Template-Type: ReDIF-Article 1.0 Author-Name: Lawrence Dhliwayo Author-X-Name-First: Lawrence Author-X-Name-Last: Dhliwayo Author-Name: Florance Matarise Author-X-Name-First: Florance Author-X-Name-Last: Matarise Author-Name: Charles Chimedza Author-X-Name-First: Charles Author-X-Name-Last: Chimedza Title: Modelling seasonal fractionally integrated process with volatility and structural change Abstract: This study investigates fractionally integrated processes, specifically SARFIMA-GARCH models with structural changes. These models encompass four key aspects of time series data: seasonality, fractional integration, volatility, and structural change. The primary focus of this study is to extend the seasonal structural change detection test for both mean and volatility in a given realisation. The parameters for the seasonal structural change (SSC)-SARFIMA and seasonal structural change (SSC)-GARCH models were derived. Additionally, we establish test statistics that are crucial for assessing the statistical significance of seasonal structural change in a SARFIMA-GARCH model. A simulation study was conducted to demonstrate the reliability of the derived detection procedures. Journal: Int. J. of Computational Economics and Econometrics Pages: 468-485 Issue: 4 Volume: 14 Year: 2024 Keywords: time series analysis; seasonality; fractional integration; structural change; SSC-SARFIMA; SSC-GARCH. File-URL: http://www.inderscience.com/link.php?id=142098 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:4:p:468-485 Template-Type: ReDIF-Article 1.0 Author-Name: Giorgia Marini Author-X-Name-First: Giorgia Author-X-Name-Last: Marini Title: Editorial Abstract: The volume consists of a selection of six papers, all of them containing applications of classical and advanced statistical techniques and econometric methods to original datasets that provide policy makers with useful and relevant results in terms of procedures and planning (Atella and Decarolis), hospital efficiency (Devitt et al. and Mozhaeva and Barzdins), public health (Bhattacharya and Marini), health-affecting behaviours and nutritional regime (Gitto et al.) and health inequalities (Giannoni). Journal: Int. J. of Computational Economics and Econometrics Pages: 99-102 Issue: 2 Volume: 14 Year: 2024 Keywords: policy makers; health economics; health; healthcare; Econometrics. File-URL: http://www.inderscience.com/link.php?id=138519 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:2:p:99-102 Template-Type: ReDIF-Article 1.0 Author-Name: Niall Devitt Author-X-Name-First: Niall Author-X-Name-Last: Devitt Author-Name: Marta Zieba Author-X-Name-First: Marta Author-X-Name-Last: Zieba Author-Name: Declan Dineen Author-X-Name-First: Declan Author-X-Name-Last: Dineen Title: Technical efficiency in Irish public hospitals: a multi-output distance function SFA approach 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. Journal: Int. J. of Computational Economics and Econometrics Pages: 122-150 Issue: 2 Volume: 14 Year: 2024 Keywords: hospitals; technical efficiency; TE; output distance function; ODF; true random-effects; TRE; SFA; efficiency determinants; Ireland. File-URL: http://www.inderscience.com/link.php?id=137891 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:2:p:122-150 Template-Type: ReDIF-Article 1.0 Author-Name: Lara Gitto Author-X-Name-First: Lara Author-X-Name-Last: Gitto Author-Name: Valeria Cernaro Author-X-Name-First: Valeria Author-X-Name-Last: Cernaro Author-Name: Guido Gembillo Author-X-Name-First: Guido Author-X-Name-Last: Gembillo Author-Name: Alfredo Laudani Author-X-Name-First: Alfredo Author-X-Name-Last: Laudani Author-Name: Daniela Metro Author-X-Name-First: Daniela Author-X-Name-Last: Metro Author-Name: Domenico Santoro Author-X-Name-First: Domenico Author-X-Name-Last: Santoro Title: Might low-protein diet for chronic kidney disease patients be successful? A case study with the application of a random effects ordered probit model 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. Journal: Int. J. of Computational Economics and Econometrics Pages: 197-213 Issue: 2 Volume: 14 Year: 2024 Keywords: chronic kidney disease; CKD; low-protein diet; LPD; random effects ordered probit model; promoting adherence. File-URL: http://www.inderscience.com/link.php?id=137901 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:2:p:197-213 Template-Type: ReDIF-Article 1.0 Author-Name: Irina Mozhaeva Author-X-Name-First: Irina Author-X-Name-Last: Mozhaeva Author-Name: Juris Barzdins Author-X-Name-First: Juris Author-X-Name-Last: Barzdins Title: The role of home healthcare in reducing hospital readmissions and costs in patients with acute myocardial infarction Abstract: The aim of this study is to examine the causal effects of post-discharge home healthcare services on hospital readmissions and public inpatient expenditure in the older myocardial infarction patient cohort. We employ individual-level administrative healthcare data and apply the dynamic difference-in-differences approach to estimate the contemporaneous and post-intervention causal effects of homecare. The results suggest that post-acute home healthcare provided to myocardial infarction survivors has a strong prolonged negative (i.e., favourable) effect on the probability of hospital readmissions and related costs. The patterns of the post-intervention effect point to considerable health improvements in patients referred to domiciliary care compared to their counterparts discharged with self-care. The indicated benefits of home healthcare provide grounds for reconsidering current eligibility criteria of this public program and expanding its coverage in the myocardial infarction patient cohort. Journal: Int. J. of Computational Economics and Econometrics Pages: 151-171 Issue: 2 Volume: 14 Year: 2024 Keywords: home healthcare; acute myocardial infarction; readmissions; public inpatient expenditure; average treatment effect; ATE. File-URL: http://www.inderscience.com/link.php?id=137909 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:2:p:151-171 Template-Type: ReDIF-Article 1.0 Author-Name: Jay Bhattacharya Author-X-Name-First: Jay Author-X-Name-Last: Bhattacharya Author-Name: Giorgia Marini Author-X-Name-First: Giorgia Author-X-Name-Last: Marini Title: Is the European refugee crisis a potential threat to public health? Evidence from Italy Abstract: We examine the impact of both disembarkations (the raw number of disembarked people) and integrated refugees (the number of disembarked people identified as asylum seekers and integrated in the country) on a broad set of infectious diseases and healthcare expenditure, respectively on a panel of 23 (1998-2020) and 14 years (2005-2018) for 20 Italian regions. We find a statistically significant and clinically meaningful correlation between refugee influx (measured as the number of disembarked people) and some diseases. These results pose some important questions on screening and prevention, costs associated with them and changes to the local epidemiology. Moreover, as regions with higher refugee influx experienced higher healthcare expenditure in the year the refugee influx occurred, a sustained refugee influx may have an impact on healthcare costs, which may raise a problem of sustainability of the national healthcare system. Journal: Int. J. of Computational Economics and Econometrics Pages: 172-196 Issue: 2 Volume: 14 Year: 2024 Keywords: refugees; infectious diseases; healthcare expenditure; public health; Italy. File-URL: http://www.inderscience.com/link.php?id=137910 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:2:p:172-196 Template-Type: ReDIF-Article 1.0 Author-Name: Margherita Giannoni Author-X-Name-First: Margherita Author-X-Name-Last: Giannoni Title: Financial problems and self-reported health status: an analysis for selected European countries Abstract: This paper investigates the relation between financial difficulties and self-reported health status for adult individuals living in France, Italy and the UK. A set of random effects Probit models is estimated for the probability of reporting poor physical health status, chronic conditions, and limitations in daily life using Eurostat-EU-SILC 2010-2014 longitudinal data for France, Italy, and the UK. All estimates are obtained after controlling for demographic, geographic and socio-economic, as well as for measures of households' over-indebtedness and housing tenure status individual characteristics. To deal with the potential endogeneity of the indebtedness-health relationship, recursive bivariate random parameters Probit models are estimated. Over-indebtedness showed the largest effect among all economic determinants of health. It is important to protect individuals' health status during economic recessions by supporting households in financial difficulties to meet their end needs in the short term and by improving access to home ownership. Journal: Int. J. of Computational Economics and Econometrics Pages: 214-250 Issue: 2 Volume: 14 Year: 2024 Keywords: financial problems; health inequalities; health disparities; econometrics; over-indebtedness and health; housing and health inequalities. File-URL: http://www.inderscience.com/link.php?id=137911 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:2:p:214-250 Template-Type: ReDIF-Article 1.0 Author-Name: Vincenzo Atella Author-X-Name-First: Vincenzo Author-X-Name-Last: Atella Author-Name: Francesco Decarolis Author-X-Name-First: Francesco Author-X-Name-Last: Decarolis Title: Procuring medical devices: evidence from Italian public tenders Abstract: The public procurement of medical devices is increasingly relying on auction mechanisms to move toward more transparent procedures and to promote competition between suppliers in a market where the quality of the products matters enormously. An improper auction design could lead to inefficient outcomes, such as a market with higher-than-optimal prices, or lower-than-optimal quality. Based on Italian public tender data, we present new evidence on the performance of the public tenders to procure orthopaedic prosthesis for hips, knees and shoulders. Focusing on three main outcomes, the number of participants, the presence of a single firm bidding and the winning rebate, for the first time we describe how features related to the tender, hospital, region and bidders' competition all contribute to explain the functioning of the procurement auctions. The evidence we obtain can meaningfully help policymakers in designing and implementing better public procurement systems. Journal: Int. J. of Computational Economics and Econometrics Pages: 103-121 Issue: 2 Volume: 14 Year: 2024 Keywords: procurement auctions; medical devices; orthopaedic prosthesis; tender characteristics; Italy. File-URL: http://www.inderscience.com/link.php?id=137914 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:2:p:103-121 Template-Type: ReDIF-Article 1.0 Author-Name: M. Boutahar Author-X-Name-First: M. Author-X-Name-Last: Boutahar Author-Name: M. Royer-Carenzi Author-X-Name-First: M. Author-X-Name-Last: Royer-Carenzi Title: Identifying trend nature in time series using autocorrelation functions and stationarity tests Abstract: Time series non-stationarity can be detected thanks to autocorrelation functions. But trend nature, either deterministic or either stochastic, is not identifiable. Strategies based on Dickey-Fuller unit root-test are appropriate to choose between a linear deterministic trend or a stochastic trend. But all the observed deterministic trends are not linear, and such strategies fail in detecting a quadratic deterministic trend. Being a confounding factor, a quadratic deterministic trend makes a unit root spuriously appear. We provide a new procedure, based on Ouliaris-Park-Phillips unit root test, convenient for time series containing polynomial trends with a degree higher than one. Our approach is assessed based on simulated data. The strategy is finally applied on two real datasets: money stock in USA and on CO<SUB align="right"><SMALL>2</SMALL></SUB> atmospheric concentration. Compared with Dickey-Fuller diagnosis, our strategy provides the model with the best performances. Journal: Int. J. of Computational Economics and Econometrics Pages: 1-22 Issue: 1 Volume: 14 Year: 2024 Keywords: time series; stationarity; autocorrelation functions; unit root tests; Dickey-Fuller; KPSS; OPP test; trend detection; deterministic or stochastic trend; spurious unit root. File-URL: http://www.inderscience.com/link.php?id=135644 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:1:p:1-22 Template-Type: ReDIF-Article 1.0 Author-Name: Mohsin Nazir Author-X-Name-First: Mohsin Author-X-Name-Last: Nazir Author-Name: Zunaira Butt Author-X-Name-First: Zunaira Author-X-Name-Last: Butt Author-Name: Aneeqa Sabah Author-X-Name-First: Aneeqa Author-X-Name-Last: Sabah Author-Name: Azeema Yaseen Author-X-Name-First: Azeema Author-X-Name-Last: Yaseen Author-Name: Anca Jurcut Author-X-Name-First: Anca Author-X-Name-Last: Jurcut Title: Machine learning-based business risk analysis for big data: a case study of Pakistan Abstract: In finance, machine learning helps the business by improving its abilities and flexibility to prevent risks, errors and to accept such challenges. This research analyses and forecasts the interest rate risk of Pakistan using machine learning models. It took a ten-year financial dataset of Pakistan investment bonds from the State Bank of Pakistan website. In this study, a framework was proposed and four different models were developed to forecast the interest rates: neural network, bootstrap aggregated regression trees, cascade-forward neural network, and radial basis neural network. Subsequently, these models were run under four different scenarios: forecasting with original, generated, LASSO extracted and weighted average features. In addition, the outcomes of these models were compared with four performance metrics: mean absolute percentage error, daily peak mean absolute percentage error, mean absolute error, and root mean square error. Overall, the results showed that radial basis neural network provided the best forecasting. Journal: Int. J. of Computational Economics and Econometrics Pages: 23-41 Issue: 1 Volume: 14 Year: 2024 Keywords: machine learning; business risk analysis; interest rate risk; risk analysis; big data; forecasting models; Pakistan. File-URL: http://www.inderscience.com/link.php?id=135648 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:1:p:23-41 Template-Type: ReDIF-Article 1.0 Author-Name: Zhuwei Li Author-X-Name-First: Zhuwei Author-X-Name-Last: Li Author-Name: Baolu Wang Author-X-Name-First: Baolu Author-X-Name-Last: Wang Author-Name: Rong He Author-X-Name-First: Rong Author-X-Name-Last: He Title: Evaluation and improvement of two homogeneous stock trading systems under computational and experimental finance in China: based on IASM model Abstract: Artificial simulated stock market model is widely used because it can provide repeatable simulation experiment platform for different trading systems to play a role in the financial market. Based on the investor structure, trading behaviours and institutional rules of Chinese stock market, this paper uses intelligent artificial stock market (IASM) model, aims to build a comprehensive evaluation index system of stock market quality, evaluate the effect of the T + N trading system and the price limit system on the quality of Chinese stock market, and then gives analysis results and improvement suggestions. It is found that T + 0 trading system and narrowing the range of limit price fluctuation can significantly improve the quality of Chinese stock market. At the same time, among the combinations of various T + N trading systems and price limit systems, the combination of T + 0 trading system and 5% price limit system of Chinese stock market has the highest comprehensive quality. Journal: Int. J. of Computational Economics and Econometrics Pages: 363-388 Issue: 4 Volume: 14 Year: 2024 Keywords: stock trading system; IASM model; market quality evaluation; system improvement; computational and experimental finance. File-URL: http://www.inderscience.com/link.php?id=142048 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:4:p:363-388 Template-Type: ReDIF-Article 1.0 Author-Name: Mohammad Javad Sheikhzadeh Author-X-Name-First: Mohammad Javad Author-X-Name-Last: Sheikhzadeh Author-Name: Sajjad Rahmany Author-X-Name-First: Sajjad Author-X-Name-Last: Rahmany Title: Improved stock price forecasting by streamlining indicators: an approach via feature selection and classification Abstract: Accurately predicting changes in stock prices is a complex and challenging task due to the multitude of factors influencing the stock market. Stock market analysts commonly rely on indicators for forecasting, but the interpretation of these indicators is often complicated and can result in inaccurate predictions. To enhance the precision of stock price forecasting, we propose a novel approach that incorporates feature selection algorithms and classification techniques. In fact, by identifying the most impactful indicators affecting each stock's price, the process of predicting stock prices will be significantly simplified. We conducted experimental tests on stock data from multiple companies listed in the Tehran Stock Exchange, spanning 2008 to 2021. Our findings demonstrate that reducing the number of features and indicators can significantly enhance the accuracy of stock price predictions in specific scenarios. Journal: Int. J. of Computational Economics and Econometrics Pages: 42-60 Issue: 1 Volume: 14 Year: 2024 Keywords: stock forecasting; indicators; feature selection; classification. File-URL: http://www.inderscience.com/link.php?id=135655 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:1:p:42-60 Template-Type: ReDIF-Article 1.0 Author-Name: Farid Zamani Che Rose Author-X-Name-First: Farid Zamani Che Author-X-Name-Last: Rose Author-Name: Mohd Tahir Ismail Author-X-Name-First: Mohd Tahir Author-X-Name-Last: Ismail Author-Name: Nur Aqilah Khadijah Rosili Author-X-Name-First: Nur Aqilah Khadijah Author-X-Name-Last: Rosili Title: Structural breaks detection using step-indicator saturation technique in state-space model Abstract: Recently, there has been a lot of interest in identifying structural breaks in economic time series. Failing to capture any structural breaks may have a pernicious effect on model estimation due to significant forecast errors after such breaks and inappropriate tests. Therefore, this study proposed a step-indicator saturation (SIS) technique as an extension of the general-to-specific (GETS) modelling framework for detecting any structural changes in time series. Monte Carlo simulations assessed the performance of the SIS in the local level model based on potency and gauge metrics using the 'gets' package in the R programming language. Sequential selection outperformed the non-sequential approach in the automatic GETS model selection procedure. Accordingly, this study applied the SIS technique to the Financial Times Stock Exchange (FTSE) Bursa Malaysia Hijrah Shariah and FTSE USA Shariah using a split-half approach and sequential selection. The retained indicators in the terminal model were selected based on the sequential and non-sequential algorithms. It was found that the retained indicators in both indices collided with the financial crises in 2008-2009. Overall, the proposed technique offers an effective approach to detect unknown locations, magnitudes, and structural break signs in a structural times series framework. Journal: Int. J. of Computational Economics and Econometrics Pages: 61-80 Issue: 1 Volume: 14 Year: 2024 Keywords: structural breaks; step-indicator saturation; SIS; Monte Carlo; model selection; state-space model; general-to-specific; GETS. File-URL: http://www.inderscience.com/link.php?id=135656 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:1:p:61-80 Template-Type: ReDIF-Article 1.0 Author-Name: Emiliano Alvarez Author-X-Name-First: Emiliano Author-X-Name-Last: Alvarez Title: A bibliometric survey of macroeconomic topics in agent-based models Abstract: In recent decades, the analysis of economies and their different sectors has intensified through simulations based on agent-based models (ABM). This is especially relevant for macroeconomics, since these methodologies allow us to analyse macroeconomic phenomena from actions and the interaction between individuals. In this article, a bibliometric analysis of ABMs in macroeconomics is briefly shown from the information gathered in the databases of the Web of Science (WOS) and Scopus. The main results of this work show that ABMs have analysed a wide spectrum of the most relevant topics in macroeconomics. There is a greater emphasis on credit crisis and financial instability, explained by the possibilities of this type of implementation to simulate network effects. These works are concentrated in a few research centres, mainly in Europe. In recent years, the agenda of topics to be addressed has grown, as well as the possibilities of a multidisciplinary agenda. Journal: Int. J. of Computational Economics and Econometrics Pages: 269-283 Issue: 3 Volume: 14 Year: 2024 Keywords: agent-based model; ABM; economics; macroeconomics; bibliometric analysis; complex system. File-URL: http://www.inderscience.com/link.php?id=139754 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:3:p:269-283 Template-Type: ReDIF-Article 1.0 Author-Name: Arturo Lorenzo-Valdes Author-X-Name-First: Arturo Author-X-Name-Last: Lorenzo-Valdes Title: American financial markets dependencies: a vine copula approach Abstract: Regular vine copulas are used to evaluate the dependence between American financial markets (Argentina, Brazil, Canada, Chile, Colombia, Mexico, Peru, and the USA) from August 16, 2011, to April 21, 2022. The behaviour of marginal distributions is described by AR(1)-TGARCH models with errors distributed as an asymmetric skewed Student's t, which are adequate to model returns and their volatility. The conditional dependency between pairwise countries is estimated for the covid period, and three subperiods are analysed, pre-covid, covid, and post-covid. It is found that the contagion routes between the different American countries have the USA as the root node. Journal: Int. J. of Computational Economics and Econometrics Pages: 81-97 Issue: 1 Volume: 14 Year: 2024 Keywords: vine copulas; TGARCH; dependence. File-URL: http://www.inderscience.com/link.php?id=135659 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:1:p:81-97 Template-Type: ReDIF-Article 1.0 Author-Name: Muhammadriyaj Faniband Author-X-Name-First: Muhammadriyaj Author-X-Name-Last: Faniband Author-Name: Pravin Jadhav Author-X-Name-First: Pravin Author-X-Name-Last: Jadhav Title: Determinants of government bond returns: an Indian experience Abstract: This paper examines the impact of macroeconomic factors and non-macroeconomic factors on government bond returns in India using quantile regression methodology and the monthly dataset from April 2010 to May 2022. This paper produces a new dataset of three government bonds indices which include the top 20 and top 5 bonds and Treasury Bills (T-Bill). We are the first to document the following results. First, the top 20 and top 5 traded bonds have less sensitivity to the exchange rates. Second, inflation has a negligible impact on the top 20 and T-Bills. Third, all three bonds are significantly affected by interest rates. Fourth, the effect of geopolitical risk is significant on T-Bills. Firth, economic policy uncertainty and volatility do not affect bond returns. Sixth, the Nifty has a significant positive impact on the top 20 and top 5 bonds. Our results are useful for investors, portfolio managers and policymakers. Journal: Int. J. of Computational Economics and Econometrics Pages: 251-268 Issue: 3 Volume: 14 Year: 2024 Keywords: macroeconomic; non-macroeconomic; government bond; bond returns; quantile regression; India. File-URL: http://www.inderscience.com/link.php?id=139755 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:3:p:251-268 Template-Type: ReDIF-Article 1.0 Author-Name: Dilip B. Madan Author-X-Name-First: Dilip B. Author-X-Name-Last: Madan Title: General financial economic equilibria Abstract: Uncertain demands and supplies, given prices, may not be equated to define an equilibrium. New concepts of equilibria are then formulated by modeling markets as an abstract agent absorbing the clearing risk. The new equilibria invoke the theory of acceptable risks to define a two-price equilibrium termed a general financial economic equilibrium (GFEE). The market sets two prices for each commodity, one at which it buys and the other at which it sells. The two prices are determined by targeting the aggregate random net inventory and net revenue exposures to be acceptable risks. The introduction of a two price labour market naturally leads to the concept of both an equilibrium unemployment rate and an equilibrium unemployment insurance rate. It is shown that the unemployment rate rises with the productivity of the economy and can be mitigated by expanding the number of products. Journal: Int. J. of Computational Economics and Econometrics Pages: 389-422 Issue: 4 Volume: 14 Year: 2024 Keywords: acceptable risks; distorted expectations; equilibrium unemployment; equilibrium unemployment insurance. File-URL: http://www.inderscience.com/link.php?id=142061 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:4:p:389-422 Template-Type: ReDIF-Article 1.0 Author-Name: Khalid Usman Author-X-Name-First: Khalid Author-X-Name-Last: Usman Title: The trade led-growth hypothesis in China and G8 countries: pooled mean group estimation Abstract: This research aims to examine the trade-led growth (TLG) hypothesis, especially the relationship between trade openness (TRA) and economic growth (GDP) in China and G8 (UK, Russia, Canada, USA, France, Italy, Germany, Japan) economies with two threshold variables, labour force (LF) and gross fixed capital formation (GFC). The study explores cointegration among these variables and evaluates their short and long-term effects utilising data from 1992 to 2021. Different tests, including CADF unit root, Westerlund panel cointegration, and pooled mean group estimation (PMG), are used while considering cross-section dependence (CD) and D-H tests. The PMG estimator identifies a positive long-term impact of GFC on GDP in both China and the G8 economies. Conversely, the D-H test exposes no causal relationship between GDP, labour force and gross fixed capital, and trade and gross fixed capital. These findings recommend that policymakers should prioritise trade development by spending on capital formation and labour production to improve economic growth. Furthermore, adopting amplified trade cooperation between China and G8 economies is suggested. Journal: Int. J. of Computational Economics and Econometrics Pages: 423-448 Issue: 4 Volume: 14 Year: 2024 Keywords: trade openness; economic growth; pooled mean group estimation; PMG; D-H test; trade-led growth hypothesis; China; G8 countries. File-URL: http://www.inderscience.com/link.php?id=142062 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:4:p:423-448 Template-Type: ReDIF-Article 1.0 Author-Name: Ahmed Wassal Elroukh Author-X-Name-First: Ahmed Wassal Author-X-Name-Last: Elroukh Title: Worker occupational skills and unemployment duration: a competing-risks econometric approach Abstract: This paper explains differences in unemployment duration among unemployed workers by differences in their skills, using the unemployed workers' previous occupation and education level to capture their skills. I use the cumulative incidence approach from the statistics literature, which is a better alternative to the standard survival econometric methods in cases of competing risks. In addition to showing that the standard survival econometric methods are biased, I find that the higher the unemployed worker is on the skill ladder based on their previous occupation, the faster their transition rate to a full-time job. An extra year of education has a positive effect on reducing unemployment duration. Those with a bachelor's degree tend to have the shortest employment duration among all unemployed individuals. However, the impact of education on transitioning from unemployment to a full-time job is less pronounced the higher the unemployed worker's previous occupation is on the skill ladder. Journal: Int. J. of Computational Economics and Econometrics Pages: 306-336 Issue: 3 Volume: 14 Year: 2024 Keywords: human capital; unemployment; competing risks; worker skills; duration analysis. File-URL: http://www.inderscience.com/link.php?id=139762 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:3:p:306-336 Template-Type: ReDIF-Article 1.0 Author-Name: Nurazlina Abdul Rashid Author-X-Name-First: Nurazlina Abdul Author-X-Name-Last: Rashid Author-Name: Mohd Tahir Ismail Author-X-Name-First: Mohd Tahir Author-X-Name-Last: Ismail Title: Nonlinear autoregressive with exogeneous input neural network time series model performance: bitcoin price prediction Abstract: There are over 10,000 listed cryptocurrencies, with bitcoin becoming the most used cryptocurrency at present. This research's aim is to establish the different dynamic time series architectures of nonlinear autoregressive having exogenous input (NARX) and nonlinear input output (NIO) to forecast the bitcoin price as well as compare their performance. Furthermore, this study attempts to combine the different number of inputs, hidden nodes, and time delay to assess the social media attribute (X) and bitcoin price (Y) past value impact in each model. The results show that all model architectures NARX and NIO with Levenberg-Marquardt backpropagation training algorithm have a significant relationship between inputs and output. This means social dominance, social volume, and weighted social sentiment have a relationship and effect on price except for model 3 with architecture NIO-1-5-1 (d = 1) and NIO 1-10-1 (d = 2). This research is significant because the results of this study will help traders and investors reduce risk and increase returns. Journal: Int. J. of Computational Economics and Econometrics Pages: 337-362 Issue: 3 Volume: 14 Year: 2024 Keywords: bitcoin; cryptocurrency; price prediction; nonlinear autoregressive with exogeneous input; NARX; neural network time series; dynamic nonlinear; social media; social dominance. File-URL: http://www.inderscience.com/link.php?id=139764 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:3:p:337-362 Template-Type: ReDIF-Article 1.0 Author-Name: Shuffield Seyram Asafo Author-X-Name-First: Shuffield Seyram Author-X-Name-Last: Asafo Author-Name: Luca Riccetti Author-X-Name-First: Luca Author-X-Name-Last: Riccetti Title: New transmission channels of ECB's unconventional monetary policies Abstract: Following the zero-lower bound on interest rates, many central banks turned to increasing the size of their balance sheet to achieve their mandate of price stability and low unemployment. Using the euro area as a case study, we investigate the macroeconomic effects and the transmission channels of the balance sheet expansion by way of a Bayesian VAR model. We identified the BVAR model by imposing sign restrictions on the impulse responses of control variables while leaving our variables of interest unconstrained. We find that the balance sheet expansion caused a higher increase in output than prices. These positive effects were transmitted through the wealth channel, the bank lending channel, and the fiscal channel. Journal: Int. J. of Computational Economics and Econometrics Pages: 284-305 Issue: 3 Volume: 14 Year: 2024 Keywords: Bayesian VAR; euro area; inflation expectations; unconventional monetary policy; commodity prices. File-URL: http://www.inderscience.com/link.php?id=139766 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:3:p:284-305 Template-Type: ReDIF-Article 1.0 Author-Name: Salaheddine El Omari Author-X-Name-First: Salaheddine El Author-X-Name-Last: Omari Author-Name: Noureddine Ben Lagha Author-X-Name-First: Noureddine Ben Author-X-Name-Last: Lagha Title: Fiscal policy and private investment: some anomalies from Saudi Arabia Abstract: This article examines the relationship between fiscal policy and private investment in Saudi Arabia, a country heavily dependent on government spending for its economy. Rather than solely analysing aggregate government expenditures, our study focuses on various government spending components, such as infrastructure, human resources, health and social development, economic resources, transport and communication, and municipal services. We employ the auto-regressive distributed lag (ARDL) model and an error-correction approach to assess the short-term and long-term impacts of these components on private investment. The empirical findings indicate that all components of public expenditures in Saudi Arabia have significant effects on private investment, in the long-run and/or the short-run, except spending on human resources. This lack of impact from expenditures on human resources development contradicts the theoretical prediction that such public spending stimulates labour productivity and encourages private investment. Journal: Int. J. of Computational Economics and Econometrics Pages: 449-467 Issue: 4 Volume: 14 Year: 2024 Keywords: government expenditures; fiscal policy; private investment; crowding-out effect; crowding-in effect; cointegration; error-correction model; Saudi Arabia. File-URL: http://www.inderscience.com/link.php?id=142075 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcome:v:14:y:2024:i:4:p:449-467