International Journal of Computational Economics and Econometrics (12 papers in press)

Regular Issues

• Fiscal policy feasibility in Tunisia: a neo-Keynesian DSGE model approach  
  by Slah Slimani 
  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.
  Keywords: neo-Keynesian model; dynamic and stochastic general equilibrium; DSGE; fiscal policy; public spending; shocks.
  DOI: 10.1504/IJCEE.2022.10049136
   
  
• An empirical analysis of herding behavior : evidence from developed and frontier financial markets  
  by Abdelati Hakmaoui, Ouael El Jebari 
  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.
  Keywords: herding behaviour; quantile regression; extreme returns; volatility; developed markets; frontier markets; cross-sectional absolute deviation; CSAD.
  DOI: 10.1504/IJCEE.2022.10051460
   
  
• Impact of the increase in the price of smuggled gasoline on the Beninese economy: an analysis using a dynamic computable general equilibrium model  
  by Aristide Medenou, Arouna Ogouchôni Lekoyo, Rafiou Raphaël Bétila 
  Abstract: This paper aims to study the impact of an increase in the informal oil price on Benins 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 Benins economy. The authorities should implement policies to reduce Benins dependence on the informal oil trade.
  Keywords: smuggled gasoline; kpayo; dynamic computable general equilibrium; CGE; Benin; Nigeria; computational economics.
  DOI: 10.1504/IJCEE.2023.10054174
   
  
• Interpreting return variability via the dividend-price-earnings ratio  
  by Catherine Georgiou 
  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.
  Keywords: dividend-price ratio; non-stationary ratios; modified ratios; in-sample predictive regressions; out-of-sample performance.
  DOI: 10.1504/IJCEE.2023.10054782
   
  
• Predicting stock return and volatility with machine learning and econometric models - a comparative case study of the Baltic stock market  
  by Anders Nõu, Darya Lapitskaya, M. Hakan Eratalay, Rajesh Sharma 
  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.
  Keywords: machine learning; neural networks; autoregressive moving average; ARMA; generalised autoregressive conditionally heteroscedastic; GARCH.
  DOI: 10.1504/IJCEE.2023.10056253
   
  
• The (relative) importance of the attack in the game of football: evidence from a team-level study of Italian Serie A  
  by Siyan Chen, Saul Desiderio 
  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.
  Keywords: football; strikers; team success; Italian Serie A; pooled OLS; probit.
  DOI: 10.1504/IJCEE.2022.10044361
   
  
• Polarisation, institutional quality, and social cohesion: evidence in worldwide scenario  
  by Muhammad Nadeem, Mumtaz Anwar, Zahid Pervaiz 
  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.
  Keywords: polarisation; institutional quality; social cohesion; income inequality; globalisation; gender equality; per capita income; panel data; Hausman test; fixed-effect model.
  DOI: 10.1504/IJCEE.2022.10049339
   
  
• Quantile regression-based seasonal adjustment  
  by Massimiliano Caporin, Mohammed Elseidi 
  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.
  Keywords: quantile regression; seasonal adjustment; deterministic seasonal patterns.
  DOI: 10.1504/IJCEE.2022.10045739
   
  
• Price stickiness and wage stickiness in generalised new Keynesian model  
  by Rui Wang 
  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%.
  Keywords: price stickiness; wage stickiness; generalised new Keynesian model; distortion.
  DOI: 10.1504/IJCEE.2022.10050457
   
  
• Measuring market power in antitrust: a new hybrid approach  
  by Walid Gani 
  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.
  Keywords: algorithm; competition law; computational antitrust; market power; market share; mark-up.
  DOI: 10.1504/IJCEE.2022.10048420
   
  

Special Issue on: Health Economics and Econometrics

• Technical efficiency in Irish public hospitals: a multi-output distance function SFA approach  
  by Niall Devitt, Marta Zieba, Declan Dineen 
  Abstract: This paper estimates output-oriented technical efficiency (TE) for 37 acute public hospitals in Ireland using monthly panel data for the years 2017-2018. To allow an accurate estimation of multi-output production technology, we utilise a trans-logarithmic output distance function (ODF) and apply the true random-effects (TRE) stochastic frontier model which accounts for both transient and persistent inefficiency. The findings indicate that Irish hospitals operate with an average short-run efficiency score of 0.93-0.94, whereas the persistent efficiency is 0.90. Furthermore, all input elasticities are positive but the returns to scale are decreasing. Inpatient discharges account for the highest output elasticity and the highest marginal rate of transformation. While the hospital model-type increases the level of output in hospitals, the share of emergency patients negatively affects the hospitals production. Moreover, the length of stay is an important contributor to hospitals inefficiency and medium-sized hospitals with 200 to 400 beds are the most efficient hospital units.
  Keywords: hospitals; technical efficiency; TE; output distance function; ODF; true random-effects; TRE; SFA; efficiency determinants; Ireland.
  DOI: 10.1504/IJCEE.2022.10051477
   
  
• Might low-protein diet for chronic kidney disease patients be successful A case study with the application of a random effects ordered probit model.  
  by Lara Gitto, Valeria Cernaro, Guido Gembillo, Alfredo Laudani, Daniela Metro, Domenico Santoro 
  Abstract: A low-protein diet (LPD) in chronic kidney disease (CKD) patients delays the natural progression towards end-stage renal disease. The identification of the factors that guarantee patients adherence to the diet may help physicians to provide a better assistance as well as improving patients quality of life. Fifty-one patients following a LPD were asked to assess their satisfaction with the diet, difficulties in complying with the nutritional regime and if they felt their health had improved. A random effect ordered probit model, whose dependent variable is patients perceived health states (better, unchanged, worse) following the diet was estimated. After six months, 49% of patients stated that their conditions improved. Age, gender and number of comorbidities had an impact on the probability to report worse health conditions. The results emphasise the importance of an appropriate nutritional regime for CKD patients and signal the need to design support programs to promote adherence.
  Keywords: chronic kidney disease; CKD; low-protein diet; LPD; random effects ordered probit model; promoting adherence.
  DOI: 10.1504/IJCEE.2023.10056058