoud Author-X-Name-First: Hager Author-X-Name-Last: Farhoud Author-Name: Lotfi Taleb Author-X-Name-First: Lotfi Author-X-Name-Last: Taleb Title: Unreplicated factorial experimental designs for offline quality improvement and industrial process optimisation Abstract: A problem frequently encountered in the industrial offline improvement of quality is to identify from among many factors, those which are responsible for large changes in the quality characteristics, namely factors with active location and/or dispersion effects. Unreplicated experimental designs propose economic tools to discover what manufacturing conditions minimise product variation, maintain product measurements near the desired target value and make the product insensitive to environmental changes. However, no degrees of freedom are left to estimate the experimental error. To remove this dependency structure of residuals at the high and low levels of factor combinations, this study first addresses a synthesis and a critical analysis of existing location and dispersion effect identification methods. Second, a new method is proposed, and robustness check is based on real example and extensive simulation study. Third, practical issues are presented to enlighten investigators in their decision-making process. Journal: Int. J. of Computational Economics and Econometrics Pages: 153-167 Issue: 2 Volume: 13 Year: 2023 Keywords: statistical process optimisation; quality engineering; screening designs; IER; EER; modified residuals. File-URL: http://www.inderscience.com/link.php?id=129977 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:2:p:153-167 Template-Type: ReDIF-Article 1.0 Author-Name: Olalekan Bashir Aworinde Author-X-Name-First: Olalekan Bashir Author-X-Name-Last: Aworinde Title: Globalisation and the Nigerian environment: empirical evidence from quantile cointegration Abstract: This paper examines the validity of the environmental Kuznets curve (EKC) in Nigeria by exploring the impact of economic growth, overall, economic, social, and political globalisation on Nigerian ecological footprint using the recently developed quantile autoregressive distributed lag (Q-ARDL) technique for the period of 1970Q1-2018Q4. The findings of the linear ARDL support the presence of a long-run relationship and validity of EKC in all the four models considered. The Q-ARDL results showed that the assumptions of the error-correction terms are met across all quantiles. The long-run results reveal evidence of an inverted U-shaped EKC in Nigeria. Additionally, the long-run period shows that overall, social, economic, and political globalisation worsens the Nigerian environment. This study, therefore, recommends that the Nigerian Government should adopt energy-efficient environmental policies that will promote green growth development. Journal: Int. J. of Computational Economics and Econometrics Pages: 168-188 Issue: 2 Volume: 13 Year: 2023 Keywords: globalisation; environment; Nigeria; environmental Kuznets curve; EKC; quantile autoregressive distributed lag; Q-ARDL; ecological footprint. File-URL: http://www.inderscience.com/link.php?id=129978 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:2:p:168-188 Template-Type: ReDIF-Article 1.0 Author-Name: Fadzilah Salim Author-X-Name-First: Fadzilah Author-X-Name-Last: Salim Author-Name: Nur Azman Abu Author-X-Name-First: Nur Azman Author-X-Name-Last: Abu Title: An S-curve efficient frontier on second-hand auto price Abstract: An efficient frontier has been a popular concept in a capital asset pricing model in the last 50 years. An efficient frontier will be explored as a practical predictive model for second-hand automobile prices. It is a practically useful model that comes with an upper price recommendation on the market to suggest maximum possible coverage by auto insurance. A nonlinear model has been observed to give a better estimate of price appreciation while describing real-life phenomena. In this paper, an S-efficient frontier curve model is proposed as a simple nonlinear model used for estimating second-hand automobile pricing. A dynamic S-shaped membership function (SMF) will be used as a basis to construct an S-curve algorithm in this automobile price model. An S-curve model is found to offer a useful and practical estimation regarding second-hand auto prices. Therefore, an S-curve efficient frontier model along automobile make years is expected to provide a better estimate of second-hand auto pricing in Malaysia. Journal: Int. J. of Computational Economics and Econometrics Pages: 189-215 Issue: 2 Volume: 13 Year: 2023 Keywords: S-curve model; efficient frontier; second-hand auto price; price modelling. File-URL: http://www.inderscience.com/link.php?id=129979 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:2:p:189-215 Template-Type: ReDIF-Article 1.0 Author-Name: Ruzhdie Bici Author-X-Name-First: Ruzhdie Author-X-Name-Last: Bici Title: Simple methods to handle missing data Abstract: Missing data are a common problem in big data sets. Specifically, missing data are present in surveys and in different studies, leading to increase of variance and unreliable results. While most of the researchers focus on the analysis of more sophisticated methods, the simplest techniques are not treated in detail. The article explains the theoretical concepts of different types of missing data, the causes of missing data, and analyses methods on how to deal with missing data. The focus is using simple imputation techniques (mean imputation, regression imputation and non-treating missing at all). The analysis is done using Malawi data, IHS5 2019-2020 survey data. In this article, the interest is to know the whole property values (selling and renting) in the country, while the information in these variables is partly not filled. The results show how the different imputation methods influence the results and sometimes the value is predicted from other auxiliary variables. Journal: Int. J. of Computational Economics and Econometrics Pages: 216-242 Issue: 2 Volume: 13 Year: 2023 Keywords: simple methods; missing data; handle missing data; imputation; regression; non-response. File-URL: http://www.inderscience.com/link.php?id=129986 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:13:y:2023:i:2:p:216-242