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Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

The objective of this chapter is to introduce the reader to Spatial Health Econometrics (SHE). In both micro and macro health economics there are phenomena that are characterised by a strong spatial dimension, from hospitals engaging in local competitions in the delivery of health care services, to the regional concentration of health risk factors and needs. SHE allows health economists to incorporate these spatial effects using simple econometric models that take into account these spillover effects. This improves our understanding of issues such as hospital quality, efficiency and productivity and the sustainability of health expenditure of regional and national health care systems, to mention a few.

The purpose of this paper is to analyze the influence of different forms of RMB foreign exchange rates on Chinese foreign trade.

This paper constructed spatial panel model and Markov Chain Monte Carlo estimation method and collected the data of 25 countries’ (including China) quarterly macroeconomic data from first quarter of 1993 until third quarter of 2013 to conduct the data analysis.

This paper finds that USD/CNY, which is widely used in trade settlement, is more significant in effecting Chinese export. Totally, 1 percent appreciation of CNY against USD will lead to 1.532 percent decline of Chinese export, while 1 percent appreciation of CNY NEER only 0.42 percent. What is more, 1 percent increases of the volatility of USD/CNY results in 0.579 percent decline of Chinese export. As policy suggestions, we should further reform the foreign exchange derivative market in China, and provide more currency derivatives, so that the ability of Chinese economy to deal with foreign exchange risk could be improved.

Effect of exchange rate on imports and exports relates to the future direction of China’s exchange rate policy. This paper claims that China should accelerate the construction of foreign exchange derivatives market, improving the ability to respond quickly to foreign currency risk.

First, denominated exchange rate has more significant impact on the Chinese export trade to other countries than effective exchange rate. Second, the RMB exchange rate fluctuations also significantly affect the export trade. Third, China’s import and export trade have significant spatial effect.

This paper recommends the construction of the RMB currency futures market as soon as possible, providing a richer foreign exchange derivatives and other risk hedging instruments, thus to enhance the ability to respond to exchange rate risks.

This paper uses spatial panel model with the refined data to study various factors on the import and export trade, and thus more comprehensive analysis on the impact of the exchange rate on the import and export trade with other major countries.

This research aims to empirically analyze the spatial bank branch network in Canada. The authors study the market structure (both industrial and geographic concentrations) via its own or adjacent postal areas. The empirical framework of this study considers branch density (the ratio of the total number of branches to area size) by employing a spatial two-way fixed effects model. The main finding of this study is that there are no effects associated with market structure, however, there are strong spatial within and nearby effects associated with the socioeconomic variables. In addition, the authors also study the effect of spatial competition from rival banks: they find that large banks and small banks tend to avoid markets dominated by their competitors.

A widely held belief before the 1990s – referred to as the oil-blessing hypothesis – was that oil discovery and production should promote economic growth and development and lead to poverty reduction. However, the so-called ‘oil-curse’ hypothesis, postulated by Sachs and Warner in 1995, challenged this belief, thus provoking a heated debate on the theme. The oil-curse hypothesis has been traditionally tested by means of cross-sectional and panel-data models. The author goes beyond these traditional methods to test whether the presence of spatial effects can alter the hypothesis in oil-producing African countries. In particular, this paper aims to investigate the effects on economic growth of oil production, oil resources and oil revenues along with the quality of democratic institutions, investment and openness to trade.

A Durbin spatial model, a cross-sectional model and panel-data model are used.

First, the validity of the spatial Durbin model is vindicated. Second, consistently with the oil-curse hypothesis, oil production, resources, rent and revenues have a negative and generally significant effect on economic growth. This result is robust for across the panel data, spatial Durbin and spatial autoregressive models and for different measures of spatial proximity between countries. Third, the author finds that the extent to which the business environment is perceived as benign for investment has a positive and marginally effect on economic growth. Additionally, economic growth of a country is further stimulated by a spatial proximity of a neighbouring country if the neighbouring country has created strong institutions protecting investments. Fourth, openness to international trade has a positive and marginally significant effect on economic growth.

This paper examines theories and studies that have been done before. However, as the related literature on the growth–resource abundance nexus has rarely examined spatial effects, this study seeks to test jointly the spatial effect and the neighbouring effect on the oil curse hypothesis.

The purpose of this paper is to examine the real estate sales in Turkey on a district basis to reveal the current state of real estate sales and any meaningful changes in the last period. The real estate market is important and is an indicator of the country’s general economic health, as real estate is seen as an investment.

As a powerful method of spatial analysis and evaluation, geographic information systems have been used to examine real estate data in both spatial and temporal ways. In this study, 14 years of sales data covering the years 2004 to 2017 obtained from government agencies on a district basis were evaluated using spatiotemporal methods. Several maps were produced using Getis-Ord Gi* and local Moran’s I indices, which showed the spatiotemporal change of sales and sales rates.

When looking at the maps, provinces such as Istanbul, Ankara, Izmir, Antalya and their surrounding districts have buoyant real estate markets compared to the other side of the country. Real estate sales are more stagnant in the eastern and northern parts of the country. In addition, the authors found that the growth rate of annual average real estate sales was approximately seven times higher than the annual average population growth.

This spatiotemporal study, which presents 14 years of performance data of the real estate market and, by extension, the economic situation, also highlights the regions that stand out for investment planning throughout the country. The results of spatiotemporal analysis also present a new way of real estate market visualization using maps with well-designed categorizations.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

In this chapter, the authors contribute toward building a better understanding of farmers’ responses to behavioral drivers of land-use decision by establishing an alternative analytical procedure, which can overcome various drawbacks suffered by methods currently used in existing studies. Firstly, our procedure makes use of spatially high-resolution data, so that idiosyncratic effects of physical environment drivers, e.g., soil textures, can be explicitly modeled. Secondly, we address the well-known censored data problem, which often hinders a successful analysis of land-use shares. Thirdly, we incorporate spatial error dependence (SED) and heterogeneity in order to obtain efficiency gain and a more accurate formulation of variances for the parameter estimates. Finally, the authors reduce the computational burden and improve estimation accuracy by introducing an alternative generalized method of moments (GMM)–quasi maximum likelihood (QML) hybrid estimation procedure. The authors apply the newly proposed procedure to spatially high-resolution data in England and found that, by taking these features into consideration, the authors are able to formulate conclusions about causal effects of climatic and physical environment, and environmental policy on land-use shares that differ significantly from those made based on methods that are currently used in the literature. Moreover, the authors show that our method enables derivation of a more effective predictor of the land-use shares, which is utterly useful from the policy-making point of view.

The paper investigates the prevalence of extreme poverty in a panel of 39 sub-Saharan African (SSA) countries over the period 2000–2018 while accounting for spillover effects.

The study adopts the recently developed spatial dependence-consistent, bias-corrected quasi-maximum likelihood (QML) estimators and the linear dynamic panel regression to control for the potential endogeneity in poverty and corruption spillovers.

The spatial model shows. consistently across all the specifications, that there is a substantial spillover effect of corruption and poverty across the region. Additionally, the study also found that investment in health and education is a significant determinant of poverty in the region. However, the effectiveness of these policy variables to reduce poverty declines in the face of corruption spillovers. More importantly, the empirical analysis shows that poverty does not only exhibit spatial spillovers but also has a persistent effect over time. The results, therefore, suggest that to reduce poverty in the region, sub-Saharan African governments must adopt spatially differentiated policies and programmes by working together to reduce unemployment and corruption in the region, and not the widely adopted spatially mute designs currently in place. The research and policy implications are discussed.

The study accounts for spatial dependency and spillover effects in the analysis of poverty and corruption in SSA

The purpose of this paper is to examine the determinants of immigration from European Neighborhood (EN) and new member states to the EU core countries over the period 2000-2010. Apart from income differentials, unemployment rates and other standard variables hypothesized to determine immigration, the paper focusses attention on welfare-chasing as well as measures to enforce immigration policy. Using a variant of the gravity model, the paper investigates whether tests of these hypotheses are robust with respect to spatial misspecification.

The determinants of migration from the European Neighborhood and new member states to the EU core countries is estimated using a spatial variant of the gravity model. The methodology is used for both multilateral and spatial flows. Gravity model estimations are presented for immigration into the EU core destinations using standard, non-spatial econometrics, as well as spatial econometrics for single and double-spatial dynamics.

Immigration to EU core countries varies directly with the change in social spending per head in the destination. This result stands out in all the models, both OLS and spatial. Immigrants are attracted by economic inequality as measured by the Gini coefficient. However, in this case it is the level that matters rather than its change. No evidence is found that the threat of apprehension at the destination deters migrants from the European Neighborhood and other countries.

The authors assume multilateralism is spatial. This means that everything else given, destinations are closer substitutes the nearer they are, and that immigration shocks are likely to be more correlated among origins the closer they are. This implicit assumption is restrictive because multilateralism is just spatial.

While immigration to EU core countries varies directly with the change in (not level of) social spending per head. If a given country becomes more benevolent it attracts more immigration. The results suggest that if during 2000-2010 social spending per capita grew by 1 percent, the immigration rate increased by between 1 and 2 percentage points relative to the number of foreign-born in 2000. This is a large demographic effect.

Uniquely, this paper does not assume immigration flows are independent and stresses their spatial and multilateral nature. A series of new non-spatial and spatial (single and double-spatial lag) models are used to empirically test hypotheses about the determinants of immigration to the EU core countries.