Discussion: Applications and Innovations in Spatial Econometrics

These articles provide a discussion of studies presented in a session on spatial econometrics, focusing on the ability of spatial regression models to quantify the magnitude of spatial spillover impacts. Both articles presented argue that a proper modeling of spatial spillovers is required to truly understand the phenomena under study, in one case the impact of climate change on land values (or crop yields) and in the second the role of regional industry composition on regional business establishment growth.lagged variables, panel data, spatial spillovers, Community/Rural/Urban Development, Environmental Economics and Policy, Resource /Energy Economics and Policy, C33, C51,

Network dependence in multi-indexed data on international trade flows

Faced with the problem that conventional multidimensional fixed effects models only focus on unobserved heterogeneity, but ignore any potential cross-sectional dependence due to network interactions, we introduce a model of trade flows between countries over time that allows for network dependence in flows, based on sociocultural connectivity structures. We show that conventional multidimensional fixed effects model specifications exhibit cross-sectional dependence between countries that should be modeled to avoid simultaneity bias. Given that the source of network interaction is unknown, we propose a panel gravity model that examines multiplenetwork interaction structures, using Bayesian model probabilities to determine those most consistent with the sample data. This is accomplished with the use of computationally efficient Markov Chain Monte Carlo estimation methods that produce a Monte Carlo integration estimate of the log-marginal likelihood that can be used for model comparison. Application of the model to a panel of trade flows points to network spillover effects, suggesting the presence of network dependence and biased estimates from conventional trade flow specifications. The most important sources of network dependence were found to be membership in trade organizations, historical colonial ties, common currency, and spatial proximity of countries.Series: Working Papers in Regional Scienc

Incorporating Transportation Network Structure in Spatial Econometric Models of Commodity Flows

We introduce a regression-based gravity model for commodity flows between 35 regions in Austria. We incorporate information regarding the highway network into the spatial connectivity structure of the spatial autoregressive econometric model. We find that our approach produces improved model fit and higher likelihood values. The model accounts for spatial dependence in the origin-destination flows by introducing a spatial connectivity matrix that allows for three types of spatial dependence in the origins to destinations flows. We modify this origin-destination connectivity structure that was introduced by LeSage and Pace (2005) to include information regarding the presence or absence of a major highway/train corridor that passes through the regions. Empirical estimates indicate that the strongest spatial autoregressive effects arise when both origin and destination regions have neighboring regions located on the highway network. Our approach provides a formal spatial econometric methodology that can easily incorporate network connectivity information in spatial autoregressive models.Commodity flows, Spatial autoregression, Bayesian, Maximum likelihood, Spatial connectivity of origin-destination flows

Conventional versus network dependence panel data gravity model specifications

Past focus in the panel gravity literature has been on multidimensional fixed effects specifications in an effort to accommodate heterogeneity. After introducing conventional multidimensional fixed effects, we find evidence of cross-sectional dependence in flows. We propose a simultaneous dependence gravity model that allows for network dependence in flows, along with computationally efficient Markov Chain Monte Carlo estimation methods that produce a Monte Carlo integration estimate of log-marginal likelihood useful for model comparison. Application of the model to a panel of trade flows points to network spillover effects, suggesting the presence of network dependence and biased estimates from conventional trade flow specifications. The most important sources of network dependence were found to be membership in trade organizations, historical colonial ties, common currency and spatial proximity of countries.Series: Working Papers in Regional Scienc

Estimates of the impact of static and dynamic knowledge spillovers on regional factor productivity

We develop an empirical approach to examine static and dynamic knowledge externalities in the context of a regional total factor productivity relationship. Static externalities refer to current period scale or industry-size effects which have been labeled localization externalities or region-size effects known as agglomeration externalities. Dynamic externalities refer to the relationship between accumulated or prior period knowledge and current levels of innovation, where past learning-by-doing makes innovation positively related to cumulative production over time. Our empirical specification allows for the presence of both static and dynamic externalities, and provides a way to assess the relative magnitude of spillovers associated with spillovers from these two types of knowledge externalities. The magnitude of own-region impacts and other-region (spillovers) can be assessed using scalar summary measures of the own- and cross-partial derivatives from the model. We find evidence supporting the presence of dynamic externalities as well as static, and our estimates suggest that dynamic externalities may have a larger magnitude of impact than static externalities.

Spatial Econometric Issues for Bio-Economic and Land-Use Modeling

We survey the literature on spatial bio-economic and land-use modelling and review thematic developments. Unobserved site-specific heterogeneity is common in almost all of the surveyed works. Heterogeneity appears also to be a significant catalyst engendering significant methodological innovation. To better equip prototypes to adequately incorporate heterogeneity, we consider a smorgasbord of extensions. We highlight some problems arising with their application; provide Bayesian solutions to some; and conjecture solutions for others.spatial econometrics, bio-economic and land-use modelling, Bayesian solution, Land Economics/Use,

Cross-sectional dependence model specifications in a static trade panel data setting

The focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time invariant factors such as socio-cultural indicator variables, e.g., common language and currency. These are typically treated as a source of heterogeneity eliminated using fixed effects transformations, but we find evidence of cross-sectional dependence after eliminating country-specific and time-specific effects. These findings suggest use of alternative simultaneous dependence model specifications that accommodate cross-sectional dependence, which we set forth along with Bayesian estimation methods. Ignoring cross-sectional dependence implies biased estimates from panel trade flow models that rely on fixed effects.Series: Working Papers in Regional Scienc

The impact of knowledge capital on regional total factor productivity

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. The dependent variable is total factor productivity, defined as output (in terms of gross value added) per unit of labour and physical capital combined, while the explanatory variable is a patent stock measure of regional knowledge endowments. We provide an econometric derivation of the relationship, which in the presence of unobservable knowledge capital leads to a spatial regression model relationship. This model form is extended to account for technological dependence between regions, which allows us to quantify disembodied knowledge spillover impacts arising from both spatial and technological proximity. A six-year panel of 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity

Spatial econometric methods for modeling origin destination flows

Spatial interaction models of the gravity type are used in conjunction with sample data on flows between origin and destination locations to analyse international and interregional trade, commodity, migration and commuting patterns. The focus is on the classical log-normal model version and spatial econometric extensions that have recently appeared in the literature. These new models replace the conventional assumption of independence between origin-destination flows with formal approaches that allow for spatial dependence in flow magnitudes. The paper also discusses problems that arise in applied practice when estimating (log-normal) spatial interaction models. (authors' abstract

MCMC estimation of panel gravity models in the presence of network dependence

Past focus in the panel gravity literature has been on multidimensional fixed effects specifications in an effort to accommodate heterogeneity. After introducing fixed effects for each origin- destination dyad and time-period speciffic effects, we find evidence of cross-sectional dependence in flows. We propose a simultaneous dependence gravity model that allows for network dependence in flows, along with computationally efficient MCMC estimation methods that produce a Monte Carlo integration estimate of log-marginal likelihood useful for model comparison. Application of the model to a panel of trade flows points to network spillover effects, suggesting the presence of network dependence and biased estimates from conventional trade flow specifications.Series: Working Papers in Regional Scienc