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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.

We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices, event counts and limited-dependent variables (truncation, censoring, and sample selection) among others. Our algorithm relies upon a novel implementation of efficient importance sampling (EIS) specifically designed to exploit typical sparsity of high-dimensional spatial precision (or covariance) matrices. It is numerically very accurate and computationally feasible even for very high-dimensional latent processes. Thus, maximum likelihood (ML) estimation of high-dimensional non-Gaussian spatial models, hitherto considered to be computationally prohibitive, becomes feasible. We illustrate our approach with ML estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.

This study is the first that aims to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

Multiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

With network structure analysis, this study finds that they contain different information content from the perspective of graphical display, node strength and correlation. Developed and emerging countries all play major roles in trade connection, while only developed countries play major roles in financial linkage. Second, by applying the multidimensional SAR model, only the spatial autocorrelation coefficients for trade and financial linkages are significant during the full sample period, which is in sharp contrast to published studies using the SAR model with a single matrix. Third, the spillover channels that play major roles in various periods are different. Only trade channel plays a role during crisis periods and it is the most important. Fourth, the spatial correlation among countries greatly amplifies the shock’s impacts on one market. And spatial effect for developed countries is larger than those for emerging countries, while the mean spatial effect of a unit shock in the USA on emerging countries is slightly greater than that on developed countries.

Multiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

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.

This study aims to examine the impact of relative importance of local characteristics, distance from the city centre and unobservable spatial relation in explaining values of constant‐quality apartment units in Vienna.

Drawing on recent developments in spatial econometrics and spatial hedonic house price modelling, the rent gradient hypothesis is examined by means of hedonic regression and spatial hedonic regression. Spatial autocorrelation tests are applied in order to assess possible presence of spatial dependence. The authors borrow Florax et al.'s specification search strategy in order to choose the most appropriate spatial model specification.

This research shows that local characteristics – or particularities – proxied by district and distance from the city centre are important location variables with regard to the Viennese apartment market. The spatial analysis suggests that the apartment prices are spatially autocorrelated and the Viennese apartment market has a distance‐based neighbourhood structure. The main finding is, however, that residents are willing to bid more for constant‐quality apartment units that are close to the centre of the city.

Rent gradient hypothesis is usually tested within non‐spatial hedonic frameworks: this study estimates a spatial hedonic model additionally in order to allow for comparison of results. This is also the first article to apply recent developments in spatial econometrics to examine explicitly rent gradient theory in the context of the Viennese apartment market.

This article aims to discuss the binary matrix of spatial association which is suggested by Moran, and proposes a new method of the definition of the w matrix to obtain a new space‐time correlation coefficient considering the correlation of both time and space.

From the perspective of the multi‐dimension of space and time, this article proposes a new computational method of a correlation coefficient considering both temporal and spatial factors, based on the analysis of the characteristics of Moran's Global Index and Moran's Local Index. The number of patents granted in mainland China's provinces and municipalities is taken as an example of multi‐dimensional analysis.

The results of quantitative analysis using this space‐time correlation coefficient show that the outcomes calculated by this new correlation coefficient are not only highly correlated with Moran's Index, but also have advantages in analyzing the trends of both spatial and temporal indicators simultaneously, which is verified by the illustration of the algorithm.

Due to a scarcity of data in China, the algorithm is based on data for the last 20 years, which may not be long enough for this research. Although this does not reduce the value of the conclusions of this article, a closer look should be taken at the effectiveness of the new space‐time correlation coefficient in the future.

The results of space‐time correlation coefficient are highly correlated with Moran's Index. In addition, it can not only analyze the “flow” indicators in a certain period but also analyze the “stock” indicators to reflect both space and time changes. These may reflect superiority of space‐time correlation coefficient to Moran's Index.

This new correlation coefficient that considers both temporal and spatial factors and will provide a more scientific and effective tool for spatial econometric analysis in time and space changes of management on society and the economy.

This paper explores whether the Euro-area sovereign credit default swap market is prone to contagion effects. It investigates whether the sharp increase in sovereign CDS spread of a given country is due to a deterioration of the macroeconomic variables or some form of contagion.

For this purpose, the authors use an innovative approach, i.e. spatial econometrics. Although modeling spatial dependence is an attractive challenge, its application in the field of finance remains limited.

The empirical findings show strong evidence of spatial dependence highlighting the presence of pure contagion. Furthermore, evidence of wake-up call contagion-increased sensitivity of investors to fundamentals of neighboring countries and shift contagion-increased sensitivity to common factors are well recorded.

This study aims to study a crucial financial issue that gained increased research interest, i.e. financial contagion. A methodological contribution is made by extending the standard spatial Durbin model (SDM) to analyze and differentiate between several forms of contagion. The results can be used to understand how shocks are spreading through countries.

This chapter provides an overview of the status of applied econometric research in hospitality and tourism management and outlines the econometric toolsets available for quantitative researchers using empirical data from the field. Basic econometric models, cross-sectional models, time-series models, and panel data models are reviewed first, followed by an evaluation of relevant applications. Next, econometric modeling topics that are germane to hospitality and tourism research are discussed, including endogeneity, multi-equation modeling, causal inference modeling, and spatial econometrics. Furthermore, major feasibility issues for applied researchers are examined based on the literature. Lastly, recommendations are offered to promote applied econometric research in hospitality and tourism management.

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.