Forthcoming articles


International Journal of Computational Economics and Econometrics


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International Journal of Computational Economics and Econometrics (35 papers in press)


Regular Issues


  • Beyond Equilibrium: Revisiting Two-Sided Markets from an Agent-Based Modeling Perspective   Order a copy of this article
    by Torsten Heinrich, Claudius Grabner 
    Abstract: Two-sided markets are an important aspect of today's economies. Yet, the attention they have received in economic theory is limited, mainly due to methodological constraints of conventional approaches: two-sided markets quickly lead to non-trivial dynamics that would require a computational approach, as analytical models quickly become intractable. One approach to this problem is to opt for models that operate on an aggregated level, abstracting from most of the (micro-level) causes of these non-trivial dynamics. Here we revisit a well-known equilibrium model by Rochet and Tirole of two-sided markets that has taken this approach. Analyzing the model from an agent-based perspective, however, reveals several inconsistencies and implicit assumptions of the original model. This, together with the highly implausible assumptions that are required to make the model analytically tractable, limits its explanatory power significantly and motivates an alternative approach. The agent-based model we propose allows us to study the phenomenon of two-sided markets in a more realistic and adequate manner: Not only are we able to compare different decision making rules for the providers, we are also able to study situations with more than two providers. Thus, our model represents a first step towards a more realistic and policy-relevant study of two-sided markets.
    Keywords: two-sided markets; network externalities; agent-based modelling; simulation; heuristic decision making; reinforcement learning; satisīŦcing; differential evolution; evolutionary economics; market structure; IT economics; equilibrium dynamics.
    DOI: 10.1504/IJCEE.2019.10016573
  • Gender dimension of migration decisions in Ghana: The reinforcing role of anticipated welfare of climatic effect   Order a copy of this article
    by Franklin Amuakwa-Mensah, Victoria N. Sam, Evelyne Nyathira Kihiu 
    Abstract: The concept of migration has been a male phenomenon in time past, however, there has been a change in events as females are gradually gaining dominance in migration patterns in recent times. Using nationwide survey data this paper investigates the determinants of internal migration decisions for males and females in Ghana. We examined whether there is any significant differences in how climate elements together with anticipated welfare gains and socio-economic factors explain internal migration decision of males and females. We find some variations in the determinants of migration decisions for males and female, though these decisions are significantly affected by anticipated welfare gain, socio-economic factors and climate conditions. We observed that females respond more to climate or environmental elements than males. Moreover, the effect of climate on migration decisions for both males and females is reinforced by anticipated welfare gain.
    Keywords: Climate; environment; males; females; migration; Heckman two-stage; Ghana.

  • Insurance risk capital and risk aggregation: Bivariate copula approach   Order a copy of this article
    by Hanene Mejdoub, Mounira BEN ARAB 
    Abstract: The current paper discusses the risk aggregation issue using copula theory in the sphere of the insurance industry. In this context, a flexible modelling of the dependence structure for non-life insurance risks is considered. The aim of the modelling is to assess the impact of dependence structure among losses on the total risk capital estimation measured by the Value-at-Risk. Using numerical illustrations based on a Tunisian insurance company, we use first various copula families that can capture the dependencies across losses that are derived from four lines of business. To obtain the appropriate bivariate copula, we perform a goodness-of-fit analysis. Then, based on the Monte-Carlo simulation, the total risk capital is deduced by applying the Value at risk on the aggregate loss distributions. We also conduct a comparative analysis between the various types of the copulas. Our findings reveal that there is a regular impact on the capital requirement estimation indicating that a static approach ignoring the real dependencies between different risks can systematically lead to an overestimation of the total capital requirement.
    Keywords: Non-life insurance; Risk aggregation; Value-at-Risk; Dependence structure; Bivariate Copulas; Monte-Carlo Simulation.

  • Research Note: Futures Hedging with Stochastic Volatility: A New Method   Order a copy of this article
    by Moawia Alghalith, Christos Floros 
    Abstract: The aim of this paper is to present a continuous-time dynamic model of futures hedging. In particular, we extend the theoretical and empirical literature (e.g. Alghalith, 2016; Alghalith et al., 2015; and Corsi et al., 2008) in several important ways. First, we present a theory-based model. A significant empirical contribution is that we do not need data for the basis risk or the spot price. To the best of our knowledge, this is the first paper to assume that the volatility of futures price is stochastic and thus to estimate the volatility of volatility of futures price. Using daily futures data from the S&P500 index, we calculate an average daily volatility as well as the volatility of volatility of futures prices. We recommend that the managers of the futures market should report the stochastic volatility of the futures price (and its volatility), in addition to the traditional volatility.
    Keywords: stochastic volatility; volatility of volatility; futures; hedging.

  • Earnings Management to Avoid Losses and Earnings Declines in Croatia   Order a copy of this article
    by Stavros Degiannakis, George Giannopoulos, Salma Ibrahim, Ivana Rozic 
    Abstract: This paper provides empirical evidence that Croatian companies manage reported earnings to avoid losses and earnings declines. Specifically, we find that the cross-sectional distribution of scaled earnings and changes in earnings show high frequencies of small positive earnings and small increases in earnings while the frequencies of small losses and small decreases in earnings are less frequent. Furthermore, we demonstrate that these discontinuities are likely due to discretionary accruals. We examine the frequency distribution of reported earnings after removing discretionary accruals and find that the cross sectional distributions of non-discretionary scaled earnings shows lower frequencies of small positive earnings and higher frequencies of small negative earnings. Additionally, the cross sectional distribution of non-discretionary change in earnings demonstrates mixed frequencies of non-discretionary changes in earnings. Overall, this paper adds new empirical evidence to the benchmark-beating literature by demonstrating international evidence of earnings management around zero earnings and zero earnings changes benchmarks.
    Keywords: Earnings management; Earnings Declines; Earnings Losses; Discretionary Accruals; Earnings frequency distribution.

  • Stein-Rule Estimation in Genetic Carrier Testing   Order a copy of this article
    by Tong Zeng, Carter Hill 
    Abstract: In this paper, we apply the fully correlated random parameters logit (FCRPL) model to the genetic carrier testing data using shrinkage estimation. We show that shrinkage estimates with higher shrinkage constant improve the percentages of correct predicted choices by 2% and 10% respectively with Jewish and general population samples. The mean estimates of elasticity based on the shrinkage estimates are closer to those with the FCRPL model estimates and have smaller standard errors than the corresponding results based on the uncorrelated random parameters logit model estimates.
    Keywords: pretest estimator; positive-part Stein-like estimator; likelihood ratio test; random parameters logit model.
    DOI: 10.1504/IJCEE.2019.10016587
  • Efficiency in Banking: Does the Choice of Inputs and Outputs Matter?   Order a copy of this article
    by Christos Floros, Constantin Zopounidis, Christos Lemonakis, Alexandros Garefalakis 
    Abstract: This paper examines banking efficiency using recent data from PIGS countries (i.e.: Portugal, Italy, Greece and Spain) which suffer from debt problems. We employ a 2-stage approach based on the effect of several items of balance sheets on cash flows and DEA analysis. More specifically, we extend previous studies by giving attention to the deposit dilemma. The reported results show that the choice of inputs and outputs does matter in the case of European banking efficiency. Although the role of deposits is controversial, we find that deposits may be an output variable, due to liquidity issues that play a major role in the efficiency of PIGS banking sector. We also report that the DEA model with deposits as an output variable generates efficiency scores that fall between periods. These results are helpful to bank managers and financial analysts dealing with efficiency modelling.
    Keywords: PIGS; Banking sector; Efficiency; Deposits dilemma; 2-stage approach; Cash flows; DEA; regression.

  • Multi-period Mean-variance Portfolio Selection with Practical Constraints Using Heuristic Genetic Algorithms   Order a copy of this article
    by Yao-Tsung Chen, Hao-Qun Yang 
    Abstract: Since Markowitz proposed the meanvariance (MV) formulation in 1952, it has been used to configure various portfolio selection problems. However Markowitzs solution is only for a single period. Multi-period portfolio selection problems have been studied for a long time but most solutions depend on various forms of utility function, which are unfamiliar to general investors. Some works have formulated the problems as MV models and solved them analytically in closed form subject to certain assumptions. Unlike analytical solutions, genetic algorithms (GA) are more flexible because they can solve problems without restrictive assumptions. The purpose of this paper is to formulate multi-period portfolio selection problems as MV models and solve them by GA. To illustrate the generality of our algorithm, we implement a program by Microsoft Visual Studio to solve a multi-period portfolio selection problem for which there exists no general analytical solution.
    Keywords: Multi-period portfolio selection; Mean-variance formulation; Genetic algorithm; Transaction costs.

  • Using singular spectrum analysis for inference on seasonal time series with seasonal unit roots   Order a copy of this article
    by Dimitrios Thomalos, Hossein Hassani 
    Abstract: The problem of optimal linear filtering, smoothing and trend extraction for m-period differences of processes with a unit root is studied. Such processes arise naturally in economics and finance, in the form of rates of change (price inflation, economic growth, financial returns) and finding an appropriate smoother is thus of immediate practical interest. The filter and resulting smoother are based on the methodology of Singular Spectrum Analysis (SSA). An explicit representation for the asymptotic decomposition of the covariance matrix is obtained. The structure of the impulse and frequency response functions indicates that the optimal filter has a permanent" and a transitory component", with the corresponding smoother being the sum of two such components. Moreover, a particular form for the extrapolation coefficients that can be used in out-of-sample prediction is proposed. In addition, an explicit representation for the filtering weights in the context of SSA for an arbitrary covariance matrix is derived. This result allows one to examine the specific effects of smoothing in any situation. The theoretical results are illustrated using different data sets, namely U.S. inflation and real GDP growth.
    Keywords: Core inflation; Business cycles; Differences; Euro; Linear filtering; Trend extraction and prediction; Unit root.

  • Bias decomposition in the Value at Risk calculation by a GARCH(1,1)   Order a copy of this article
    by Gholam Reza K. Haddad 
    Abstract: The recent researches show that Value at Risk estimations are biased and is calculated conservatively. Bao and Ullah (2004) proved the bias of an ARCH(1) model for VaR can be decomposed in to two parts: bias due to return misspecification distributional assumption for GARCH(1,1) (Bias1) and bias due to estimation error (Bias2). Using quasi maximum likelihood estimation method this paper intends to find an analytical framework for the two source of biases. We generate returns from Normal and t-student distributions, then estimate the GARCH(1,1) under Normal and t-student assumptions. Our findings reveal that Bias1 equals to zero for the Normal likelihood function, but Bias2 0. Also, Bias1 and Bias2 are not zero for the t-student likelihood function as analytically were expected. However all the biases become modest, when the number of observations and degree of freedom is large.
    Keywords: Value-at-Risk; GARCH(1,1); Second-order bias.

  • Performance Evaluation of the Bayesian and classical Value at Risk models with circuit breakers set up   Order a copy of this article
    by Gholam Reza K. Haddad 
    Abstract: Circuit breakers, like price limits and trading suspensions, are used to reduce price volatility in security markets. When returns hit price limits or missed, observed returns deviate from equilibrium returns. This creates a challenge for predicting stock returns and modeling Value at Risk (VaR). In Tehran Stock Exchange (TSE), the circuit breakers are applied to control for the excess price volatilities. We extend Weis (2002) model, in the framework of Bayesian Censored and Missing-GARCH approach, to estimate VaR for Iran Khodro Company (IKCO) share in TSE. Using daily data for the period of June 2006 to June 2016, we show that the Censored and missing- GARCH model with t-student distribution outperforms. Kullback-Leibler (KLIC), Kupic (1995) test and Lopez score (1998) outcomes show that estimated VaR by Censored and missing- GARCH model with t-student distribution is of the most accuracy among all other classical and Bayesian estimation models.
    Keywords: Circuit Breakers; Censored and Missing–GARCH; Bayesian estimation; Value at Risk; Ranking Models.

  • Stages and determinants of European Union Small and Medium Sized firms failure process   Order a copy of this article
    by Alexios Makropoulos, Charlie Weir 
    Abstract: This paper uses a combination of Factor and Cluster analysis to identify and compare failure processes in Small and Medium sized firms from a number of European Union countries. Panel data analysis is then used to identify the determinants of the firms transition from financial health towards liquidation in the alternative failure processes. The results suggest that there are 4 different firm failure processes. We find that financial performance and director characteristics differ between firm failure processes. We also find that the economic environment, the legal tradition of countries and excessive firm growth are determinants of the transition of firms towards liquidation across most firm failure processes. These findings may be of practical use to policy makers, lenders and risk managers who will benefit from a better understanding of the differences between the alternative firm failure processes and from the determinants of a firms transition towards liquidation within these failure processes.
    Keywords: SME failure; firm failure; firm failure process; factor/cluster analysis; ordered random effects regression; failure status transition.

  • Abnormal returns and systemic risk: evidence from a non-parametric bootstrap framework during the European sovereign debt crisis.   Order a copy of this article
    by Konstantinos Gkillas, Christos Floros, Christoforos Konstantatos, Dimitrios I. Vortelinos 
    Abstract: We investigate the impact of European Central Bank (ECB) interventions on major European and Turkish stock and credit default swap (CDS) markets highlighting the importance of abnormal to excess abnormal returns in the systemic risk. In particular, we examine the impact of ECB announcements (news) on major European and Turkish financial markets (stocks and CDSs indices) for a high and low-volatility period, i.e. from November 6th, 2008 to December 31st, 2015. We also examine the market efficiency by using both an event study methodology and the Capital Asset Pricing Model. Moreover, the impact of the ECB events is measured by an event study and a systemic risk analysis. The results show that investors exposed to Finland, Sweden, Austria and Spain tend to be more vulnerable to risk and volatility, when ECB announcements are published.
    Keywords: abnormal returns; bootstrap; ECB events; financial crises;.

  • An analysis of major Moroccan domestic sectors interdependencies and volatility spillovers using Multivariate GARCH models.   Order a copy of this article
    by Ouael EL JEBARI, Abdelati HAKMAOUI 
    Abstract: This paper tries to give a thorough analysis of the mechanisms of volatility spillovers, as well as, a study of the time-varying interdependencies of volatilities of seven major sectors of the Moroccan stock exchange by proposing an empirical approach based on multivariate GARCH models. It uses daily data spanning the period between 02/07/2007 and 15/12/2016, covering seven principal sectors indices. The results of the study confirm the existence of multiple volatility transmissions in both ways and of both signs between sectors of our sample, along with, the quasi-abundance of positive correlations suggesting possible contagion effects. More importantly, our findings are in line with those discovered in the U.S financial market. The notoriety of this article resides in the fact that it broadens previously documented studies focusing mainly on external shocks by providing a study of internal shocks while applying two multivariate GARCH models.
    Keywords: Volatility spillover; dynamic conditional correlations; interdependencies; domestic sectors; multivariate GARCH models.

  • A note on the use of the Box-Cox Transformation for Financial Data
    by Dimitrios Kartsonakis Mademlis, Nikolaos Dritsakis 
    Abstract: This paper tests whether the Box-Cox transformation reduces the problem of non-normality in financial data.
    Keywords: ARIMA models; Box-Cox transformation; Box-Jenkins methodology; normality; stock market; oil prices

Special Issue on: ICOAE2015 Applied Economics

  • Reconsidering the relationship between foreign direct investment and growth   Order a copy of this article
    by Carlos Encinas-Ferrer, Eddie Villegas-Zermeño 
    Abstract: It has been assumed that foreign direct investment (FDI) is a variable that explains economic growth (EG). As investment (I) is the dynamic element of gross domestic product (GDP), therefore, FDI, as part of total investment, should be also the independent variable and GDP growth the dependent one. However, many studies in many countries have shown the contrary, there is not such a causal relationship between FDI and GDP. In our investigation, we include the study of the cases of Mexico, China, Brazil and the Republic of Korea. It is our hypothesis that there is not a causal relationship between FDI, as the independent variable, and GDP grow as the dependent one in the selected countries and that this is in part because FDI is a small proportion of total (national and foreign) direct investment and so its impact is reduced.
    Keywords: FDI; foreign direct investment; GDP; gross domestic product; economic growth; gross fixed capital formation.
    DOI: 10.1504/IJCEE.2019.10011558

Special Issue on: Dynamics of and on Networks

  • Growth and collapse: An agent-based banking model of endogenous leverage cycles and financial contagion   Order a copy of this article
    by Robert De Caux, Frank McGroarty, Markus Brede 
    Abstract: We create an agent-based banking model that allows the simulation of leverage cycles and financial contagion. Banks within our model adapt their investment strategies in an evolutionary manner according to the success of their competitors, creating an endogenous interbank loan network and a dynamic asset market as they try to maximise profit by adjusting their leverage. The system exhibits periods of slow risk growth and fast insolvency cascades, allowing us to assess both the size and frequency of those cascades over a long time-frame. We demonstrate that banks endogenise systemic risk into their leverage behaviour when the asset market is subject to either low or high levels of volatility, but are less successful for medium volatility when the number of bank insolvencies is maximised. We also show that in a low volatility environment, banks are more susceptible to systemic contagion. While the majority of insolvencies occur through the asset side of the balance sheet due to fire sales causing a rapid depreciation in the asset price, failures through the liability side of the balance sheet tend to be correlated, acting as an amplification mechanism to create far more serious cascades. By creating realistic cycles of growth and collapse, the model provides a suitable framework for performing further policy tests.
    Keywords: financial contagion; agent-based model; bank insolvency; evolutionary game theory; simulation; networks.

  • Structural Characteristics of Knowledge Exchange in Innovation Networks   Order a copy of this article
    by Michael Rothgang, Bernhard Lageman 
    Abstract: While there is a large pool of assured knowledge about various general dimensions of the structure and dynamics of innovation networks, there are several basic structural features that deserve more attention in future network research. Our contribution concentrates on three key structural characteristics: (i) the type of actors, (ii) contexts and (inter-) organiza-tional environments of networks and (iii) modes and contents of knowledge exchange. We discuss the role of these structural characteristics by using research material from an ac-companying evaluation of the German Leading-Edge Clusters Competition. Major results are: Innovation network actors present themselves in a highly heterogeneous manner; the relevant spectrum stretches from highly complex organizations to single individuals. Net-works are embedded in different sectoral, organizational and situational contexts. In many cases, even defining the boundaries of the relevant network is a challenge for innovation researchers. Knowledge exchange flows through different channels, and can be seen as a context-dependent combination of formal and tacit elements, as well as an interplay of both (relative) openness and restraint between the acting individuals and organizations.
    Keywords: Innovation Networks; Knowledge Exchange; Cluster Organization; Cluster Policy.

  • Endogenous Dynamics of Innovation Networks in the German Automotive Industry: Analyzing Structural Network Evolution using a Stochastic Actor-Oriented Approach   Order a copy of this article
    by Daniel Hain, Tobias Buchmann, Muhamed Kudic, Matthias Müller 
    Abstract: The generation of innovation is well known to be a social process depending on mutual interactions, aiming at accessing and exchanging knowledge in order to generate novel goods and services. Accordingly, interest in interfirm innovation networks has increased sharply over the last decade. Preceding research indicates that the structural dynamics of networks is driven both by endogenous and exogenous forces. In particular, we focus on the role of the endogenous determinants of the network evolution of interfirm networks a category of often underestimated forces. We employ a longitudinal dataset that comprises German automotive firms performance between 2002 and 2006 and apply a stochastic actor-oriented model (SAOM) designed to analyze both the endogenous and exogenous determinants of network change. Our results show that endogenous determinants approximated by measures for local and global clustering exhibit greater explanatory power than exogenous firm characteristics such as age, size, and R&D activity.
    Keywords: Network evolution; network endogeneity; innovation networks; automotive industry; stochastic actor-oriented approach.

  • How to find a needle in a haystack? A theory-driven approach to social network analysis of regional energy transitions   Order a copy of this article
    by Andre Schaffrin, Tanja Nietgen, Benjamin Schrempf 
    Abstract: Social network analysis bears great potential for the study of complex social transition processes such as regional energy transitions. We know that there is substantial influence of local renewable energy projects on a wider process of structural, institutional, and social transitions throughout larger regions. The complexity of the social processes results from a lack of empirical research, that utilizes social network analysis. With this complexity come the difficulties in collecting relevant and sufficient empirical data on socio-technical levels and alongside varying phases of development across a selection of renewable energy projects. In this paper, we propose a theory-driven, conceptual framework to select relevant cases, interviewees, and influencing factors for the empirical analysis of complex network dynamics. We base our conceptualization on mainstream literature on socio-technical levels, phase-models of innovation processes, and social, economic, and political factors influencing the local and regional energy transition. To demonstrate the usefulness of our approach, we apply the case of a local and regional energy transition in a German county. We argue that the conceptual framework is a crucial step and serves as a guiding system to a more thorough analysis of social network dynamics within complex social transition processes. The conceptual framework allows formulating hypotheses about different pathways and network dynamics, for the selection of relevant cases, and as a guideline for the collection of data.
    Keywords: energy transition; network dynamics; socio-technical multilevel perspective; empirical data collection.

  • Geographical dynamics of knowledge flows. Descriptive statistics on inventor network distance and patent citation graphs in the pharmaceutical industry.   Order a copy of this article
    by Ben Vermeulen 
    Abstract: In the knowledge-based geography of innovation literature, there are two opposing claims on the spatio-temporal pattern in knowledge flows over the course of a technological trajectory. The first claim is that, after the breakthrough, externalities stimulate agglomeration of specialized firms and thus cause the incremental inventions, extensions, and adaptations to take place progressively localized. The second claim is that, after the breakthrough, progressive codification and technological crystallization facilitates absorption and collaboration over greater distances. In this study, forward citation graphs of breakthrough patents are constructed and enriched with the NUTS3/ TL3 locations of the inventors. These forward citation graphs are subsequently used to study these claims and several more specific claims on co-inventor network distances and distances of groups of inventors across patent citations. Apart from obtaining support for existing claims, the study also reveals several distinct, more complex spatio-temporal patterns. Notably, it is found that, early on in technological trajectories, inventors generally collaborate mostly locally, yet cite knowledge sources found more remotely. Later on in technological trajectories, inventors collaborate over greater distances, yet cite more local knowledge sources. Conclusively, there is progressive globalization of inventor networks, whereby knowledge sources are used increasingly locally in follow-up inventions.
    Keywords: inventor network; forward citation graph; patent analysis; geographical dynamics; spatial analysis; knowledge-based; geography of innovation; descriptive statistics.

  • Innovation cooperation in East and West Germany: A study on regional and technological impact   Order a copy of this article
    by Uwe Cantner, Alexander Giebler, Jutta Günther, Maria Kristalova, Andreas Meder 
    Abstract: In this paper we investigate the impact of regional and technological innovation systems on innovation cooperation. We develop an indicator applicable to regions, which demonstrates the relative regional impact on innovation cooperation. Applying this method to German patent data, we find that regional differences in the degree of innovation cooperation do not only depend on the technology structure of a region but also on specific regional effects. High-tech oriented regions, whether east or west, are not automatically highly cooperative regions. East German regions have experienced a dynamic development of innovation cooperation since re-unification in 1990. Their cooperation intensity remains higher than in West German regions.
    Keywords: regional innovation system; technological innovation system; innovation cooperation; Germany.

  • Knowledge diffusion in formal networks The roles of degree distribution and cognitive distance   Order a copy of this article
    by Kristina Bogner, Matthias Mueller, Michael Schlaile 
    Abstract: Social networks provide a natural infrastructure for knowledge creation and exchange. In this paper we study the effects of a skewed degree distribution within formal networks on knowledge exchange and diffusion processes. To investigate how the structure of networks affects diffusion performance, we use an agent-based simulation model of four theoretical networks as well as an empirical network. Our results indicate an interesting effect: neither path length nor clustering coefficient are the decisive factors determining diffusion performance but the skewness of the link distribution is. Building on the concept of cognitive distance, our model shows that even in networks where knowledge can diffuse freely, poorly connected nodes are excluded from a joint learning in networks.
    Keywords: agent-based simulation; cognitive distance; degree distribution; direct project funding; Foerderkatalog; German energy sector; innovation networks; knowledge diffusion; publicly funded R&D projects; random networks; scale-free networks; simulation of empirical networks; skewness; small-world networks.

Special Issue on: ICOAE2016 Applied Economics

  • Overvaluation in a non-optimal currency area   Order a copy of this article
    by Carlos Encinas-Ferrer 
    Abstract: The devaluation tool in an optimal currency area with monetary sovereignty has a significant importance in determining economic policies to adjust relative costs and interest rates to the situation faced by a country in front of economic shocks, either asymmetric or generalized. Devaluation risk is due not only to domestic inflation but to its relationship with that of its major trading partners. If inflation of a nation is greater than the average one of its trading partners and the gap between them is not adjusted by the depreciation of its currency, it will start a process of overvaluation. This overvaluation ends manifesting itself by a growing lack of competitiveness in its foreign trade showed by trade deficit, reduced gross domestic product (GDP) and rising unemployment. Devaluation or depreciation would restore the competitiveness of the productive apparatus. However, in a non-optimal currency area -as a country unilaterally dollarized- this adjustment may be made by abandoning the anchor coin and adopting a new national currency, what it has been called remonetization (Encinas-Ferrer 2003-1 and 2) but the Eurozone experience from 2011 shows us that abandoning a non- optimal currency area and stablishing a new national currency is a very difficult decision that no one has dared to take until now.
    Keywords: overvaluation; optimal currency areas; non-optimal currency areas; euro-zone.

  • Causality among CO2 Emissions, Energy Consumption and Economic Growth in Italy.   Order a copy of this article
    by Pavlos Stamatiou, Nikolaos Dritsakis 
    Abstract: The aim of this paper is to investigate the relationship between CO2 emissions (carbon dioxide emissions), energy consumption and economic growth in Italy, using annual data covering the period 1960-2011. The unit root tests results indicated that the variables are not stationary in levels but in their first differences. Subsequently, the Johansen cointegration test showed that there is a cointegrated vector between the examined variables. The Vector Error Correction Model (VECM) is used in order to find the causality relations among the variables. The empirical results of the study revealed that both in the short and long run there is a strong unidirectional causality relation between economic growth and CO2 emissions with direction from economic growth to CO2 emissions. Finally, the impulse response functions indicated that a reduction in CO2 emissions has a positive effect on energy consumption, while it causes a decrease in economic growth.
    Keywords: Carbon emissions; Energy consumption; Economic Growth; Cointegration Test; Vector Error Correction; Causality; Variance Decomposition; Impulse response analysis; Italy.

  • The Public Sector Wage Premium Puzzle   Order a copy of this article
    by Yi Wang, Peng Zhou 
    Abstract: This paper investigates the public sector wage premium in the UK over the first decade of the 21st century using both econometric and economic modelling methods. A comprehensive literature review is conducted to summarise the four popular types of methods adopted by the traditional microeconometric studies. Application of these methods results in an estimated public sector wage premium equal to 6.5%. Indirect inference is then introduced as a new method of testing and estimating a microfounded economic model. All four types of econometric methods can be used as auxiliary models to summarise the data features, based on which the distance between the actual data and the model-simulated data is assessed. The selection bias can also be tested in a straightforward way under indirect inference.
    Keywords: Public Sector Wage Premium; Microfoundation; Propensity Score Matching; Indirect Inference.

  • Modelling Agricultural Risk in a Large Scale Positive Mathematical Programming Model   Order a copy of this article
    by Ivan Arribas, Kamel Louhichi, Angel Perni, Jose Vila, Sergio Gomez-y-Paloma 
    Abstract: Mathematical programming has been extensively used to account for risk in farmers' decision making. The recent development of the Positive Mathematical Programming (PMP) has renewed the need to incorporate risk in a more robust and flexible way. Most of the existing PMP-risk models have been tested at farm-type level and for a very limited sample of farms. This paper presents and tests a novel methodology for modelling risk at individual farm level in a large scale model, called IFM-CAP (Individual Farm Model for Common Agricultural Policy analysis). Results show a clear trade-off between including and excluding the risk specification. Albeit both alternatives provide very close estimates, simulation results shows that the explicit inclusion of risk in the model allows isolating risk effects on farmer behaviour. However, this specification increases three times the computation time required for estimation.
    Keywords: agriculture; positive mathematical programming; risk and uncertainty; expected utility; Highest Posterior Density; European Common Agricultural Policy.

  • Depth, tightness, and resiliency as market liquidity dimensions: evidence from the Polish stock market   Order a copy of this article
    by Joanna Olbrys, Michal Mursztyn 
    Abstract: Liquidity in a financial market is not a one-dimensional variable but it includes several dimensions. The main aim of the paper is an empirical analysis of market liquidity dimensions on the Warsaw Stock Exchange (WSE). We investigate market depth, market tightness, and market resiliency for fifty-three WSE-listed companies divided into three size groups. The high-frequency data covers the period from January 3, 2005 to June 30, 2015. The additional goal is a robustness analysis of the obtained results with respect to the whole sample period and three adjacent subsamples, each of equal size: pre-crisis, crisis, and post-crisis periods. Order ratio (OR) is employed as a proxy of market depth. Market tightness is approximated by using relative spread (RS). Market resiliency is estimated by utilizing realized spread (RealS) which is a temporary component of bid/ask effective spread. As we expected, the empirical results indicate that OR values do not depend on a firm size, while RS estimations are slightly higher for small companies. RealS proxy values are positive for almost all stocks, except for isolated cases. Moreover, the results turn out to be robust to the choice of the sample for all groups of assets.
    Keywords: dimensions of market liquidity; market depth; market tightness; market resiliency; Global Financial Crisis; Polish stock market.

Special Issue on: Coping with Uncertainty in Complex Socio-Economic Systems

  • Technology Diffusion of Industry 4.0: An Agent-Based Approach   Order a copy of this article
    by Martin Prause, Christina Gunther 
    Abstract: Governmental interventions, such as public policies and programs, play a vital role in innovation diffusion, particularly if the application area is heterogeneous, like the German federal high-tech approach of Industry 4.0. Interventions can thus inhibit market failure and negative externalities or disseminate the technology and promote positive externalities. To analyze the impact of governmental intervention, considering the particularities of the Industry 4.0 approach, an agent-based model is proposed, particularly to test the sensitivity of Industry 4.0 innovation diffusion speed and degree due to interventions such as promotion, educational support, technology networks (hubs), technology standardization, and financial aid among manufacturing SMEs in Germany. This article describes a conceptual model structured along the overview, design concept, and details framework. Grounding and calibration of input parameters and agent behavior are based on firm characteristics and adoption determinants (technology-organization-environment model) from survey data and Industry 4.0 case studies.
    Keywords: Agent-based Model; Industry 4.0; Innovation Diffusion; SME; Technology Adoption.

  • Forecasting Inflation in Tunisia during Instability: Using Dynamic Factors Model A two-step based procedure based on Kalman Fitler   Order a copy of this article
    Abstract: This work presents a forecasting inflation model using a monthly database. Conventional models that forecast inflation use a few macroeconomic variables. In the context of globalization and dependent economic world, models have to take into account a large amount of information. This model is the goal of recent research in various industrialized countries as well as developing ones. With the Dynamic Factors Model (DFM), the forecast values are closer to the actual inflation than those obtained from the conventional models in the short term. In our research, we devise the inflation into free and administered inflation and test the performance of the DFM under instability (before and after the revolution) in different types of inflation and trend inflation, namely administered and free inflation. Knowing that periods of instability are simultaneously the period of price liberalization of basic goods (2008) and the post-revolution period (2011-2017). We have found that the DFM with an instability factor leads to substantial forecasting improvements over the DFM without an instability factor in the period after the revolution.
    Keywords: Inflation forecasting; PCA; VAR; Dynamic Factors Model; Kalman Filter; Space-state; Instability factor.

  • Measuring Uncertainties: a Theoretical Approach   Order a copy of this article
    by Carolina Facioni, Isabella Corazziari, Filomena Maggino 
    Abstract: When our aim is to draw the possible developments of future events, we are faced with a practical obstacle. Indeed, we cannot have any empirical experience of the future. Have we, therefore, to be inferred that forecasting, exploring future or, better: exploring futures, or anticipating futures have not to be considered activities of a scientific kind? Answer to such a difficult question requires a multidisciplinary approach, where statistical models, methodology of social science and of course statistics and sociology as a whole are enhanced in their ability to express the change and sometimes the risk that the change itself implies. A great help in understanding complexity, and trends, comes from a method for multi-way data, based on the joint application of a factorial analysis and regression over time, called dynamic factor analysis (DFA)
    Keywords: uncertainty measure; futures studies; DFA; dynamic factor analysis.
    DOI: 10.1504/IJCEE.2018.10011864
  • Career mobility of PhD holders in Social Sciences and Humanities: evidences from the POCARIM project   Order a copy of this article
    by Lucio Morettini, Emilia Primeri, Emanuela Reale, Antonio Zinilli 
    Abstract: The paper aims at investigating factors that could affect the likelihood of changing job of PhD holders in the fields of social sciences and humanities (SSH). We use data collected through a survey developed within the POCARIM project (funded by the EC under the EUFP7) in order to analyse variations in PhDs\' career paths in a longitudinal dimension: we consider the career of each agent as a whole and investigate what elements related to individual features can influence careers entropy in term of changes of sector and/or country.
    Keywords: Career mobility; PhD; Job market; Higher education; Career paths.

  • Do the Flexible Employment Arrangements Increase Job Satisfaction and Employee Loyalty? Evidence from Bayesian Networks and Instrumental Variables   Order a copy of this article
    by Eleftherios Giovanis 
    Abstract: This study explores the relationship between job satisfaction, employee loyalty and two types of flexible employment arrangements; teleworking and flexible timing. The analysis relies on data derived by the Workplace Employee Relations Survey (WERS) in 2004 and 2011. We apply the propensity score matching approach and least squares regressions. Furthermore, we employ the Bayesian Networks (BN) and Directed Acyclic Graphs (DAGs) to confirm the causality between employment types explored and the outcomes of interest. Additionally, we propose an instrumental variables (IV) approach based on the BN framework. The results support that a positive causal effect from these employment arrangements on job satisfaction and employee loyalty is present.
    Keywords: Bayesian Networks; Directed Acyclic Graphs; Employee Loyalty; Employment Arrangements; Flexible Timing; Job Satisfaction; Teleworking; Workplace Employment Relations Survey.

  • On the Validity of Exclusion Restrictions in the Structural Multivariate Framework: a Monte Carlo Simulation   Order a copy of this article
    by Talel Boufateh 
    Abstract: This paper aims to examine the validity of identifying restrictions used in the Structural multivariate models. Whether we are under short-term identification approach and / or long term identification one, the scheme adopted implies the imposition of additional assumptions which usually take the form as exclusion restrictions. We believe however, that the value of a restriction is not necessarily equal to zero even if it expresses the lack of impact of a shock on a variable. We think that this lack of impact may reflect an effect asymptotically equal to zero and that the little nuance could be amplified with the model dynamics and affect the structural analysis. We have chosen to study this problem by using a Monte Carlo simulation and to examine the consequences of slipping of the identification restriction value. The results that emerge from this work confirm the sensitivity of variables' responses to change, even the slightest it may be, in the value of identification restrictions. Whatever the strength and elegance of the theory and the economic reasoning from which emanate the exclusion restrictions, precision measurements should be considered.
    Keywords: Exclusion restrictions; SVAR approach; Monte Carlo Simulation.

Special Issue on: ICOAE2017 Applied Economics

  • An Analysis of Long-Run Relationship between ICT Sectors and Economic Growth: Evidence from ASEAN Countries   Order a copy of this article
    by Chukiat Chaiboonsri, Satawat Wannapan 
    Abstract: This paper is proposed to investigate the causal panel relationship between information and communication technology (ICTs) segments and economic expansionary rates in ASEAN countries. Methodologically, the panel time-series data observed during 2006 to 2016 is employed to estimate the panel Granger Causality test. According to the technical problem of lag selection for the panel causal analysis, the computationally statistical approach called Newtons optimization method is helpfully applied to verify the suitable lag selection. The empirical results found that ICTs are not the major factor that causally motivates economic growth in ASEAN. This is confirmed by the extended section of the Autoregressive Distributed Lag (ARDL) cointegration approach, which is based on Bayesian statistics combining with the simulation method called Markov Chain Monte Carlo (MCMC). The results state Thailand is the only one among eight selected countries in ASEAN contained the long-run relationship between ICTs and GDP. This can be strongly concluded that the ICT sectors are not sustainable for driving economic growth in ASEAN. To address the issue, equitable educational systems and advanced infrastructural developments are the primary that should be corporately implemented.
    Keywords: ICT segments ;economic growth ;long-run relationship ;ASEAN countries;Bayesian approach.