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This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.

This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.

This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.

This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.

The purpose of this paper is to introduce an empirical model for house price spillovers between real estate markets. The model is presented by using data from the US-UK and London-New York housing markets over a period of 1975Q1-2016Q1 by employing both static and dynamic methodologies.

The research analyzes long-run static and dynamic spillover elasticity coefficients by employing three methods, namely, autoregressive distributed lag, the fully modified ordinary least square and dynamic ordinary least squares estimator under a Kalman filter approach. The empirical method also investigates dynamic correlation between the house prices by employing the dynamic control correlation method.

The paper shows how a dynamic spillover pricing analysis can be applied between real estate markets. On the empirical side, the results show that country-level causality in housing prices is running from the USA to UK, whereas city-level causality is running from London to New York. The model outcomes suggest that real estate portfolios involving US and UK assets require a dynamic risk management approach.

One of the findings is that the dynamic conditional correlation between the US and the UK housing prices is broken during the crisis period. The paper does not discuss the reasons for that break, which requires further empirical tests by applying Markov switching regime shifts. The timing of the causality between the house prices is not empirically tested. It can be examined empirically by applying methods such as wavelets.

The authors observed a unidirectional causality from London to New York house prices, which is opposite to the aggregate country-level causality direction. This supports London’s specific power in the real estate markets. London has a leading role in the global urban economies residential housing markets and the behavior of its housing prices has a statistically significant causality impact on the house prices of New York City.

The house price co-integration observed in this research at both country and city levels should be interpreted as a continuity of real estate and financial integration in practice.

The paper is the first research which applies a dynamic spillover analysis to examine the causality between housing prices in real estate markets. It also provides a long-term empirical evidence for a dynamic causal relationship for the global housing markets.

This paper attempts to develop a simple, static model of tax administration that is capable of explaining the widespread collusive petty tax administration corruption observed in developing countries.

This paper utilizes a positivist research framework and adopts a theoretical method of analysis, although secondary data will also be mentioned to support theoretical arguments whenever it is appropriate to do so.

A high rate of collusive tax corruption is inevitable in developing countries.

The model is static and needs to be extended into a dynamic model.

Traditional enforcement tools such as higher audits or a higher penalty regime against tax evasion do not work. Tax simplification can lessen the incidence of tax corruption.

Fighting tax corruption requires significant changes in the attitudes of taxpayers and tax auditors.

This paper combines the literature on Kantian economics and tax compliance in an innovative fashion.

Exchange rate uncertainty leads to an indecisive environment for imports and exports that would condense international trade, foreign direct investment, trade earnings, trade volumes, economic growth and welfare. This study aims to examine, empirically, the effect of exchange rate uncertainty on bilateral trade performance, focusing on eight SAARC member economies using the popular modified gravity model of trade.

The paper includes eight SAARC members – Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, Pakistan and Sri Lanka panel data set over the period 2005–2018. The authors consider both standardized value (standard deviation) and conditional variance model to determine volatility of exchange rate. Primarily, ordinary least squares, random effects and fixed effects estimation techniques are employed to investigate the impact of exchange rate volatility. Endogeneity and robustness of the findings have been tested using the simultaneity-adjusted model and dynamic panel data two-step system GMM estimation techniques.

Empirical findings endorse the view that exchange rate volatility lowers trade flows in the SAARC regions. However, this adverse effect of exchange rate uncertainty on trade is pretty small. The negative correlation between exchange rate volatility and bilateral trade remains consistent and significant after controlling of simultaneous causality, autocorrelation, year effects, country-pair heterogeneity and endogeneity irrespective of panel data estimation techniques and different measures of volatility.

The present paper is original work.

This paper examines the impact of regional and non-regional trade on economic growth using annual data from Economic Community of West African States (ECOWAS) member countries for the period 2007 to 2017.

Trade data were decomposed into regional (trade among ECOWAS Member States) and non-regional (trade between ECOWAS Member States and the rest of the world). We used the dynamic system GMM to estimate the models and introduced exchange rate, unemployment rate, population growth and gross capital formation as controlled variables.

The results revealed that the estimated coefficient of ECOWAS regional trade is statistically significant and positive in predicting growth, while the non-regional trade coefficient is negative and not statistically significant in predicting growth. Other predictors of growth introduced into the model as controlled variables, such as exchange rate, unemployment rate, population growth and gross capital formation, displayed mixed results. More importantly, population growth, unemployment and exchange rate depreciation hurt economic growth, while gross capital formation promotes economic growth.

The findings provide strong support in favour of the Krugman (1991) hypothesis that regional trade agreements (RTAs) are a better alternative to global trade.

Our decision to disaggregate ECOWAS trade is unique and influenced largely by the objective of the study, which is to establish the type of ECOWAS trade that is a good predictor of growth. The evidence from our findings support the theory that RTAs are a better catalyst to economic growth.

Logistical facility location decisions can make a crucial difference in the success or failure of a company. Geographical Information Systems (GIS) have recently become a very popular decision support system to help deal with facility location problems. However, until recently, GIS methodologies have not been fully embraced as a way to deal with new facility location problems in business logistics. This research makes a framework for categorizing empirical facility location problems based on the intensity of the involvement of GIS methodologies in decision making. This framework was built by analyzing facility location models and GIS methodologies. The research results revealed the depth of the embracement of GIS methodologies in logistics for determining new facility location decisions. In the new facility location decisions, spatial data inputs are almost always coupled with the visualization of the problems and solutions. However, the usage of GIS capability solely (i.e. suitability analysis) for problem solving has not been embraced at the same level. In most cases, the suitability analysis is used together with special optimization models for choosing among the multiple alternatives.

This paper aims to explore the extreme effect of crude oil price fluctuations and its volatility on the economic growth of Middle East and North Africa (MENA) countries. It also investigates the asymmetric and dynamic relationship between oil price and economic growth. Further, a separate analysis for each MENA oil-export and oil-import countries is conducted. Furthermore, it studies to what extent the quality of institutions will change the effect of oil price fluctuations on economic growth.

As the effect of oil price fluctuations is not the same over different business cycles or oil price levels, the paper uses a panel quantile regression approach with other linear models such as fixed effects, random effects and panel generalized method of moments. The panel quantile methodology is an extension of traditional linear models and it has the advantage of exploring the relationship over the different quantiles of the whole distribution.

The paper can summarize results as following: changes in oil price and its volatility have an opposite effect for each oil-export and oil-import countries; for the former, changes in oil prices have a positive impact but the volatility a negative effect. While for the latter, changes in oil prices have a negative effect but volatility a positive effect. Further, the impact of oil price changes and their uncertainty are different across different quantiles. Furthermore, there is evidence about the asymmetric effect of the oil price changes on economic growth. Finally, accounting for institutional quality led to a reduction in the impact of oil price changes on economic growth.

The study concludes more detailed results on the impact of oil prices on gross domestic product growth. Thus, it can be used as a decision-support tool for policymakers.